@string{aaai = "Proceedings of the AAAI Conference on Artificial Intelligence"}
@string{icml = "International Conference on Machine Learning ({ICML})"}
@string{icra = "IEEE International Conference on Robotics and Automation ({ICRA})"}
@string{neurips = "Advances in Neural Information Processing Systems ({NeurIPS})"}
@string{iclr = "International Conference on Learning Representations ({ICLR})"}
@string{crv = "Proceedings of the Conference on Computer and Robot Vision ({CRV})"}
@string{iser = "International Symposium on Experimental Robotics ({ISER})"}
@string{iros = "IEEE/RSJ International Conference on Intelligent Robots and Systems ({IROS})"}

@article{daneshmand2026safeexplorer,
  title={SafeExplorer: An Unbiased Mixed-Policy Gradient for Reinforcement Learning with Recovery Interventions},
  author={Daneshmand, Elham and Khadiv, Majid and Berseth, Glen and Lin, Hsiu-Chin},
  journal={Transactions on Machine Learning Research (TMLR)},
  year={2026},
  pdf={https://openreview.net/forum?id=ppOrbSsMM0},
  website={https://lhumd.github.io/safeexplorer/},
  media={https://www.youtube.com/embed/Fcvv6jQkTjY},
  abstract={Training agents directly on the task they will be deployed on often leads to the best policy performance, but training on physical robots can cause damage through falls, so minimizing training falls is critical. A standard mitigation hands control to a separate recovery policy whenever the agent leaves a designer-specified safe region. The resulting mixed-policy rollouts bias the on-policy update, and importance sampling cannot remove this bias when the recovery policy is deterministic. We propose SafeExplorer, a drop-in modification of proximal policy optimization (PPO) built on an unbiased policy-gradient estimator of the mixed-policy return. The estimator uses the score function only inside the safe region and never evaluates the recovery policy's density, so deterministic recovery policies pose no problem. On final reward, the estimator matches or outperforms truncated importance sampling, which is itself biased for that same gradient. SafeExplorer also speeds up learning near the safe-region boundary with a closed-form critic target at recovery-triggering states when dynamics and recovery are deterministic, and an imitation loss that copies recovery actions only from successful recoveries. On three simulated locomotion environments with ten seeds each, and given an already available recovery controller, SafeExplorer incurs 255×, 43×, and 21× fewer training falls than standard PPO on HalfCheetah, Ant, and Unitree Go1. SafeExplorer also matches or exceeds the final reward of PPO. On Ant, where 8.7% of recovery interventions end in a fall, over 40× the HalfCheetah rate, SafeExplorer reaches the success threshold on every seed and none of the five baselines reaches the threshold on any seed.}
}

@article{daneshmand2026slowrl,
  title={SLowRL: Safe Low-Rank Adaptation Reinforcement Learning for Locomotion},
  author={Daneshmand, Elham and Omar, Shafeef and Berseth, Glen and Khadiv, Majid and Lin, Hsiu-Chin},
  journal={Workshop at the Reinforcement Learning Conference (RLC)},
  year={2026},
  pdf={https://arxiv.org/abs/2603.17092},
  media={https://lhumd.github.io/assets/video/slowrl_go2_jump.mp4},
  abstract={Sim-to-real transfer of locomotion policies often leads to performance degradation due to the inevitable sim-to-real gap. Naively fine-tuning these policies directly on hardware is problematic, as it poses risks of mechanical failure and suffers from high sample inefficiency. In this paper, we address the challenge of safely and efficiently fine-tuning reinforcement learning (RL) policies for dynamic locomotion tasks. Specifically, we focus on fine-tuning policies learned in simulation directly on hardware, while explicitly enforcing safety constraints. In doing so, we introduce SLowRL, a framework that combines Low-Rank Adaptation (LoRA) with training-time safety enforcement via a recovery policy. We evaluate our method both in simulation and on a real Unitree Go2 quadruped robot for jump and trot tasks. Experimental results show that our method achieves a 46.5% reduction in fine-tuning time and near-zero safety violations compared to standard proximal policy optimization (PPO) baselines. Notably, we find that a rank-1 adaptation alone is sufficient to recover pre-trained performance in the real world, while maintaining stable and safe real-world fine-tuning. These results demonstrate the practicality of safe, efficient fine-tuning for dynamic real-world robotic applications.}
}

@misc{bonilla2026flintfastlightweightinference,
      title={FLINT: Fast Lightweight Inference for Traversability}, 
author={William Bonilla and Maxime Boisvert and David-Alexandre Poissant and David Meger and Louis Petit},
year={2026},
eprint={2609.26857},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2609.26857}, 
pdf={https://arxiv.org/abs/2609.26857}, 
}

@misc{meriaux2026amp2026multiplatformmarinerobotics,
      title={AMP2026: A Multi-Platform Marine Robotics Dataset for Tracking and Mapping}, 
author={Edwin Meriaux and Shuo Wen and David Widhalm and Zhizun Wang and Junming Shi and Mariana Sosa Guzmán and Kalvik Jakkala and Bennett Carley and Elias Sokolova and Yogesh Girdhar and Monika Roznere and Jason O'Kane and Junaed Sattar and Gregory Dudek},
year={2026},
eprint={2603.04225},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2603.04225}, 
pdf={https://arxiv.org/abs/2603.04225}, 
}

@misc{wen2026stablemultidronegnsstracking,
      title={Stable Multi-Drone GNSS Tracking System for Marine Robots}, 
author={Shuo Wen and Edwin Meriaux and Mariana Sosa Guzmán and Zhizun Wang and Junming Shi and Gregory Dudek},
year={2026},
eprint={2511.18694},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2511.18694}, 
pdf={https://arxiv.org/abs/2511.18694}, 
}

@misc{meriaux2025mobileadhocnetworkscoverage,
      title={On Mobile Ad Hoc Networks for Coverage of Partially Observable Worlds}, 
author={Edwin Meriaux and Shuo Wen and Louis-Roy Langevin and Doina Precup and Antonio Loría and Gregory Dudek},
year={2025},
eprint={2512.09495},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2512.09495}, 
pdf={https://arxiv.org/abs/2512.09495}, 
}

@misc{ismayilov2025decentralizedmultiagentgoalassignment,
      title={Decentralized Multi-Agent Goal Assignment for Path Planning using Large Language Models}, 
author={Murad Ismayilov and Edwin Meriaux and Shuo Wen and Gregory Dudek},
year={2025},
eprint={2510.23824},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2510.23824}, 
pdf={https://arxiv.org/abs/2510.23824}, 
}

@misc{wen2025scalableaerialgnsslocalization,
      title={Scalable Aerial GNSS Localization for Marine Robots}, 
author={Shuo Wen and Edwin Meriaux and Mariana Sosa Guzmán and Charlotte Morissette and Chloe Si and Bobak Baghi and Gregory Dudek},
year={2025},
eprint={2505.04095},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2505.04095}, 
pdf={https://arxiv.org/abs/2505.04095}, 
}

@misc{morissette2026tactilemodalityfusionvisionlanguageaction,
      title={Tactile Modality Fusion for Vision-Language-Action Models}, 
      author={Charlotte Morissette and Amin Abyaneh and Wei-Di Chang and Anas Houssaini and David Meger and Hsiu-Chin Lin and Jonathan Tremblay and Gregory Dudek},
      year={2026},
      eprint={2603.14604},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2603.14604}, 
      pdf={https://arxiv.org/abs/2603.14604}, 
}

@article{xiru2026delays,
author = {Xiru Zhu and Gregory Dudek and David Meger},
title = {Improving Policy Learning from Delayed Interventions},
journal = {In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2026}
}

@inproceedings{chung2026perceived,
title={Perceived Regret: Evaluating Agents in Any World},
author={Wesley Chung and Zihan Wang and Xiao-Wen Chang and David Meger and Doina Precup},
booktitle={Continual Reinforcement Learning Workshop at RLC 2026},
year={2026},
url={https://openreview.net/forum?id=exMKyPA2wP},
pdf={https://openreview.net/forum?id=exMKyPA2wP}
}

@article{danesh2026copyquad,
  title = {CopyQuad: Quadrupedal Skill Transfer from a Single Side-View Video Without a Depth Model},
  author = {Mohamad H. Danesh and Raina Hu and Mengxue Xu and Hsiu-Chin Lin},
  journal = {ArXiv:2610.16737},
  year = {2026},
  pdf = {https://arxiv.org/abs/2610.16737},
  website = {https://modanesh.github.io/papers/copyquad/}
}

@article{driftQL2026,

  author = {Anas Houssaini and Mohamad H. Danesh and Amin Abyaneh and Scott
    Fujimoto and Hsiu-Chin Lin and David Meger},
  title = {Drift Q-Learning},
  journal = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  pdf = {https://arxiv.org/abs/2606.00350},
  website = {https://driftql.github.io/}
}

@article{valiCA2,
	title = {CA2: Code-Aware Agent for Automated Game Testing},
	author = {Valliappan Chidambaram Adaikkappan and Vincent Martineau and Joshua Romoff and David Meger},
	journal = {Canadian AI Conference},
	year = {2026},
	pdf = {https://arxiv.org/abs/2605.13918}
}

@article{danesh2026toward,
  title={Toward Hardware-Agnostic Quadrupedal World Models via Morphology Conditioning},
  author={Danesh, Mohamad H. and Li, Chenhao and Abyaneh, Amin and Houssaini, Anas and Ellis, Kirsty and Berseth, Glen and Hutter, Marco and Lin, Hsiu-Chin},
  journal={Conference on Robot Learning (CoRL)},
  pdf = {https://arxiv.org/abs/2604.08780},
  website = {https://modanesh.github.io/papers/qwm/},
  year={2026}
}


@article{wangMBMARLSALE,
	title = {Multi-Agent Model-Based Reinforcement Learning with Joint State-Action Learned Embeddings},
	author = {Zhizun Wang and David Meger},
	journal = {The 25th International Conference on Autonomous Agents and Multiagent Systems},
	year = {2026},
	pdf = {https://dl.acm.org/doi/10.65109/IXMJ8234}
}


@article{wu2025vocaloco,
  title={VOCALoco: Viability-Optimized Cost-aware Adaptive Locomotion},
  author={Wu, Stanley and Danesh, Mohamad H. and Li, Simon and Yurchyk, Hanna and Abyaneh, Amin and Houssaini, Anas El and Meger, David and Lin, Hsiu-Chin},
  journal={IEEE Robotics and Automation Letters (RAL), 2025},
  year={2025}
}

@article{holliday2025learning,
  title={Learning heuristics for transit network design and improvement with deep reinforcement learning},
  author={Holliday, Andrew and El-Geneidy, Ahmed and Dudek, Gregory},
  journal={Transportmetrica B: Transport Dynamics},
  volume={13},
  number={1},
  pages={2561863},
  year={2025},
  publisher={Taylor \& Francis},
  pdf={https://www.tandfonline.com/doi/pdf/10.1080/21680566.2025.2561863}
}

@article{zhao2025blockchain,
  title={A Blockchain Framework for Equitable and Secure Task Allocation in Robot Swarms},
  author={Zhao, Hanqing and Pacheco, Alexandre and Beltrame, Giovanni and Liu, Xue and Dorigo, Marco and Dudek, Gregory},
  journal={IEEE Robotics and Automation Letters},
  year={2025},
  publisher={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/7083369/7339444/11150749.pdf}
}

@article{ablett2024multimodal,
  title={Multimodal and force-matched imitation learning with a see-through visuotactile sensor},
  author={Ablett, Trevor and Limoyo, Oliver and Sigal, Adam and Jilani, Affan and Kelly, Jonathan and Siddiqi, Kaleem and Hogan, Francois and Dudek, Gregory},
  journal={IEEE Transactions on Robotics},
  year={2024},
  publisher={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/8860/4359257/10814647.pdf}
}

@article{jilani2024visual,
  title={Visual-Tactile Inference of 2.5 D Object Shape From Marker Texture},
  author={Jilani, Affan and Hogan, Fran{\c{c}}ois and Morissette, Charlotte and Dudek, Gregory and Jenkin, Michael and Siddiqi, Kaleem},
  journal={IEEE Robotics and Automation Letters},
  year={2024},
  publisher={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/7083369/7339444/10803047.pdf}
}

@article{rivkin2024aiot,
  title={Aiot smart home via autonomous llm agents},
  author={Rivkin, Dmitriy and Hogan, Francois and Feriani, Amal and Konar, Abhisek and Sigal, Adam and Liu, Xue and Dudek, Gregory},
  journal={IEEE Internet of Things Journal},
  year={2024},
  publisher={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/6488907/6702522/10729865.pdf}
}

@inproceedings{limoyo2024photobot,
  title={PhotoBot: Reference-Guided Interactive Photography via Natural Language},
  author={Limoyo, Oliver and Li, Jimmy and Rivkin, Dmitriy and Kelly, Jonathan and Dudek, Gregory},
  booktitle={2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={2479--2486},
  year={2024},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/10801246/10801290/10801790.pdf}
}

@inproceedings{limoyo2024working,
  title={Working Backwards: Learning to Place by Picking},
  author={Limoyo, Oliver and Konar, Abhisek and Ablett, Trevor and Kelly, Jonathan and Hogan, Francois R and Dudek, Gregory},
  booktitle={2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={11155--11162},
  year={2024},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/10801246/10801290/10802173.pdf}
}

@inproceedings{konar2024accelerating,
  title={Accelerating Digital Twin Calibration with Warm-Start Bayesian Optimization},
  author={Konar, Abhisek and Feriani, Amal and Wu, Di and Jang, Seowoo and Liu, Xue and Dudek, Gregory},
  booktitle={ICC 2024-IEEE International Conference on Communications},
  pages={2372--2377},
  year={2024},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/10622104/10622158/10622967.pdf}
}

@inproceedings{xu2024optimizing,
  title={Optimizing Energy Saving for Wireless Networks via Offline Decision Transformer},
  author={Xu, Yi Tian and Wu, Di and Jenkin, Michael and Jang, Seowoo and Liu, Xue and Dudek, Gregory},
  booktitle={ICC 2024-IEEE International Conference on Communications},
  pages={409--414},
  year={2024},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/10622104/10622158/10622786.pdf}
}

@inproceedings{al2024probabilistic,
  title={Probabilistic mobility load balancing for multi-band 5G and beyond networks},
  author={Al Lahham, Saria and Wu, Di and Hossain, Ekram and Liu, Xue and Dudek, Gregory},
  booktitle={2024 IEEE International Conference on Communications Workshops (ICC Workshops)},
  pages={1673--1678},
  year={2024},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/10615255/10615259/10615828.pdf}
}

@inproceedings{li2024anomaly,
  title={Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization},
  author={Li, Jimmy and Kozlov, Igor and Wu, Di and Liu, Xue and Dudek, Gregory},
  booktitle={2024 IEEE International Conference on Communications Workshops (ICC Workshops)},
  pages={1395--1400},
  year={2024},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/10615255/10615259/10615617.pdf}
}

@inproceedings{lajoie2024peoplex,
  title={PEOPLEx: Pedestrian opportunistic positioning leveraging IMU, UWB, BLE and WiFi},
  author={Lajoie, Pierre--Yves and Baghi, Bobak Hamed and Herath, Sachini and Hogan, Francois and Liu, Xue and Dudek, Gregory},
  booktitle={ICC 2024-IEEE International Conference on Communications},
  pages={3518--3523},
  year={2024},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/10622104/10622158/10622566.pdf}
}

@inproceedings{luo2024adaptive,
  title={Adaptive dynamic programming for energy-efficient base station cell switching},
  author={Luo, Junliang and Xu, Yi Tian and Wu, Di and Jenkin, Michael and Liu, Xue and Dudek, Gregory},
  booktitle={2024 IEEE International Conference on Communications Workshops (ICC Workshops)},
  pages={1365--1370},
  year={2024},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/10615255/10615259/10615533.pdf}
}

@inproceedings{holliday2024neural,
  title={A neural-evolutionary algorithm for autonomous transit network design},
  author={Holliday, Andrew and Dudek, Gregory},
  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={4457--4464},
  year={2024},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/10609961/10609862/10611313.pdf}
}

@inproceedings{lotfi2024uncertainty,
  title={Uncertainty-aware hybrid paradigm of nonlinear MPC and model-based RL for offroad navigation: Exploration of transformers in the predictive model},
  author={Lotfi, Faraz and Virji, Khalil and Faraji, Farnoosh and Berry, Lucas and Holliday, Andrew and Meger, David and Dudek, Gregory},
  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={2925--2931},
  year={2024},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/10609961/10609862/10610452.pdf}
}

@inproceedings{rivkin2024cartier,
  title={Cartier: Cartographic language reasoning targeted at instruction execution for robots},
  author={Rivkin, Dmitriy and Kakodkar, Nikhil and Hogan, Francois and Baghi, Bobak H and Dudek, Gregory},
  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={5615--5621},
  year={2024},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/10609961/10609862/10610072.pdf}
}

@inproceedings{jenkin2024interacting,
  title={Interacting with a Visuotactile Countertop},
  author={Jenkin, Michael and Hogan, Francois R and Siddiqi, Kaleem and Tremblay, Jean-Francois and Baghi, Bobak and Dudek, Gregory},
  booktitle={International Conference on Robotics, Computer Vision and Intelligent Systems},
  pages={361--374},
  year={2024},
  organization={Springer}
}

@inproceedings{li2022traffic,
  title={Traffic scenario clustering and load balancing with distilled reinforcement learning policies},
  author={Li, Jimmy and Wu, Di and Xu, Yi Tian and Li, Tianyu and Jang, Seowoo and Liu, Xue and Dudek, Gregory},
  booktitle={ICC 2022-IEEE International Conference on Communications},
  pages={1536--1541},
  year={2022},
  organization={IEEE}
}

@inproceedings{wu2022active,
  title={Active deep multi-task learning for forecasting short-term loads},
  author={Wu, Di and Jenkin, Michael and Liu, Xue and Dudek, Gregory},
  booktitle={ICC 2022-IEEE International Conference on Communications},
  pages={5523--5529},
  year={2022},
  organization={IEEE}
}

@inproceedings{li2022communication,
  title={Communication traffic prediction with continual knowledge distillation},
  author={Li, Hang and Wang, Ju and Hu, Chengming and Chen, Xi and Liu, Xue and Jang, Seowoo and Dudek, Gregory},
  booktitle={ICC 2022-IEEE International Conference on Communications},
  pages={5481--5486},
  year={2022},
  organization={IEEE}
}

@inproceedings{wu2022short,
  title={Short-term load forecasting with deep boosting transfer regression},
  author={Wu, Di and Xu, Yi Tian and Jenkin, Michael and Wang, Ju and Li, Hang and Liu, Xue and Dudek, Gregory},
  booktitle={ICC 2022-IEEE International Conference on Communications},
  pages={5530--5536},
  year={2022},
  organization={IEEE}
}

@inproceedings{hogan2022finger,
  title={Finger-STS: Combined proximity and tactile sensing for robotic manipulation},
  author={Hogan, Francois R and Tremblay, Jean-Francois and Baghi, Bobak H and Jenkin, Michael and Siddiqi, Kaleem and Dudek, Gregory},
  booktitle={IEEE Robotics and Automation Letters},
  pages={10865--10872},
  year={2022},
  publisher={IEEE}
}

@inproceedings{feriani2022multiobjective,
  title={Multiobjective load balancing for multiband downlink cellular networks: A meta-reinforcement learning approach},
  author={Feriani, Amal and Wu, Di and Xu, Yi Tian and Li, Jimmy and Jang, Seowoo and Hossain, Ekram and Liu, Xue and Dudek, Gregory},
  booktitle={IEEE Journal on Selected Areas in Communications},
  pages={2614--2629},
  year={2022},
  publisher={IEEE}
}

@inproceedings{manjanna2022scalable,
  title={Scalable multirobot planning for informed spatial sampling},
  author={Manjanna, Sandeep and Hsieh, M Ani and Dudek, Greogory},
  booktitle={Autonomous Robots},
  pages={817--829},
  year={2022},
  publisher={Springer}
}

@inproceedings{baghi2022sesno,
  title={Sesno: Sample efficient social navigation from observation},
  author={Baghi, Bobak H and Konar, Abhisek and Hogan, Francois and Jenkin, Michael and Dudek, Gregory},
  booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={9164--9171},
  year={2022},
  organization={IEEE}
}

@inproceedings{zhao2022behaviour,
  title={Behaviour Learning with Adaptive Motif Discovery and Interacting Multiple Model},
  author={Zhao, Hanging and Manderson, Travis and Zhang, Hao and Liu, Xue and Dudek, Gregory},
  booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={10788--10794},
  year={2022},
  organization={IEEE}
}

@inproceedings{wu2022attentive,
  title={Attentive Knowledge Transfer for Short-term Load Forecasting},
  author={Wu, Di and Jenkin, Michael and Xu, Yi Tian and Liu, Xue and Dudek, Gregory},
  booktitle={GLOBECOM 2022-2022 IEEE Global Communications Conference},
  pages={5285--5291},
  year={2022},
  organization={IEEE}
}

@inproceedings{hussien2022efficient,
  title={Efficient neural data compression for machine type communications via knowledge distillation},
  author={Hussien, Mostafa and Xu, Yi Tian and Wu, Di and Liu, Xue and Dudek, Gregory},
  booktitle={GLOBECOM 2022-2022 IEEE Global Communications Conference},
  pages={1169--1174},
  year={2022},
  organization={IEEE}
}

@inproceedings{kang2022generalized,
  title={A generalized load balancing policy with multi-teacher reinforcement learning},
  author={Kang, Jikun and Wang, Ju and Hu, Chengming and Liu, Xue and Dudek, Gregory},
  booktitle={GLOBECOM 2022-2022 IEEE Global Communications Conference},
  pages={3096--3101},
  year={2022},
  organization={IEEE}
}

@inproceedings{hu2022accurate,
  title={Accurate Communication Traffic Forecasting with Multi-Source Adaptive Feature Boosting},
  author={Hu, Chengming and Wang, Ju and Wu, Di and Liu, Xue and Dudek, Gregory},
  booktitle={GLOBECOM 2022-2022 IEEE Global Communications Conference},
  pages={2316--2321},
  year={2022},
  organization={IEEE}
}

@inproceedings{wu2023learning,
  title={Learning to Adapt: Communication Load Balancing via Adaptive Deep Reinforcement Learning},
  author={Wu, Di and Xu, Yi Tian and Li, Jimmy and Jenkin, Michael and Hossain, Ekram and Jang, Seowoo and Xin, Yan and Zhang, Charlie and Liu, Xue and Dudek, Gregory},
  booktitle={GLOBECOM 2023-2023 IEEE Global Communications Conference},
  pages={2973--2978},
  year={2023},
  organization={IEEE}
}

@inproceedings{wu2023energy,
  title={Energy saving in cellular wireless networks via transfer deep reinforcement learning},
  author={Wu, Di and Xu, Yi Tian and Jenkin, Michael and Jang, Seowoo and Hossain, Ekram and Liu, Xue and Dudek, Gregory},
  booktitle={GLOBECOM 2023-2023 IEEE Global Communications Conference},
  pages={7019--7024},
  year={2023},
  organization={IEEE}
}

@inproceedings{hu2023adateacher,
  title={AdaTeacher: Adaptive Multi-Teacher Weighting for Communication Load Forecasting},
  author={Hu, Chengming and Wang, Ju and Wu, Di and Xin, Yan and Zhang, Charlie and Liu, Xue and Dudek, Gregory},
  booktitle={GLOBECOM 2023-2023 IEEE Global Communications Conference},
  pages={7399--7404},
  year={2023},
  organization={IEEE}
}

@inproceedings{zhao2023zero,
  title={Zero-shot Fault Detection for Manipulators through Bayesian Inverse Reinforcement Learning},
  author={Zhao, Hanqing and Liu, Xue and Dudek, Gregory},
  booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={3582--3589},
  year={2023},
  organization={IEEE}
}

@inproceedings{zhao2023generic,
  title={A generic framework for Byzantine-tolerant consensus achievement in robot swarms},
  author={Zhao, Hanqing and Pacheco, Alexandre and Strobel, Volker and Reina, Andreagiovanni and Liu, Xue and Dudek, Gregory and Dorigo, Marco},
  booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={8839--8846},
  year={2023},
  organization={IEEE}
}

@inproceedings{holliday2023augmenting,
  title={Augmenting transit network design algorithms with deep learning},
  author={Holliday, Andrew and Dudek, Gregory},
  booktitle={2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)},
  pages={2343--2350},
  year={2023},
  organization={IEEE}
}

@inproceedings{lotfi2023robust,
  title={Robust scuba diver tracking and recovery in open water using YOLOv7, SORT, and Spiral Search},
  author={Lotfi, Faraz and Virji, Khalil and Dudek, Gregory},
  booktitle={2023 20th Conference on Robots and Vision (CRV)},
  pages={233--240},
  year={2023},
  organization={IEEE}
}

@article{wu2023reinforcement,
  title={Reinforcement learning for communication load balancing: approaches and challenges},
  author={Wu, Di and Li, Jimmy and Ferini, Amal and Xu, Yi Tian and Jenkin, Michael and Jang, Seowoo and Liu, Xue and Dudek, Gregory},
  journal={Frontiers in Computer Science},
  volume={5},
  pages={1156064},
  year={2023},
  publisher={Frontiers Media SA}
}

@inproceedings{konar2023communication,
  title={Communication load balancing via efficient inverse reinforcement learning},
  author={Konar, Abhisek and Wu, Di and Xu, Yi Tian and Jang, Seowoo and Liu, Steve and Dudek, Gregory},
  booktitle={ICC 2023-IEEE International Conference on Communications},
  pages={472--478},
  year={2023},
  organization={IEEE}
}

@inproceedings{kang2023multi,
  title={Multi-agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networks},
  author={Kang, Jikun and Wu, Di and Wang, Ju and Hossain, Ekram and Liu, Xue and Dudek, Gregory},
  booktitle={ICC 2023-IEEE International Conference on Communications},
  pages={5160--5165},
  year={2023},
  organization={IEEE}
}

@inproceedings{kozlov2023self,
  title={Self-supervised transformer architecture for change detection in radio access networks},
  author={Kozlov, Igor and Rivkin, Dmitriy and Chang, Wei-Di and Wu, Di and Liu, Xue and Dudek, Gregory},
  booktitle={ICC 2023-IEEE International Conference on Communications},
  pages={2227--2232},
  year={2023},
  organization={IEEE}
}

@inproceedings{wang2023eliminating,
  title={Eliminating space scanning: Fast mmWave beam alignment with UWB radios},
  author={Wang, Ju and Chen, Xi and Liu, Xue and Dudek, Gregory},
  booktitle={ACM Transactions on Sensor Networks},
  pages={1--20},
  year={2023},
  publisher={ACM New York, NY}
}




@inproceedings{li2022data,
  title={Data-efficient communication traffic prediction with deep transfer learning},
  author={Li, Hang and Wang, Ju and Chen, Xi and Liu, Xue and Dudek, Gregory},
  booktitle={ICC 2022-IEEE International Conference on Communications},
  pages={3190--3195},
  year={2022},
  organization={IEEE}
}


@inproceedings{kang2021hierarchical,
  title={Hierarchical policy learning for hybrid communication load balancing},
  author={Kang, Jikun and Chen, Xi and Wu, Di and Xu, Yi Tian and Liu, Xue and Dudek, Gregory and Lee, Taeseop and Park, Intaik},
  booktitle={ICC 2021-IEEE International Conference on Communications},
  pages={1--6},
  year={2021},
  organization={IEEE}
}

@inproceedings{wang2021uwb,
  title={UWB-Assisted Fast mmWave Beam Alignment},
  author={Wang, Ju and Chen, Xi and Liu, Xue and Dudek, Gregory},
  booktitle={ICC 2021-IEEE International Conference on Communications},
  pages={1--6},
  year={2021},
  organization={IEEE}
}

@inproceedings{girdhar2021optimizing,
  title={Optimizing cellular networks via continuously moving base stations on road networks},
  author={Girdhar, Yogesh and Rivkin, Dmitriy and Wu, Di and Jenkin, Michael and Liu, Xue and Dudek, Gregory},
  booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={4020--4025},
  year={2021},
  organization={IEEE}
}

@inproceedings{holliday2021scale,
  title={Scale-invariant localization using quasi-semantic object landmarks},
  author={Holliday, Andrew and Dudek, Gregory},
  booktitle={Autonomous Robots},
  pages={407--420},
  year={2021},
  publisher={Springer}
}

@inproceedings{konar2021learning,
  title={Learning goal conditioned socially compliant navigation from demonstration using risk-based features},
  author={Konar, Abhisek and Baghi, Bobak H and Dudek, Gregory},
  booktitle={IEEE Robotics and Automation Letters},
  pages={651--658},
  year={2021},
  publisher={IEEE}
}

@inproceedings{ma2022coordinated,
  title={Coordinated load balancing in mobile edge computing network: a multi-agent drl approach},
  author={Ma, Manyou and Wu, Di and Xu, Yi Tian and Li, Jimmy and Jang, Seowoo and Liu, Xue and Dudek, Gregory},
  booktitle={ICC 2022-IEEE International Conference on Communications},
  pages={619--624},
  year={2022},
  organization={IEEE}
}

@inproceedings{chen2022fidora,
  title={Fidora: Robust WiFi-based indoor localization via unsupervised domain adaptation},
  author={Chen, Xi and Li, Hang and Zhou, Chenyi and Liu, Xue and Wu, Di and Dudek, Gregory},
  booktitle={IEEE Internet of Things Journal},
  pages={9872--9888},
  year={2022},
  publisher={IEEE}
}

@inproceedings{chen2021one,
  title={One for all: Traffic prediction at heterogeneous 5G edge with data-efficient transfer learning},
  author={Chen, Xi and Wang, Ju and Li, Hang and Xu, Yi Tian and Wu, Di and Liu, Xue and Dudek, Gregory and Lee, Taeseop and Park, Intaik},
  booktitle={2021 IEEE global communications conference (GLOBECOM)},
  pages={01--06},
  year={2021},
  organization={IEEE}
}

@inproceedings{wu2021load,
  title={Load balancing for communication networks via data-efficient deep reinforcement learning},
  author={Wu, Di and Kang, Jikun and Xu, Yi Tian and Li, Hang and Li, Jimmy and Chen, Xi and Rivkin, Dmitriy and Jenkin, Michael and Lee, Taeseop and Park, Intaik and others},
  booktitle={2021 IEEE global communications conference (GLOBECOM)},
  pages={01--07},
  year={2021},
  organization={IEEE}
}

@inproceedings{hu2021afb,
  title={Afb: Improving communication load forecasting accuracy with adaptive feature boosting},
  author={Hu, Chengming and Chen, Xi and Wang, Ju and Li, Hang and Kang, Jikun and Xu, Yi Tian and Liu, Xue and Wu, Di and Jang, Seowoo and Park, Intaik and others},
  booktitle={2021 IEEE Global Communications Conference (GLOBECOM)},
  pages={01--06},
  year={2021},
  organization={IEEE}
}

@inproceedings{babarahmati2022fractal,
  title={Fractal impedance for passive controllers: a framework for interaction robotics},
  author={Babarahmati, Keyhan Kouhkiloui and Tiseo, Carlo and Smith, Joshua and Lin, Hsiu-Chin and Erden, Mustafa Suphi and Mistry, Michael},
  journal={Nonlinear Dynamics},
  volume={110},
  number={3},
  pages={2517--2533},
  year={2022},
  publisher={Springer},
  abstract={There is increasing interest in control frameworks capable of moving robots from industrial cages to unstructured environments and coexisting with humans. Despite significant improvement in some specific applications (e.g., medical robotics), there is still the need for a general control framework that improves interaction robustness and motion dynamics. Passive controllers show promising results in this direction; however, they often rely on virtual energy tanks that can guarantee passivity as long as they do not run out of energy. In this paper, a Fractal Attractor is proposed to implement a variable impedance controller that can retain passivity without relying on energy tanks. The controller generates a Fractal Attractor around the desired state using an asymptotic stable potential field, making the controller robust to discretization and numerical integration errors. The results prove that it can accurately track both trajectories and end-effector forces during interaction. Therefore, these properties make the controller ideal for applications requiring robust dynamic interaction at the end-effector.}
}

@inproceedings{abyaneh2023learning,
  title={Learning Lyapunov-stable polynomial dynamical systems through imitation},
  author={Abyaneh, Amin and Lin, Hsiu-Chin},
  journal={7th Annual Conference on Robot Learning 2023},
  year={2023},
  pdf={https://arxiv.org/pdf/2310.20605},
  abstract={Imitation learning is a paradigm to address complex motion planning problems by learning a policy to imitate an expert's behavior. However, relying solely on the expert's data might lead to unsafe actions when the robot deviates from the demonstrated trajectories. Stability guarantees have previously been provided utilizing nonlinear dynamical systems, acting as high-level motion planners, in conjunction with the Lyapunov stability theorem. Yet, these methods are prone to inaccurate policies, high computational cost, sample inefficiency, or quasi stability when replicating complex and highly nonlinear trajectories. To mitigate this problem, we present an approach for learning a globally stable nonlinear dynamical system as a motion planning policy. We model the nonlinear dynamical system as a parametric polynomial and learn the polynomial's coefficients jointly with a Lyapunov candidate. To showcase its success, we compare our method against the state of the art in simulation and conduct real-world experiments with the Kinova Gen3 Lite manipulator arm. Our experiments demonstrate the sample efficiency and reproduction accuracy of our method for various expert trajectories, while remaining stable in the face of perturbations.}
}

@inproceedings{abyaneh2024globally,
  title={Globally stable neural imitation policies},
  author={Abyaneh, Amin and Guzm{\'a}n, Mariana Sosa and Lin, Hsiu-Chin},
  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={15061--15067},
  year={2024},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10610791},
  abstract={Imitation learning mitigates the resource-intensive nature of learning policies from scratch by mimicking expert behavior. While existing methods can accurately replicate expert demonstrations, they often exhibit unpredictability in unexplored regions of the state space, thereby raising major safety concerns when facing perturbations. We propose SNDS, an imitation learning approach aimed at efficient training of scalable neural policies while formally ensuring global stability. SNDS leverages a neural architecture that enables the joint training of the policy and its associated Lyapunov candidate to ensure global stability throughout the learning process. We validate our approach through extensive simulations and deploy the trained policies on a real-world manipulator arm. The results confirm SNDS’s ability to address instability, accuracy, and computational intensity challenges highlighted in the literature, positioning it as a promising solution for scalable and stable policy learning in complex environments.}
}

@inproceedings{abyaneh2024globally,
  title={Globally stable neural imitation policies},
  author={Abyaneh, Amin and Guzm{\'a}n, Mariana Sosa and Lin, Hsiu-Chin},
  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={15061--15067},
  year={2024},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10610791},
  abstract={Imitation learning mitigates the resource-intensive nature of learning policies from scratch by mimicking expert behavior. While existing methods can accurately replicate expert demonstrations, they often exhibit unpredictability in unexplored regions of the state space, thereby raising major safety concerns when facing perturbations. We propose SNDS, an imitation learning approach aimed at efficient training of scalable neural policies while formally ensuring global stability. SNDS leverages a neural architecture that enables the joint training of the policy and its associated Lyapunov candidate to ensure global stability throughout the learning process. We validate our approach through extensive simulations and deploy the trained policies on a real-world manipulator arm. The results confirm SNDS’s ability to address instability, accuracy, and computational intensity challenges highlighted in the literature, positioning it as a promising solution for scalable and stable policy learning in complex environments.}
}

@inproceedings{abyaneh2024contractive,
  title={Contractive dynamical imitation policies for efficient out-of-sample recovery},
  author={Abyaneh, Amin and Boroujeni, Mahrokh G and Lin, Hsiu-Chin and Ferrari-Trecate, Giancarlo},
  journal={International Conference on Learning Representations (ICLR)},
  year={2024},
  pdf={https://arxiv.org/pdf/2412.07544},
  abstract={Imitation learning is a data-driven approach to learning policies from expert behavior, but it is prone to unreliable outcomes in out-of-sample (OOS) regions. While previous research relying on stable dynamical systems guarantees convergence to a desired state, it often overlooks transient behavior. We propose a framework for learning policies modeled by contractive dynamical systems, ensuring that all policy rollouts converge regardless of perturbations, and in turn, enable efficient OOS recovery. By leveraging recurrent equilibrium networks and coupling layers, the policy structure guarantees contractivity for any parameter choice, which facilitates unconstrained optimization. We also provide theoretical upper bounds for worst-case and expected loss to rigorously establish the reliability of our method in deployment. Empirically, we demonstrate substantial OOS performance improvements for simulated robotic manipulation and navigation tasks. }
}

@inproceedings{jhaveri2025convergence,
  title={Convergence Theorems for Entropy-Regularized and Distributional Reinforcement Learning},
  author={Jhaveri, Yash and Wiltzer, Harley and Shafto, Patrick and Bellemare, Marc G and Meger, David},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2025},
  pdf={https://arxiv.org/pdf/2510.08526},
  abstract={In the pursuit of finding an optimal policy, reinforcement learning (RL) methods generally ignore the properties of learned policies apart from their expected return. Thus, even when successful, it is difficult to characterize which policies will be learned and what they will do. In this work, we present a theoretical framework for policy optimization that guarantees convergence to a particular optimal policy, via vanishing entropy regularization and a temperature decoupling gambit. Our approach realizes an interpretable, diversity-preserving optimal policy as the regularization temperature vanishes and ensures the convergence of policy derived objects--value functions and return distributions. In a particular instance of our method, for example, the realized policy samples all optimal actions uniformly. Leveraging our temperature decoupling gambit, we present an algorithm that estimates, to arbitrary accuracy, the return distribution associated to its interpretable, diversity-preserving optimal policy.}
}

@inproceedings{berry2023escaping,
  title={Epistemic Uncertainty Estimation in Regression Ensemble Models with Pairwise Epistemic Estimators},
  author={Berry, Lucas and Meger, David},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2025},
  pdf={https://arxiv.org/pdf/2308.13498},
  abstract={This work introduces an efficient novel approach for epistemic uncertainty estimation for ensemble models for regression tasks using pairwise-distance estimators (PaiDEs). Utilizing the pairwise-distance between model components, these estimators establish bounds on entropy. We leverage this capability to enhance the performance of Bayesian Active Learning by Disagreement (BALD). Notably, unlike sample-based Monte Carlo estimators, PaiDEs exhibit a remarkable capability to estimate epistemic uncertainty at speeds up to 100 times faster while covering a significantly larger number of inputs at once and demonstrating superior performance in higher dimensions. To validate our approach, we conducted a varied series of regression experiments on commonly used benchmarks: 1D sinusoidal data, Pendulum, Hopper, Ant and Humanoid. For each experimental setting, an active learning framework was applied to demonstrate the advantages of PaiDEs for epistemic uncertainty estimation. We compare our approach to existing active learning methods and find that our approach outperforms on high-dimensional regression tasks.}
}


@inproceedings{yurchyk2025large,
  title={Large Pre-Trained Models for Bimanual Manipulation in 3D},
  author={Yurchyk, Hanna and Chang, Wei-Di and Dudek, Gregory and Meger, David},
  booktitle={2025 IEEE-RAS 24th International Conference on Humanoid Robots (Humanoids)},
  year={2025},
  organization={IEEE},
  pdf={https://arxiv.org/pdf/2509.20579},
  abstract={We investigate the integration of attention maps from a pre-trained Vision Transformer into voxel representations to enhance bimanual robotic manipulation. Specifically, we extract attention maps from DINOv2, a self-supervised ViT model, and interpret them as pixel-level saliency scores over RGB images. These maps are lifted into a 3D voxel grid, resulting in voxel-level semantic cues that are incorporated into a behavior cloning policy. When integrated into a state-of-the-art voxel-based policy, our attention-guided featurization yields an average absolute improvement of 8.2% and a relative gain of 21.9% across all tasks in the RLBench bimanual benchmark.}
}

@inproceedings{tremblay2025topological,
  title={Topological mapping for traversability-aware long-range navigation in off-road terrain},
  author={Tremblay, Jean-Francois and Alhosh, Julie and Petit, Louis and Lotfi, Faraz and Landauro, Lara and Meger, David},
  booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={14850--14856},
  year={2025},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/11127273/11127223/11128536.pdf},
  abstract={Autonomous robots navigating in off-road terrain like forests open new opportunities for automation. While off-road navigation has been studied, existing work often relies on clearly delineated pathways. We present a method allowing for long-range planning, exploration and low-level control in unknown off-trail forest terrain, using vision and GPS only. We represent outdoor terrain with a topological map, which is a set of panoramic snapshots connected with edges containing traversability information. A novel traversability analysis method is demonstrated, predicting the existence of a safe path towards a target in an image. Navigating between nodes is done using goal-conditioned behavior cloning, leveraging the power of a pretrained vision transformer. An exploration planner is presented, efficiently covering an unknown off-road area with unknown traversability using a frontiers-based approach. The approach is successfully deployed to autonomously explore two 400 m^2 forest sites unseen during training, in difficult conditions for navigation.}
}

@inproceedings{tremblay2025learning,
  title={Learning active tactile perception through belief-space control},
  author={Tremblay, Jean-Francois and Meger, David and Hogan, Francois R and Dudek, Gregory},
  booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={8702--8708},
  year={2025},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/11127273/11127223/11127425.pdf},
  abstract={Robots operating in an open world will encounter novel objects with unknown physical properties, such as mass, friction, or size. These robots will need to sense these properties through interaction prior to performing downstream tasks with the objects. We propose a method that autonomously learns tactile exploration policies by developing a generative world model that is leveraged to 1) estimate the object's physical parameters using a differentiable Bayesian filtering algorithm and 2) develop an exploration policy using an information-gathering model predictive controller. We evaluate our method on three simulated tasks where the goal is to estimate a desired object property (mass, height or toppling height) through physical interaction. We find that our method is able to discover policies that efficiently gather information about the desired property in an intuitive manner. Finally, we validate our method on a real robot system for the height estimation task, where our method is able to successfully learn and execute an information-gathering policy from scratch.}
}

@inproceedings{chang2025generalizable,
  title={Generalizable imitation learning through pre-trained representations},
  author={Chang, Wei-Di and Hogan, Francois and Fujimoto, Scott and Meger, David and Dudek, Gregory},
  booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={1--8},
  year={2025},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/11127273/11127223/11127800.pdf},
  abstract={In this paper, we leverage self-supervised vision transformer models and their emergent semantic abilities to improve the generalization abilities of imitation learning policies. We introduce DVK, an imitation learning algorithm that leverages rich pre-trained Visual Transformer patch-level embeddings to obtain better generalization when learning through demonstrations. Our learner sees the world by clustering appearance features into groups associated with semantic concepts, forming stable keypoints that generalize across a wide range of appearance variations and object types. We demonstrate how this representation enables generalized behaviour by evaluating imitation learning across a diverse dataset of object manipulation tasks. To facilitate further study of generalization in Imitation Learning, all of our code for the method and evaluation, as well as the dataset, is made available.}
}

@article{danesh2025safe,
  title={Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation},
  author={Danesh, Mohamad H. and Wabartha, Maxime and Wu, Stanley and Pineau, Joelle and Lin, Hsiu-Chin},
  journal={Multi-Disciplinary Conference on Reinforcement Learning and Decision Making (RLDM)},
  year={2025},
  pdf={https://arxiv.org/pdf/2507.06111},
  abstract={Deploying reinforcement learning (RL) policies in real-world involves significant challenges, including distribution shifts, safety concerns, and the impracticality of direct interactions during policy refinement. Existing methods, such as domain randomization (DR) and off-dynamics RL, enhance policy robustness by direct interaction with the target domain, an inherently unsafe practice. We propose Uncertainty-Aware RL (UARL), a novel framework that prioritizes safety during training by addressing Out-Of-Distribution (OOD) detection and policy adaptation without requiring direct interactions in target domain. UARL employs an ensemble of critics to quantify policy uncertainty and incorporates progressive environmental randomization to prepare the policy for diverse real-world conditions. By iteratively refining over high-uncertainty regions of the state space in simulated environments, UARL enhances robust generalization to the target domain without explicitly training on it. We evaluate UARL on MuJoCo benchmarks and a quadrupedal robot, demonstrating its effectiveness in reliable OOD detection, improved performance, and enhanced sample efficiency compared to baselines.}
}

@article{alhosh2025tractable,
  title={Tractable Representations for Convergent Approximation of Distributional HJB Equations},
  author={Alhosh, Julie and Wiltzer, Harley and Meger, David},
  journal={Multi-Disciplinary Conference on Reinforcement Learning and Decision Making (RLDM)},
  year={2025},
  pdf={https://arxiv.org/pdf/2503.05563},
  abstract={In reinforcement learning (RL), the long-term behavior of decision-making policies is evaluated based on their average returns. Distributional RL has emerged, presenting techniques for learning return distributions, which provide additional statistics for evaluating policies, incorporating risk-sensitive considerations. When the passage of time cannot naturally be divided into discrete time increments, researchers have studied the continuous-time RL (CTRL) problem, where agent states and decisions evolve continuously. In this setting, the Hamilton-Jacobi-Bellman (HJB) equation is well established as the characterization of the expected return, and many solution methods exist. However, the study of distributional RL in the continuous-time setting is in its infancy. Recent work has established a distributional HJB (DHJB) equation, providing the first characterization of return distributions in CTRL. These equations and their solutions are intractable to solve and represent exactly, requiring novel approximation techniques. This work takes strides towards this end, establishing conditions on the method of parameterizing return distributions under which the DHJB equation can be approximately solved. Particularly, we show that under a certain topological property of the mapping between statistics learned by a distributional RL algorithm and corresponding distributions, approximation of these statistics leads to close approximations of the solution of the DHJB equation. Concretely, we demonstrate that the quantile representation common in distributional RL satisfies this topological property, certifying an efficient approximation algorithm for continuous-time distributional RL.}
}

@article{shahryari2025programmable,
  title={Programmable shape-preserving soft robotics arm via multimodal multistability},
  author={Shahryari, Benyamin and Mofatteh, Hossein and Sargazi, Arian and Mirabolghasemi, Armin and Meger, David and Akbarzadeh, Abdolhamid},
  journal={Advanced Functional Materials},
  volume={35},
  number={6},
  pages={2407651},
  year={2025},
  publisher={Wiley Online Library},
  pdf={https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/adfm.202407651},
  abstract={Inflatable multistable materials have significantly advanced the design of shape‐preserving soft robotic arms, offering substantial benefits in terms of shape adaptability, energy efficiency, and safety, ensuring operational reliability even in the event of sudden power loss. However, existing strategies for realizing multistable arms often limit themselves to a single mode of multistability, commonly with rotationally symmetric designs favoring extension stability and asymmetric designs inducing bending stability. To address the limitation, this study introduces a pioneering platform termed multimodal multistability that utilizes geometrical frustration. A single cylindrical symmetric cell, designed for extension bistability, could achieve frustrated multistable states in bending by controlling the cell with multiple degrees of freedom incorporated pneumatic actuator. This platform extends the spectrum of attainable stable trajectories while preserving essential attributes of arms, such as load-bearability, programmability, and reversibility of shape changes. Leveraging a pneumatic system with four degrees of freedom for pressure control, not only enables capturing previously unexplored stable configurations in mechanical metastructures but also allows for the control of their deformation modes. With applications spanning space exploration, medical instruments, and rescue missions, the multimodal multistability promises unparalleled flexibility and efficiency in the design and operation of soft robots.}
}

@inproceedings{chung2024parseval,
  title={Parseval regularization for continual reinforcement learning},
  author={Chung, Wesley and Cherif, Lynn and Meger, David and Precup, Doina},
  journal={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={37},
  pages={127937--127967},
  year={2024},
  pdf={https://proceedings.neurips.cc/paper_files/paper/2024/file/e6df4efa20adf8ef9acb80e94072a429-Paper-Conference.pdf},
  abstract={Plasticity loss, trainability loss, and primacy bias have been identified as issues arising when training deep neural networks on sequences of tasks---referring to the increased difficulty in training on new tasks. We propose to use Parseval regularization, which maintains orthogonality of weight matrices, to preserve useful optimization properties and improve training in a continual reinforcement learning setting. We show that it provides significant benefits to RL agents on a suite of gridworld, CARL and MetaWorld tasks. We conduct comprehensive ablations to identify the source of its benefits and investigate the effect of certain metrics associated to network trainability including weight matrix rank, weight norms and policy entropy.}
}

@inproceedings{wiltzer2024action,
  title={Action gaps and advantages in continuous-time distributional reinforcement learning},
  author={Wiltzer, Harley and Bellemare, Marc and Meger, David and Shafto, Patrick and Jhaveri, Yash},
  journal={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={37},
  pages={47815--47848},
  year={2024},
  pdf={https://proceedings.neurips.cc/paper_files/paper/2024/file/55769e1208c7f45e9acc98f06279c10c-Paper-Conference.pdf},
  abstract={When decisions are made at high frequency, traditional reinforcement learning (RL) methods struggle to accurately estimate action values. In turn, their performance is inconsistent and often poor. Whether the performance of distributional RL (DRL) agents suffers similarly, however, is unknown. In this work, we establish that DRL agents are sensitive to the decision frequency. We prove that action-conditioned return distributions collapse to their underlying policy's return distribution as the decision frequency increases. We quantify the rate of collapse of these return distributions and exhibit that their statistics collapse at different rates. Moreover, we define distributional perspectives on action gaps and advantages. In particular, we introduce the superiority as a probabilistic generalization of the advantage---the core object of approaches to mitigating performance issues in high-frequency value-based RL. In addition, we build a superiority-based DRL algorithm. Through simulations in an option-trading domain, we validate that proper modeling of the superiority distribution produces improved controllers at high decision frequencies.}
}

@inproceedings{berry2024shedding,
  title={Shedding light on large generative networks: Estimating epistemic uncertainty in diffusion models},
  author={Berry, Lucas and Brando, Axel and Meger, David},
  booktitle={The 40th Conference on Uncertainty in Artificial Intelligence (UAI)},
  year={2024},
  pdf={https://openreview.net/pdf?id=512IkGDqA8},
  abstract={Generative diffusion models, notable for their large parameter count (exceeding 100 million) and operation within high-dimensional image spaces, pose significant challenges for traditional uncertainty estimation methods due to computational demands. In this work, we introduce an innovative framework, Diffusion Ensembles for Capturing Uncertainty (DECU), designed for estimating epistemic uncertainty for diffusion models. The DECU framework introduces a novel method that efficiently trains ensembles of conditional diffusion models by incorporating a static set of pre-trained parameters, drastically reducing the computational burden and the number of parameters that require training. Additionally, DECU employs Pairwise-Distance Estimators (PaiDEs) to accurately measure epistemic uncertainty by evaluating the mutual information between model outputs and weights in high-dimensional spaces. The effectiveness of this framework is demonstrated through experiments on the ImageNet dataset, highlighting its capability to capture epistemic uncertainty, specifically in under-sampled image classes.}
}

@inproceedings{lotfi2024uncertainty,
  title={Uncertainty-aware hybrid paradigm of nonlinear MPC and model-based RL for offroad navigation: Exploration of transformers in the predictive model},
  author={Lotfi, Faraz and Virji, Khalil and Faraji, Farnoosh and Berry, Lucas and Holliday, Andrew and Meger, David and Dudek, Gregory},
  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={2925--2931},
  year={2024},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/iel8/10609961/10609862/10610452.pdf},
  abstract={In this paper, we investigate a hybrid scheme that combines nonlinear model predictive control (MPC) and model-based reinforcement learning (RL) for navigation planning of an autonomous model car across offroad, unstructured terrains without relying on predefined maps. Our innovative approach takes inspiration from BADGR, an LSTM-based network that primarily concentrates on environment modeling, but distinguishes itself by substituting LSTM modules with transformers to greatly elevate the performance of our model. Addressing uncertainty within the system, we train an ensemble of predictive models and estimate the mutual information between model weights and outputs, facilitating dynamic horizon planning through the introduction of variable speeds. Further enhancing our methodology, we incorporate a nonlinear MPC controller that accounts for the intricacies of the vehicle’s model and states. The model-based RL facet produces steering angles and quantifies inherent uncertainty. At the same time, the nonlinear MPC suggests optimal throttle settings, striking a balance between goal attainment speed and managing model uncertainty influenced by velocity. In the conducted studies, our approach excels over the existing baseline by consistently achieving higher metric values in predicting future events and seamlessly integrating the vehicle's kinematic model for enhanced decision-making.}
}

@article{panangaden2024policy,
  title={Policy gradient methods in the presence of symmetries and state abstractions},
  author={Panangaden, Prakash and Rezaei-Shoshtari, Sahand and Zhao, Rosie and Meger, David and Precup, Doina},
  journal={Journal of Machine Learning Research},
  volume={25},
  number={71},
  pages={1--57},
  year={2024},
  pdf={http://www.jmlr.org/papers/volume25/23-1415/23-1415.pdf},
  abstract={Reinforcement learning (RL) on high-dimensional and complex problems relies on abstraction for improved efficiency and generalization. In this paper, we study abstraction in the continuous-control setting, and extend the definition of Markov decision process (MDP) homomorphisms to the setting of continuous state and action spaces. We derive a policy gradient theorem on the abstract MDP for both stochastic and deterministic policies. Our policy gradient results allow for leveraging approximate symmetries of the environment for policy optimization. Based on these theorems, we propose a family of actor-critic algorithms that are able to learn the policy and the MDP homomorphism map simultaneously, using the lax bisimulation metric. Finally, we introduce a series of environments with continuous symmetries to further demonstrate the ability of our algorithm for action abstraction in the presence of such symmetries. We demonstrate the effectiveness of our method on our environments, as well as on challenging visual control tasks from the DeepMind Control Suite. Our method's ability to utilize MDP homomorphisms for representation learning leads to improved performance, and the visualizations of the latent space clearly demonstrate the structure of the learned abstraction.}
}

@inproceedings{changimitation,
  title={Imitation Learning from Observation through Optimal Transport},
  author={Chang, Wei-Di and Fujimoto, Scott and Meger, David and Dudek, Gregory},
  booktitle={Reinforcement Learning Conference},
  year={2024},
  pdf={https://arxiv.org/pdf/2310.01632},
  abstract={Imitation Learning from Observation (ILfO) is a setting in which a learner tries to imitate the behavior of an expert, using only observational data and without the direct guidance of demonstrated actions. In this paper, we re-examine optimal transport for IL, in which a reward is generated based on the Wasserstein distance between the state trajectories of the learner and expert. We show that existing methods can be simplified to generate a reward function without requiring learned models or adversarial learning. Unlike many other state-of-the-art methods, our approach can be integrated with any RL algorithm and is amenable to ILfO. We demonstrate the effectiveness of this simple approach on a variety of continuous control tasks and find that it surpasses the state of the art in the IlfO setting, achieving expert-level performance across a range of evaluation domains even when observing only a single expert trajectory without actions.}
}

@article{fujimoto2023sale,
  title={For {SALE}: State-action representation learning for deep reinforcement learning},
  author={Fujimoto, Scott and Chang, Wei-Di and Smith, Edward and Gu, Shixiang Shane and Precup, Doina and Meger, David},
  journal={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={36},
  pages={61573--61624},
  year={2023},
  pdf={https://proceedings.neurips.cc/paper_files/paper/2023/file/c20ac0df6c213db6d3a930fe9c7296c8-Paper-Conference.pdf},
  abstract={In reinforcement learning (RL), representation learning is a proven tool for complex image-based tasks, but is often overlooked for environments with low-level states, such as physical control problems. This paper introduces SALE, a novel approach for learning embeddings that model the nuanced interaction between state and action, enabling effective representation learning from low-level states. We extensively study the design space of these embeddings and highlight important design considerations. We integrate SALE and an adaptation of checkpoints for RL into TD3 to form the TD7 algorithm, which significantly outperforms existing continuous control algorithms. On OpenAI gym benchmark tasks, TD7 has an average performance gain of 276.7% and 50.7% over TD3 at 300k and 5M time steps, respectively, and works in both the online and offline settings.}
}

@inproceedings{lotfi2023constrained,
  title={Constrained Robotic Navigation on Preferred Terrains Using LLMs and Speech Instruction: Exploiting the Power of Adverbs},
  author={Lotfi, Faraz and Faraji, Farnoosh and Kakodkar, Nikhil and Manderson, Travis and Meger, David and Dudek, Gregory},
  booktitle={International Symposium on Experimental Robotics},
  pages={118--128},
  year={2023},
  organization={Springer},
  pdf={https://arxiv.org/pdf/2404.02294},
  abstract={This paper explores leveraging large language models for mapless offroad navigation using generative AI, thereby eliminating the need for data collection and annotation. We propose a method where the robot receives speech instructions, converted to text through Whisper, and a large language model (LLM) like GPT model extracts landmarks, preferred terrains, and crucial adverbs translated into speed settings for constrained navigation. A language-driven semantic segmentation model generates text-based masks for identifying the required landmarks and terrains in images. By translating 2D image points to the vehicle’s motion plane using camera parameters, an MPC controller guides the vehicle towards the desired terrains. This approach enables adaptability to diverse environments and enhances instructions for navigating complex and challenging terrains.}
}

@inproceedings{berry2023normalizing,
  title={Normalizing flow ensembles for rich aleatoric and epistemic uncertainty modeling},
  author={Berry, Lucas and Meger, David},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  number={6},
  pages={6806--6814},
  year={2023},
  pdf={https://ojs.aaai.org/index.php/AAAI/article/download/25834/25606},
  abstract={In this work, we demonstrate how to reliably estimate epistemic uncertainty while maintaining the flexibility needed to capture complicated aleatoric distributions. To this end, we propose an ensemble of Normalizing Flows (NF), which are state-of-the-art in modeling aleatoric uncertainty. The ensembles are created via sets of fixed dropout masks, making them less expensive than creating separate NF models. We demonstrate how to leverage the unique structure of NFs, base distributions, to estimate aleatoric uncertainty without relying on samples, provide a comprehensive set of baselines, and derive unbiased estimates for differential entropy. The methods were applied to a variety of experiments, commonly used to benchmark aleatoric and epistemic uncertainty estimation: 1D sinusoidal data, 2D windy grid-world (Wet Chicken), Pendulum, and Hopper. In these experiments, we setup an active learning framework and evaluate each model's capability at measuring aleatoric and epistemic uncertainty. The results show the advantages of using NF ensembles in capturing complicated aleatoric while maintaining accurate epistemic uncertainty estimates.}
}

@inproceedings{rezaei2023hypernetworks,
  title={Hypernetworks for zero-shot transfer in reinforcement learning},
  author={Rezaei-Shoshtari, Sahand and Morissette, Charlotte and Hogan, Francois R and Dudek, Gregory and Meger, David},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  number={8},
  pages={9579--9587},
  year={2023},
  pdf={https://ojs.aaai.org/index.php/AAAI/article/download/26146/25918},
  abstract={In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objective and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hypernetwork that can generate near-optimal value functions and policies, given the parameters of the MDP. We show that, under certain conditions, this mapping can be considered as a supervised learning problem. We empirically evaluate the effectiveness of our method for zero-shot transfer to new reward and transition dynamics on a series of continuous control tasks from DeepMind Control Suite. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.}
}

@inproceedings{rivkin2023ansel,
  title={ANSEL Photobot: A Robot Event Photographer with Semantic Intelligence},
  author={Rivkin, Dmitriy and Dudek, Gregory and Kakodkar, Nikhil and Meger, David and Limoyo, Oliver and Jenkin, Michael and Liu, Xue and Hogan, Francois},
  booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={8262--8268},
  year={2023},
  organization={IEEE},
  pdf={https://arxiv.org/pdf/2302.07931},
  abstract={Our work examines the way in which large language models can be used for robotic planning and sampling, specifically the context of automated photographic documentation. Specifically, we illustrate how to produce a photo-taking robot with an exceptional level of semantic awareness by leveraging recent advances in general purpose language (LM) and vision-language (VLM) models. Given a high-level description of an event we use an LM to generate a natural-language list of photo descriptions that one would expect a photographer to capture at the event. We then use a VLM to identify the best matches to these descriptions in the robot's video stream. The photo portfolios generated by our method are consistently rated as more appropriate to the event by human evaluators than those generated by existing methods.}
}

@article{rezaei2022continuous,
  title={Continuous {MDP} homomorphisms and homomorphic policy gradient},
  author={Rezaei-Shoshtari, Sahand and Zhao, Rosie and Panangaden, Prakash and Meger, David and Precup, Doina},
  journal={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={35},
  pages={20189--20204},
  year={2022},
  pdf={https://proceedings.neurips.cc/paper_files/paper/2022/file/7f44f98e5e70dea605d0c5baca231c58-Paper-Conference.pdf},
  abstract={Abstraction has been widely studied as a way to improve the efficiency and generalization of reinforcement learning algorithms. In this paper, we study abstraction in the continuous-control setting. We extend the definition of MDP homomorphisms to encompass continuous actions in continuous state spaces. We derive a policy gradient theorem on the abstract MDP, which allows us to leverage approximate symmetries of the environment for policy optimization. Based on this theorem, we propose an actor-critic algorithm that is able to learn the policy and the MDP homomorphism map simultaneously, using the lax bisimulation metric. We demonstrate the effectiveness of our method on benchmark tasks in the DeepMind Control Suite. Our method's ability to utilize MDP homomorphisms for representation learning leads to improved performance when learning from pixel observations.}
}

@inproceedings{wiltzer2022distributional,
  title={Distributional {H}amilton-{J}acobi-{B}ellman equations for continuous-time reinforcement learning},
  author={Wiltzer, Harley E and Meger, David and Bellemare, Marc G},
  booktitle={International Conference on Machine Learning (ICML)},
  pages={23832--23856},
  year={2022},
  organization={PMLR},
  pdf={https://proceedings.mlr.press/v162/wiltzer22a/wiltzer22a.pdf},
  abstract={Continuous-time reinforcement learning offers an appealing formalism for describing control problems in which the passage of time is not naturally divided into discrete increments. Here we consider the problem of predicting the distribution of returns obtained by an agent interacting in a continuous-time, stochastic environment. Accurate return predictions have proven useful for determining optimal policies for risk-sensitive control, learning state representations, multiagent coordination, and more. We begin by establishing the distributional analogue of the Hamilton-Jacobi-Bellman (HJB) equation for Ito diffusions and the broader class of Feller-Dynkin processes. We then specialize this equation to the setting in which the return distribution is approximated by N uniformly-weighted particles, a common design choice in distributional algorithms. Our derivation highlights additional terms due to statistical diffusivity which arise from the proper handling of distributions in the continuous-time setting. Based on this, we propose a tractable algorithm for approximately solving the distributional HJB based on a JKO scheme, which can be implemented in an online, control algorithm. We demonstrate the effectiveness of such an algorithm in a synthetic control problem.}
}

@inproceedings{fujimoto2022should,
  title={Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error},
  author={Fujimoto, Scott and Meger, David and Precup, Doina and Nachum, Ofir and Gu, Shixiang Shane},
  booktitle={International Conference on Machine Learning (ICML)},
  pages={6918--6943},
  year={2022},
  organization={PMLR},
  pdf={https://arxiv.org/pdf/2201.12417},
  abstract={In this work, we study the use of the Bellman equation as a surrogate objective for value prediction accuracy. While the Bellman equation is uniquely solved by the true value function over all state-action pairs, we find that the Bellman error (the difference between both sides of the equation) is a poor proxy for the accuracy of the value function. In particular, we show that (1) due to cancellations from both sides of the Bellman equation, the magnitude of the Bellman error is only weakly related to the distance to the true value function, even when considering all state-action pairs, and (2) in the finite data regime, the Bellman equation can be satisfied exactly by infinitely many suboptimal solutions. This means that the Bellman error can be minimized without improving the accuracy of the value function. We demonstrate these phenomena through a series of propositions, illustrative toy examples, and empirical analysis in standard benchmark domains.}
}

@inproceedings{hansen2022visuotactile,
  title={Visuotactile-rl: Learning multimodal manipulation policies with deep reinforcement learning},
  author={Hansen, Johanna and Hogan, Francois and Rivkin, Dmitriy and Meger, David and Jenkin, Michael and Dudek, Gregory},
  booktitle={2022 International Conference on Robotics and Automation (ICRA)},
  pages={8298--8304},
  year={2022},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9812019},
  abstract={Manipulating objects with dexterity requires timely feedback that simultaneously leverages the senses of vision and touch. In this paper, we focus on the problem setting where both visual and tactile sensors provide pixel-level feedback for Visuotactile reinforcement learning agents. We investigate the challenges associated with multimodal learning and propose several improvements to existing RL methods; including tactile gating, tactile data augmentation, and visual degradation. When compared with visual-only and tactile-only baselines, our Visuotactile-RL agents showcase (1) significant improvements in contact-rich tasks; (2) improved robustness to visual changes (lighting/camera view) in the workspace; and (3) resilience to physical changes in the task environment (weight/friction of objects).}
}

@article{chang2021flow,
  title={Il-flow: Imitation learning from observation using normalizing flows},
  author={Chang, Wei-Di and Higuera, Juan Camilo Gamboa and Fujimoto, Scott and Meger, David and Dudek, Gregory},
  journal={NeurIPS Workshop on Robot Learning: Self-Supervised and Lifelong Learning},
  year={2021},
  pdf={https://arxiv.org/pdf/2205.09251},
  abstract={We present an algorithm for Inverse Reinforcement Learning (IRL) from expert state observations only. Our approach decouples reward modelling from policy learning, unlike state-of-the-art adversarial methods which require updating the reward model during policy search and are known to be unstable and difficult to optimize. Our method, IL-flOw, recovers the expert policy by modelling state-state transitions, by generating rewards using deep density estimators trained on the demonstration trajectories, avoiding the instability issues of adversarial methods. We demonstrate that using the state transition log-probability density as a reward signal for forward reinforcement learning translates to matching the trajectory distribution of the expert demonstrations, and experimentally show good recovery of the true reward signal as well as state of the art results for imitation from observation on locomotion and robotic continuous control tasks.}
}


@inproceedings{mozifian2022learning,
  title={Learning Successor Feature Representations to Train Robust Policies for Multi-task Learning},
  author={Mozifian, Melissa and Fox, Dieter and Meger, David and Ramos, Fabio and Garg, Animesh},
  booktitle={NeurIPS Deep Reinforcement Learning Workshop},
  year={2022},
  pdf={https://openreview.net/pdf?id=iYJFH9D-oKl},
  abstract={The deep reinforcement learning (RL) framework has shown great promise to tackle sequential decision-making problems, where the agent learns to behave optimally through interactions with the environment and receiving rewards. The ability of an RL agent to learn different reward functions concurrently has many benefits, such as the decomposition of task rewards and promoting skill reuse. In this paper, we consider the problem of continuous control for robot manipulation tasks with an explicit representation that promotes skill reuse while learning multiple tasks with similar reward functions. Our approach relies on two key concepts: successor features (SFs), a value function representation that decouples the dynamics of the environment from the rewards, and an actor-critic framework that incorporates the learned SFs representation. SFs form a natural bridge between model-based and model-free RL methods. We first show how to learn a decomposable representation required by SFs as a pre-training stage. The proposed architecture is able to learn decoupled state and reward feature representations for non-linear reward functions. We then evaluate the feasibility of integrating SFs into an actor-critic framework, which is more tailored for tasks solved with deep RL algorithms. The approach is empirically tested on non-trivial continuous control problems with compositional structure built into the reward functions of the tasks.}
}

@inproceedings{pearce2022adaptive,
	title={Adaptive Confidence Calibration},
	author={Pearce, Jonathan and Meger, David},
	booktitle={Proceedings of the Canadian Conference on Artificial Intelligence},
	year={2022},
	pdf={https://scholar.archive.org/work/cnrgckzswjd7bafjtuhv7qc3yq/access/wayback/https://caiac.pubpub.org/pub/camnr5ix/download/pdf},
	abstract={Confidence calibration is a quickly growing area of research in deep learning, including computer vision applications. New model architectures and loss functions are being introduced to improve model calibration, an important topic for safety critical AI applications. In order to evaluate these new methods, they are frequently compared to simple baselines, which serve as a comparison and help measure new methods' effectiveness. Popular baselines for evaluating model confidence calibration include label smoothing, mixup and dropout. Despite these methods frequent use and simple implementations, the paramter values that define them are rarely validated, and are usually a default value. This paper demonstrates the danger in using these default values for calibration benchmarks on common datasets; poor model calibration, specifically, a model that is purely over- or underconfident. We present an adaptive framework that can adjust the parameter value of these baseline methods during training based on validation accuracy and confidence to maintain good model calibration and balance over- and underconfidence. A disadvantage of binning-based calibration metrics is that they are not differentiable and therefore cannot be added to the training loss to penalize calibration error. Our adaptive framework solves this problem and provides a solution for optimizing calibration methods. Experiments with the CIFAR-10 and CIFAR-100 image classification datasets show that our approach improves model calibration compared to using these popular methods with default values and we achieve good model calibration regardless of model architecture and dataset. Further, our analysis provides a comparison between these baseline methods that can be considered for future confidence calibration research.}
}

@inproceedings{rivkin2021learning,
  title={Learning Assisted Identification of Scenarios Where Network Optimization Algorithms Under-Perform},
  author={Rivkin, Dmitriy and Meger, David and Wu, Di and Chen, Xi and Liu, Xue and Dudek, Gregory},
  booktitle={2021 IEEE Global Communications Conference (GLOBECOM)},
  pages={1--7},
  year={2021},
  organization={IEEE},
  pdf={https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9685627},
  abstract={We present a generative adversarial method that uses deep learning to identify network load traffic conditions in which network optimization algorithms under-perform other known algorithms: the Deep Convolutional Failure Generator (DCFG). The spatial distribution of network load presents challenges for network operators for tasks such as load balancing, in which a network optimizer attempts to maintain high quality communication while at the same time abiding capacity constraints. Testing a network optimizer for all possible load distributions is challenging if not impossible. We propose a novel method that searches for load situations where a target network optimization method under-performs baseline, which are key test cases that can be used for future refinement and performance optimization. By modeling a realistic network simulator's quality assessments with a deep network and, in parallel, optimizing a load generation network, our method efficiently searches the high dimensional space of load patterns and reliably finds cases in which a target network optimization method under-performs a baseline by a significant margin.}
}

@article{smith2021active,
  title={Active 3d shape reconstruction from vision and touch},
  author={Smith, Edward and Meger, David and Pineda, Luis and Calandra, Roberto and Malik, Jitendra and Romero Soriano, Adriana and Drozdzal, Michal},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={16064--16078},
  year={2021},
  abstract={Humans build 3D understandings of the world through active object exploration, using jointly their senses of vision and touch. However, in 3D shape reconstruction, most recent progress has relied on static datasets of limited sensory data such as RGB images, depth maps or haptic readings, leaving the active exploration of the shape largely unexplored. In active touch sensing for 3D reconstruction, the goal is to actively select the tactile readings that maximize the improvement in shape reconstruction accuracy. However, the development of deep learning-based active touch models is largely limited by the lack of frameworks for shape exploration. In this paper, we focus on this problem and introduce a system composed of: 1) a haptic simulator leveraging high spatial resolution vision-based tactile sensors for active touching of 3D objects; 2) a mesh-based 3D shape reconstruction model that relies on tactile or visuotactile signals; and 3) a set of data-driven solutions with either tactile or visuotactile priors to guide the shape exploration. Our framework enables the development of the first fully data-driven solutions to active touch on top of learned models for object understanding. Our experiments show the benefits of such solutions in the task of 3D shape understanding where our models consistently outperform natural baselines. We provide our framework as a tool to foster future research in this direction.},
  pdf={https://proceedings.neurips.cc/paper/2021/file/8635b5fd6bc675033fb72e8a3ccc10a0-Paper.pdf}
}

@inproceedings{wapnick2021trajectory,
  title={Trajectory-constrained deep latent visual attention for improved local planning in presence of heterogeneous terrain},
  author={Wapnick, Stefan and Manderson, Travis and Meger, David and Dudek, Gregory},
  booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={460--467},
  year={2021},
  organization={IEEE},
  abstract={We present a reward-predictive, model-based learning method featuring trajectory-constrained visual attention for use in mapless, local visual navigation tasks. Our method learns to place visual attention at locations in latent image space which follow trajectories caused by vehicle control actions to later enhance predictive accuracy during planning. Our attention model is jointly optimized by the task-specific loss and additional trajectory-constraint loss, allowing adaptability yet encouraging a regularized structure for improved generalization and reliability. Importantly, visual attention is applied in latent feature map space instead of raw image space to promote efficient planning. We validated our model in visual navigation tasks of planning low turbulence, collision-free trajectories in off-road settings and hill climbing with locking differentials in the presence of slippery terrain. Experiments involved randomized procedural generated simulation and real-world environments. We found our method improved generalization and learning efficiency when compared to no-attention and self-attention alternatives.},
  pdf={https://ieeexplore.ieee.org/iel7/9635848/9635849/09636422.pdf}
}

@inproceedings{cheng2021latent,
  title={Latent attention augmentation for robust autonomous driving policies},
  author={Cheng, Ran and Agia, Christopher and Shkurti, Florian and Meger, David and Dudek, Gregory},
  booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={130--136},
  year={2021},
  organization={IEEE},
  abstract={Model-free reinforcement learning has become a
viable approach for vision-based robot control. However, sample
complexity and adaptability to domain shifts remain persistent
challenges when operating in high-dimensional observation
spaces (images, LiDAR), such as those that are involved in
autonomous driving. In this paper, we propose a flexible frame-
work by which a policy’s observations are augmented with
robust attention representations in the latent space to guide the
agent’s attention during training. Our method encodes local and
global descriptors of the augmented state representations into
a compact latent vector, and scene dynamics are approximated
by a recurrent network that processes the latent vectors in
sequence. We outline two approaches for constructing attention
maps; a supervised pipeline leveraging semantic segmentation
networks, and an unsupervised pipeline relying only on classical
image processing techniques. We conduct our experiments in
simulation and test the learned policy against varying seasonal
effects and weather conditions. Our design decisions are sup-
ported in a series of ablation studies. The results demonstrate
that our state augmentation method both improves learning
efficiency and encourages robust domain adaptation when
compared to common end-to-end frameworks and methods that
learn directly from intermediate representations.},
  pdf={https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9636449}
}

@inproceedings{huang2021autonomous,
  title={An autonomous probing system for collecting measurements at depth from small surface vehicles},
  author={Huang, Yuying and Yao, Yiming and Hansen, Johanna and Mallette, Jeremy and Manjanna, Sandeep and Dudek, Gregory and Meger, David},
  booktitle={OCEANS 2021},
  pages={1--6},
  year={2021},
  organization={IEEE},
  abstract={This paper presents the portable autonomous probing system (APS), a low-cost robotic design for collecting water quality measurements at targeted depths from an autonomous surface vehicle (ASV). This system fills an important, but often overlooked niche in marine sampling by enabling mobile sensor observations throughout the near-surface water column without the need for advanced underwater equipment. We present a probe delivery mechanism built with commercially available components and describe the corresponding winch control system. and data captured from this system in a field deployment. Finally, we discuss design trade-offs and present areas for future improvement.},
  pdf={https://arxiv.org/pdf/2110.14738}
}

@inproceedings{fujimoto2021deep,
  title={A deep reinforcement learning approach to marginalized importance sampling with the successor representation},
  author={Fujimoto, Scott and Meger, David and Precup, Doina},
  booktitle={International Conference on Machine Learning (ICML)},
  pages={3518--3529},
  year={2021},
  organization={PMLR},
  abstract={Marginalized importance sampling (MIS), which measures the density ratio between the state-action occupancy of a target policy and that of a sampling distribution, is a promising approach for off-policy evaluation. However, current state-of-the-art MIS methods rely on complex optimization tricks and succeed mostly on simple toy problems. We bridge the gap between MIS and deep reinforcement learning by observing that the density ratio can be computed from the successor representation of the target policy. The successor representation can be trained through deep reinforcement learning methodology and decouples the reward optimization from the dynamics of the environment, making the resulting algorithm stable and applicable to high-dimensional domains. We evaluate the empirical performance of our approach on a variety of challenging Atari and MuJoCo environments.},
  pdf={https://proceedings.mlr.press/v139/fujimoto21a/fujimoto21a.pdf}
}

@inproceedings{tremblay2021multimodal,
  title={Multimodal dynamics modeling for off-road autonomous vehicles},
  author={Tremblay, Jean-Francois and Manderson, Travis and Noca, Aur{\'e}lio and Dudek, Gregory and Meger, David},
  booktitle=icra,
  pages={1796--1802},
  year={2021},
  pdf={https://ieeexplore.ieee.org/iel7/9560720/9560666/09561910.pdf}
}

@inproceedings{wang2021robotic,
  title={Robotic object manipulation with full-trajectory gan-based imitation learning},
  author={Wang, Haoxu and Meger, David},
  booktitle=crv,
  pages={57--63},
  year={2021},
  pdf={https://www.researchgate.net/profile/Haoxu-Wang-2/publication/353023534_Robotic_Object_Manipulation_with_Full-Trajectory_GAN-Based_Imitation_Learning/links/6720965dedbc012ea14575dc/Robotic-Object-Manipulation-with-Full-Trajectory-GAN-Based-Imitation-Learning.pdf}
}

@inproceedings{rezaei2021learning,
  title={Learning intuitive physics with multimodal generative models},
  author={Rezaei-Shoshtari, Sahand and Hogan, Francois R and Jenkin, Michael and Meger, David and Dudek, Gregory},
  booktitle=aaai,
  pages={6110--6118},
  year={2021},
  pdf={https://ojs.aaai.org/index.php/AAAI/article/download/16761/16568}
}

@inproceedings{hogan2021seeing,
  title={Seeing through your skin: Recognizing objects with a novel visuotactile sensor},
  author={Hogan, Francois R and Jenkin, Michael and Rezaei-Shoshtari, Sahand and Girdhar, Yogesh and Meger, David and Dudek, Gregory},
  booktitle={Proceedings of the IEEE/CVF winter conference on applications of computer vision},
  pages={1218--1227},
  year={2021},
  pdf={https://openaccess.thecvf.com/content/WACV2021/papers/Hogan_Seeing_Through_Your_Skin_Recognizing_Objects_With_a_Novel_Visuotactile_WACV_2021_paper.pdf}
}

@inproceedings{dai2020urban,
  title={Urban night scenery reconstruction by day-night registration and synthesis},
  author={Dai, Andi and Meger, David},
  booktitle={Proceedings of the 28th International Conference on Advances in Geographic Information Systems},
  pages={115--118},
  year={2020},
  pdf={https://dl.acm.org/doi/pdf/10.1145/3397536.3422200}
}

@inproceedings{xu2020pressense,
  title={PresSense: Passive respiration sensing via ambient WiFi signals in noisy environments},
  author={Xu, Yi Tian and Chen, Xi and Liu, Xue and Meger, David and Dudek, Gregory},
  booktitle=iros,
  pages={4032--4039},
  year={2020},
  pdf={https://ieeexplore.ieee.org/iel7/9340668/9340635/09341474.pdf}
}

@inproceedings{rezaei2020learning,
  title={Learning the latent space of robot dynamics for cutting interaction inference},
  author={Rezaei-Shoshtari, Sahand and Meger, David and Sharf, Inna},
  booktitle=iros,
  pages={5627--5632},
  year={2020},
  pdf={https://ieeexplore.ieee.org/document/9341446}
}

@inproceedings{di2020dynamic,
  title={Dynamic planning of redundant robots within a set-based task-priority inverse kinematics framework},
  author={Di Vito, Daniele and Bergeron, Mathieux and Meger, David and Dudek, Gregory and Antonelli, Gianluca},
  booktitle={IEEE Conference on Control Technology and Applications (CCTA)},
  pages={549--554},
  year={2020}
}

@inproceedings{manderson2020learning,
  title={Learning to drive off road on smooth terrain in unstructured environments using an on-board camera and sparse aerial images},
  author={Manderson, Travis and Wapnick, Stefan and Meger, David and Dudek, Gregory},
  booktitle=icra,
  pages={1263--1269},
  year={2020},
  pdf={https://ieeexplore.ieee.org/iel7/9187508/9196508/09196879.pdf}
}

@inproceedings{cheng2020depth,
  title={Depth Prediction for Monocular Direct Visual Odometry.},
  author={Cheng, Ran and Agia, Christopher and Meger, David and Dudek, Gregory},
  booktitle=crv,
  year={2020},
  pdf={https://www.academia.edu/download/84599980/CNN-DVO-CRV-2020.pdf}
}

@inproceedings{fujimoto2020equivalence,
  title={An equivalence between loss functions and non-uniform sampling in experience replay},
  author={Fujimoto, Scott and Meger, David and Precup, Doina},
  booktitle=neurips,
  volume={33},
  pages={14219--14230},
  year={2020},
  pdf={https://proceedings.neurips.cc/paper_files/paper/2020/file/a3bf6e4db673b6449c2f7d13ee6ec9c0-Paper.pdf}
}


@inproceedings{smith20203d,
  title={3d shape reconstruction from vision and touch},
  author={Smith, Edward and Calandra, Roberto and Romero, Adriana and Gkioxari, Georgia and Meger, David and Malik, Jitendra and Drozdzal, Michal},
  booktitle=neurips,
  volume={33},
  pages={14193--14206},
  year={2020},
  pdf={https://proceedings.neurips.cc/paper/2020/file/a3842ed7b3d0fe3ac263bcabd2999790-Paper.pdf}
}


@inproceedings{jimmyli2020icra,
 author = {Jimmy Li and K. Koreitem and D. Meger and G. Dudek},
 booktitle = icra,
 title = {View-Invariant Loop Closure with Oriented Semantic Landmarks},
 year = {2020}
}

@mastersthesis{aakash2019thesis,
 address = {Montreal, Canada.},
 author = {Aakash Nandi},
 school = {School of Computer Science, Center for Intelligent Machines, McGill University},
 title = {Neural Architecture Enforced Symmetry},
 year = {2019},
 pdf = {https://doi.org/10.82308/25946},
 media = {https://www.cim.mcgill.ca/~mrl/content/publications/other/naes_td3.mp4}
}

@inproceedings{yitianCRV2019,
 abstract = {Inspired by ideas in cognitive science, we propose a novel and general approach to solve human motion under-standing via pattern completion on a learned latent representation space. Our model outperforms current state-of-the-art methods in human motion prediction across a number of tasks, with no customization. To construct a latent representation for time-series of various lengths, we propose a  new and generic autoencoder based on sequence-to-sequence learning. While traditional inference strategies find a correlation between an input and an output, we use pattern completion, which views the input as a partial pattern and to predict the best corresponding complete pattern. Our results demonstrate that this approach has advantages when combined with our autoencoder in solving human motion prediction, motion generation and action classification.},
 author = {Yi Tian Xu, Yaqiao Li, David Meger},
 booktitle = crv,
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/yitian_human_motion_pred.pdf},
 title = {Human Motion Prediction via Pattern Completion in Latent Representation Space},
 year = {2019}
}

@inproceedings{jimmyliICRA2019,
 address = {Montreal, Canada},
 author = {Jimmy Li and David Meger and Gregory Dudek},
 booktitle = icra,
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/li2019icra.pdf},
 title = {{Semantic Mapping for View-Invariant Relocalization}},
 year = {2019}
}

@inproceedings{koreitemICRA2019,
 address = {Montreal, Canada},
 author = {Karim Koreitem and Jimmy Li and Ian Karp and Travis Manderson and Gregory Dudek},
 booktitle = icra,
 month = {May},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra19-2.pdf},
 title = {{Underwater Communication Using Full-Body Gestures and Optimal
Variable-Length Prefix Codes}},
 year = {2019}
}

@inproceedings{nips2018edwardsmith,
 abstract={We consider the problem of scaling deep generative shape models to high-resolution.Drawing motivation from the canonical view representation of objects, we introduce a novel method for the fast up-sampling of 3D objects in voxel space through networks that perform super-resolution on the six orthographic depth projections. This allows us to generate high-resolution objects with more efficient scaling than methods which work directly in 3D. We decompose the problem of 2D depth super-resolution into silhouette and depth prediction to capture both structure and fine detail. This allows our method to generate sharp edges more easily than an individual network.  We evaluate our work on multiple experiments concerning high-resolution 3D objects, and show our system is capable of accurately predicting novel objects at resolutions as large as 512×512×512 – the highest resolution reported for this task, to our knowledge. We achieve state-of-the-art performance on 3D object reconstruction from RGB images on the ShapeNet dataset, and further demonstrate the first effective 3D super-resolution method.},
 author = {Smith, Edward and Fujimoto, Scott and Meger, David},
 booktitle = neurips,
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/edward42_multi_view_silhouette.pdf},
 title = {Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation},
 year = {2018}
}

@inproceedings{manjanna2018iser,
 abstract = {We present an anytime adaptive sampling technique that generates paths to efficiently measure and then mathematically model a scalar field by performing non-uniform measurements in a given region of interest. In particular, the class of scalar field we are interested is some physical or virtual parameter that varies with location, such as depth of the sea floor or the probability of finding a lost object. As the measurements are collected at each sampling location, we can compute an estimate of the large-scale variation of the phenomenon of interest. We compute a sampling path that minimizes the expected time to accurately model the phenomenon of interest by visiting high information regions using non-myopic path generation based on reinforcement learning.},
 author = {Manjanna, Sandeep and van Hoof, Herke and Dudek, Gregory},
 booktitle = iser,
 pages = {1--7},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ISER2018.pdf},
 media = {https://www.cim.mcgill.ca/~mrl/content/publications/other/msandeep2018iser.mp4},
 title = {Policy Search on Aggregated State Space for Active Sampling},
 year = {2018}
}

@inproceedings{manjanna2018reinforcement,
 abstract = {Efficient spatial exploration is a key aspect of search and rescue. In this paper, we present a search algorithm that generates efficient trajectories that optimize the rate at which probability mass is covered by a searcher. This should allow an autonomous vehicle find one or more lost targets as rapidly as possible. We do this by performing non-uniform sampling of the search region. The path generated minimizes the expected time to locate the missing target by visiting high probability regions using non-myopic path generation based on reinforcement learning. We model the target probability distribution using a classic mixture of Gaussians model with means and mixture coefficients tuned according to the location and time of sightings of the lost target. Key features of our search algorithm are the ability to employ a very general non-deterministic action model and the ability to generate action plans for any new probability distribution using the parameters learned on other similar looking distributions. One of the key contributions of this paper is the use of non-uniform state aggregation for policy search in the context of robotics. We compare the paths generated by our algorithm with other accepted spatial coverage techniques such as distribution independent boustrophedonic coverage and model dependent spiral search. We present a proof showing that rewarding for clearing probability mass instead of locating the target does not bias the objective function. The experiments show that the learned policy outperforms several well-known baselines even in scenarios different from the one it has been trained on.},
 author = {Manjanna, Sandeep and van Hoof, Herke and Dudek, Gregory},
 booktitle = {IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)},
 organization = {IEEE},
 pages = {1--7},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/SSRR2018.pdf},
 media = {https://www.cim.mcgill.ca/~mrl/content/publications/other/SSRR2018.mp4},
 title = {Reinforcement Learning with Non-uniform State Representations for Adaptive Search},
 year = {2018}
}

@inproceedings{jhansen2018iros,
 abstract={This paper considers a spatial coverage problem in which a network of passive floating sensors is used to collect samples in a body of water. We employ an iterative measurement and modeling scheme to incrementally deploy sensors so as to achieve spatial coverage, despite only controlling the initial sample point. Once deployed, sensors are moved about a survey area by ambient surface currents. We demonstrate our results in simulation on 40 different ocean flow fields and compare against several baselines. This work provides a computational tool for scientists seeking a low-cost, autonomous marine surveying system. Although in this paper, we concentrate on ocean drifters, our approach can be extended to other domains where a spatial distribution of passive nodes in a flow field can be modeled.},
 author = {Hansen, Johanna and Dudek, Gregory},
 booktitle = {Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
 doi = {10.1109/IROS.2018.8594281},
 location = {Madrid, Spain},
 month = {10},
 pages = {1906-1912},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/IROS2018.pdf},
 media = {https://www.cim.mcgill.ca/~mrl/content/publications/other/jhansen2018iros.gif},
 title = {Coverage Optimization with Non-Actuated, Floating Mobile Sensors using Iterative Trajectory Planning in Marine Flow Fields},
 year = {2018}
}

@inproceedings{holliday2018,
 abstract={Visual localization under large changes in scale is an important capability in many robotic mapping applications, such as localizing at low altitudes in maps built at high altitudes, or performing loop closure over long distances. Existing approaches, however, are robust only up to about a 3x difference in scale between map and query images. We propose a novel combination of deep-learning-based object features and state-of-the-art SIFT point-features that yields improved robustness to scale change. This technique is training-free and class-agnostic, and in principle can be deployed in any environment out-of-the-box. We evaluate the proposed technique on the KITTI Odometry benchmark and on a novel dataset of outdoor images exhibiting changes in visual scale of 7x and greater, which we have released to the public. Our technique consistently out performs localization using either SIFT features or the proposed object features alone, achieving both greater accuracy and much lower failure rates under large changes in scale.},
 author = {Andrew Holliday and Gregory Dudek},
 booktitle = {Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
 location = {Madrid, Spain},
 organization = {IEEE},
 pages = {1688-1694},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros_2018.pdf},
 title = {Scale-Robust Localization Using General Object Landmarks},
 year = {2018}
}

@inproceedings{pmlr-v80-fujimoto18a,
 abstract = {In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and the critic. Our algorithm builds on Double Q-learning, by taking the minimum value between a pair of critics to limit overestimation. We draw the connection between target networks and overestimation bias, and suggest delaying policy updates to reduce per-update error and further improve performance. We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested.},
 address = {Stockholmsmässan, Stockholm Sweden},
 author = {Fujimoto, Scott and van Hoof, Herke and Meger, David},
 booktitle = {Proceedings of the 35th International Conference on Machine Learning},
 editor = {Dy, Jennifer and Krause, Andreas},
 month = {10--15 Jul},
 pages = {1587--1596},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/fujimoto18a.pdf},
 publisher = {PMLR},
 series = {Proceedings of Machine Learning Research},
 title = {Addressing Function Approximation Error in Actor-Critic Methods},
 url = {http://proceedings.mlr.press/v80/fujimoto18a.html},
 volume = {80},
 year = {2018}
}

@article{hansen2018planning,
 author = {Hansen, Johanna and Kastner, Kyle and Courville, Aaron and Dudek, Gregory},
 code = {https://github.com/johannah/trajectories},
 journal = {Workshop on Prediction and Generative Modeling in Reinforcement Learning},
 location = {Stockholm, Sweden},
 month = {July},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/PGMRL2018.pdf},
 poster = {https://www.cim.mcgill.ca/~mrl/content/publications/other/PGMRL2018_poster.pdf},
 title = {Planning in Dynamic Environments with Conditional Autoregressive Models},
 year = {2018}
}

@inproceedings{hansen2018autonomous,
 author = {Hansen, Johanna and Manjanna, Sandeep and Li, Alberto Quattrini and Rekleitis, Ioannis and Dudek, Gregory},
 booktitle = {OCEANS 2018 MTS/IEEE Charleston},
 organization = {IEEE},
 pages = {1--7},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/OCEANS2018.pdf},
 poster = {https://www.cim.mcgill.ca/~mrl/content/publications/other/OCEANS2018_poster.pdf},
 title = {Autonomous Marine Sampling Enhanced by Strategically Deployed Drifters in Marine Flow Fields},
 year = {2018}
}

@inproceedings{koreitem_oceans2018,
 address = {Charleston, SC, USA},
 author = {Koreitem, Karim and Li, Jimmy and Karp, Ian and Manderson, Travis and Shkurti, Florian and Dudek, Gregory},
 booktitle = {MTS/IEEE OCEANS},
 month = {Oct.},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Koreitem_Oceans2018-Synthetically_Trained_3D_Visual_Tracker_of_Underwater_Vehicles.pdf},
 title = {Synthetically Trained 3D Visual Tracker of Underwater Vehicles},
 year = {2018}
}

@inproceedings{Manderson2018_oceans,
 address = {Charleston, SC, USA},
 author = {T. Manderson and G. Dudek},
 booktitle = {MTS/IEEE OCEANS},
 month = {Oct.},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/travis_manderson_oceans2018.pdf},
 title = {GPU-Assisted Learning on an Autonomous Marine Robot for Vision Based Navigation and Image Understanding},
 year = {2018}
}

@inproceedings{Manderson2018iser,
 author = {Manderson, Travis and Cheng, Ran and Meger, Dave and Dudek, Gregory},
 booktitle = {International Symposium on Experimental Robotics (ISER)},
 title = {Navigation in the Service of Enhanced Pose Estimation},
 pdf = {https://doi.org/10.1007/978-3-030-33950-0_59},
 year = {2018}
}

@unpublished{Manderson2018,
 abstract = {We address the problem of learning vision-based, collision-avoidance and target selection controllers in 3D, specifically in underwater environments densely populated with coral reefs. Using a highly maneuverable, dynamic, six-legged vehicle to swim underwater, we can exploit real-time visual feedback to make close-range navigation decisions that would be hard to achieve with other sensors. Our approach uses computer vision as the sole mechanism for both collision avoidance and the selection of the targets to observe. In particular, we seek to swim close to the reef to make observations yet avoid collisions and barren, coral-deprived regions. To do this simultaneous navigation task of collision avoidance and path selection, we use monocular image data processed in real-time. The proposed system is based on a convolutional neural network that takes as input an image from a forward-facing camera and predicts unscaled and relative heading changes. The network is trained to encode our desired obstacle avoidance and reef exploration objectives via supervised learning from human-labeled data.The predictions from the network are transformed into absolute heading changes via a combination of a temporally-smoothed proportional controller for heading targets and a low-level motor controller. This system enables safe autonomous coral reef navigation in underwater environments. We validate our approach using an untethered, fully autonomous robot swimming within a coral reef in the open ocean. We demonstrate that the robot successfully traversed 1000m of the ocean bottom doing collision-free navigation, while collecting close-up footage of coral reefs.},
 author = {Manderson, Travis and {Gamboa Higuera}, {Juan Camilo} and Cheng, Ran and Dudek, Gregory},
 booktitle = {Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/travis_manderson_iros2018.pdf},
 media = {https://www.youtube.com/embed/jjkP0SYiF7M},
 title = {Vision-based Autonomous Underwater Swimming in Dense Coral for Combined Collision Avoidance and Target Selection},
 year = {2018}
}

@inproceedings{travisCRV2018,
 author = {Travis Manderson and Andrew Holliday and Gregory Dudek},
 booktitle = {Proceedings of the Conference on Computer and Robot Vision (CRV)},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/travis_manderson_crv2018.pdf},
 title = {Gaze Selection for Enhanced Visual Odometry During Navigation},
 year = {2018}
}

@inproceedings{higuera2018synthesizing,
 author = {Gamboa Higuera, Juan Camilo and Meger, David and Dudek, Gregory},
 booktitle = {2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
 organization = {IEEE},
 pages = {2538--2544},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/1803.02291.pdf},
 title = {Synthesizing Neural Network Controllers with Probabilistic Model-Based Reinforcement Learning},
 url = {https://arxiv.org/abs/1803.02291},
 year = {2018}
}

@inproceedings{wide_baseline_2018,
 address = {Toronto, Canada},
 author = {Jimmy Li and Zhaoqi Xu and David Meger and Gregory Dudek},
 booktitle = {Conference on Computer and Robot Vision (CRV)},
 month = {May},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/crv18.pdf},
 title = {Semantic Scene Models for Visual Localization Under Large Viewpoint Changes},
 year = {2018}
}

@inproceedings{florianICRA2018,
 abstract = {In this paper we address the integrated prediction, planning, and control problem that enables a single follower robot (the "photographer") to quickly re-establish visual contact with a moving target (the "subject") that has escaped the follower’s field of view. Our work addresses this unavoidable scenario, which reactive controllers are typically ill-equipped to handle, by making intelligent predictions about the long and short-term behavior of the target, and planning pursuit paths that will maximize the chance of seeing the target again. At the core of our pursuit method is the use of predictive models of target behavior, which help narrow down the possible future locations of the target to a few discrete hypotheses, as well as the use of combinatorial search in physical space to check those hypotheses efficiently. We model target behavior in terms of a learned navigation reward function, using Inverse Reinforcement Learning, based on semantic terrain features of satellite maps. Our pursuit algorithm continuously predicts the latent destination of the target, and its position in the future, and relies on efficient graph representation and search methods in order to navigate to location hypotheses at which the target is most likely to be seen at an anticipated time. We perform extensive evaluation of our predictive pursuit algorithm over multiple satellite maps, under thousands of simulation scenarios, against state-of-the art MDP and POMDP solvers, and we show that our method significantly outperforms them by exploiting domain-specific knowledge, while being able to run in real-time.},
 address = {Brisbane, Australia},
 author = {Shkurti, Florian and Kakodkar, Nikhil and Dudek, Gregory},
 booktitle = icra,
 month = {May},
 pages = {7804-7811},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/pursuit-icra2018.pdf},
 media = {https://www.cim.mcgill.ca/~mrl/content/publications/other/pursuit_via_irl.mp4},
 title = {{Model-Based Probabilistic Pursuit via Inverse Reinforcement Learning}},
 year = {2018}
}

@inproceedings{sandeepICRA2018,
 abstract = {Physical sampling of water for off-site analysis is necessary for many applications like monitoring the quality of drinking water in reservoirs, understanding marine ecosystems, and measuring contamination levels in fresh-water systems. Persistent collection of physical water samples can be improved by automation. Robotic sampling makes it possible to strategically collect water samples based on real-time measurements of physical and chemical properties gathered with onboard sensors. In this paper, we present a multi-robot, data-driven, watersampling strategy, where autonomous surface vehicles plan and execute water sampling using the chlorophyll density as a cue for plankton-rich water samples. We use two Autonomous Surface Vehicles, one equipped with a water quality sensor and the other equipped with a physical water-sampling apparatus. The robot with sensors acts as an explorer, measuring and building a spatial map of chlorophyll density in the given region of interest. The robot equipped with water sampling apparatus makes decisions in real-time on where to sample the water, based on the suggestions made by the explorer robot. We evaluate our system in simulation and on real robots in an actual drinking-water reservoir, showing the effectiveness of the proposed system.},
 address = {Brisbane, Australia},
 author = {Sandeep Manjanna and Alberto Quattrini Li and Ryan N. Smith and Ioannis Rekleitis and Gregory Dudek},
 booktitle = icra,
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ICRA2018.pdf},
 media = {https://www.cim.mcgill.ca/~mrl/content/publications/other/sandeepICRA2018.mp4},
 title = {{Heterogeneous Multirobot System for Exploration and Strategic Water Sampling}},
 year = {2018}
}

@inproceedings{sandeep_data_driven_sampling_iros2017,
 abstract = {This paper addresses adaptive coverage of a spatial field without prior knowledge. Our application in this paper is to cover a region of the sea surface using a robotic boat, although the algorithmic approach has wider applicability. We propose an anytime planning technique for efficient data gathering using point-sampling based on non-uniform data-driven coverage. Our goal is to sense a particular region of interest in the environment and be able to reconstruct the measured spatial field. Since there are autonomous agents involved, there is a need to consider the costs involved in terms of energy consumed and time required to finish the task. An ideal map of the scalar field requires complete coverage of the region, but can be approximated by a good sparse coverage strategy along with an efficient interpolation technique. We propose to optimize the trade off between the environmental field mapping and the costs (energy consumed, time spent, and distance traveled) associated with sensing. We present an anytime algorithm for sampling the environment adaptively by following a multi-scale path to produce a variable resolution map of the spatial field. We compare our approach to a traditional exhaustive survey approach and show that we are able to effectively represent a spatial field spending minimum energy. We present results that indicate our sampling technique gathering most informative samples with least travel. We validate our approach through simulations and test the system on real robots in the open ocean.},
 address = {Vancouver, Canada},
 author = {Sandeep Manjanna and Gregory Dudek},
 booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
 month = {September},
 pages = {6111--6117},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/IROS2017.pdf},
 media = {https://www.cim.mcgill.ca/~mrl/content/publications/other/sandeepIROS2017.mp4},
 title = {Data-driven selective sampling for marine vehicles using multi-scale paths},
 year = {2017}
}

@inproceedings{context_relevance_iros_2017,
 address = {Vancouver, Canada},
 author = {Jimmy Li, David Meger and Gregory Dudek},
 booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
 month = {September},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros2017_context_relevance.pdf},
 title = {Context-Coherent Scenes of Objects for Camera Pose Estimation},
 year = {2017}
}

@article{stonge2017ras,
 author = {St-Onge, David and Brèches, Pierre-Yves and Sharf, Inna and Reeves, N. and Rekleitis, Ioannis and Abouzakhm, Patrick and Girdhar, Yogesh and Harmat, Adam and Dudek, Gregory and Giguère, Philippe},
 doi = {10.1016/j.robot.2016.10.013},
 journal = {Robotics and Autonomous Systems},
 month = {10},
 pages = {},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/St_Onge_D_2017_18901.pdf},
 title = {Control, localization and human interaction with an autonomous lighter-than-air performer},
 volume = {88},
 year = {2016}
}

@article{Sattar2018,
 abstract = {We present an algorithm for underwater robots to visually detect and track human motion. Our objective is to enable human--robot interaction by allowing a robot to follow behind a human moving in (up to) six degrees of freedom. In particular, we have developed a system to allow a robot to detect, track and follow a scuba diver by using frequency-domain detection of biological motion patterns. The motion of biological entities is characterized by combinations of periodic motions which are inherently distinctive. This is especially true of human swimmers. By using the frequency-space response of spatial signals over a number of video frames, we attempt to identify signatures pertaining to biological motion. This technique is applied to track scuba divers in underwater domains, typically with the robot swimming behind the diver. The algorithm is able to detect a range of motions, which includes motion directly away from or towards the camera. Once detected, the motion of the diver relative to the vehicle is then tracked using an Unscented Kalman Filter, an approach for non-linear estimation. The efficiency of our approach makes it attractive for real-time applications on-board our underwater vehicle, and in future applications we intend to track scuba divers in real-time with the robot. The paper presents an algorithmic overview of our approach, together with experimental evaluation based on underwater video footage.},
 author = {Sattar, Junaed
and Dudek, Gregory},
 day = {01},
 doi = {10.1007/s10514-017-9644-y},
 issn = {1573-7527},
 journal = {Autonomous Robots},
 month = {Jan},
 number = {1},
 pages = {111--124},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Sattar-Dudek2018_Article_VisualIdentificationOfBiologic.pdf},
 title = {Visual identification of biological motion for underwater human--robot interaction},
 url = {https://doi.org/10.1007/s10514-017-9644-y},
 volume = {42},
 year = {2018}
}

@article{manderson2017jfr,
 author = {Manderson, Travis and Li, Jimmy and Dudek, Natasha and Meger, David and Dudek, Gregory},
 doi = {10.1002/rob.21698},
 issn = {1556-4967},
 journal = {Journal of Field Robotics},
 number = {1},
 pages = {170--187},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Robotic_Coral_Reef_Health_Assessment_Using_Automated.pdf},
 title = {Robotic Coral Reef Health Assessment Using Automated Image Analysis},
 url = {http://dx.doi.org/10.1002/rob.21698},
 volume = {34},
 year = {2017}
}

@inproceedings{convoying_iros_2017,
 abstract = {We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments. Our method is based on the idea of tracking-by-detection, which interleaves efficient model-based object detection with temporal filtering of image-based bounding box estimation. This approach has the important advantage of mitigating tracking drift (i.e. drifting away from the target object), which is a common symptom of model-free trackers and is detrimental to sustained convoying in practice. To illustrate our solution, we collected extensive footage of an underwater robot in ocean settings, and hand-annotated its location in each frame. Based on this dataset, we present an empirical comparison of multiple tracker variants, including the use of several convolutional neural networks, both with and without recurrent connections, as well as frequency-based model-free trackers. We also demonstrate the practicality of this tracking-by-detection strategy in real-world scenarios by successfully controlling a legged underwater robot in five degrees of freedom to follow another robot’s independent motion.},
 address = {Vancouver, Canada},
 author = {Shkurti, Florian and Chang, {Wei Di} and Henderson, Peter and Islam, {Md. Jahidul} and {Gamboa Higuera}, {Juan Camilo} and Li, Jimmy and Manderson, Travis and Xu, Anqi and Dudek, Gregory and Sattar, Junaed},
 booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
 code = {https://github.com/Breakend/TemporalYolo},
 dataset = {https://github.com/Breakend/AquaBoxDataset},
 month = {September},
 media = {https://www.cim.mcgill.ca/~mrl/content/publications/other/AquaRobotConvoying.mp4},
 pages = {4189--4196},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/visual_convoying.pdf},
 projectpage = {http://www.cim.mcgill.ca/~mrl/robot_tracking},
 title = {Underwater Multi-Robot Convoying Using Visual Tracking by Detection},
 year = {2017}
}

@inproceedings{topological_pursuit_iros_2017,
 abstract = {We address the integrated planning and control problem that enables a single follower robot (the “photographer”) to maintain a moving target (the “subject”) in its field of view for as long as possible. We propose a real-time pursuit algorithm that seamlessly handles the often neglected, yet unavoidable, scenario in which the target escapes the follower’s field of view; a scenario that simple, reactive controllers are ill-equipped to handle. Our algorithm aims to minimize the expected time until visual contact is re-established, which enables the photographer to track the subject for as long as possible, even in the presence of loss of visibility. At the core of our pursuit algorithm is an efficient method for sampling plausible trajectories from different homotopy classes. We do this by generating topologically distinct shortest paths by using the Voronoi diagram. We use these paths to make informed, model-based predictions of the likely future locations of the target, given a history of observations. Given these predictions, our algorithm produces pursuit trajectories that approximately minimize the expected time to recover visual contact. We show that constraining the predictive pursuit problem to the space of homotopy classes condenses the expanse of possibilities that our algorithm must consider, which enables target tracking in large occupancy grids, as opposed to many POMDP methods that are constrained to small environments. We benchmark the tracking behavior of our algorithm against the baseline of human subjects who performed the same set of pursuit tasks in simulation, as well as against two other pursuit algorithms that only take into account paths from a single homotopy class. We show that considering homotopy alternatives in 2D pursuit improves the tracking performance and that our algorithm does at least as well as humans in most pursuit scenarios.},
 address = {Vancouver, Canada},
 author = {Florian Shkurti and Gregory Dudek},
 booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
 month = {September},
 media = {https://www.cim.mcgill.ca/~mrl/content/publications/other/topological_pursuit.mp4},
 pages = {5653--5660},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/topological_pursuit.pdf},
 projectpage = {http://www.cim.mcgill.ca/~mrl/topological_pursuit},
 title = {Topologically distinct trajectory predictions for probabilistic pursuit},
 year = {2017}
}

@inproceedings{8232370,
 author = {A. {Kalmbach} and H. M. {Sosik} and G. {Dudek} and Y. {Girdhar}},
 booktitle = {OCEANS 2017 - Anchorage},
 number = {},
 pages = {1-7},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/2017_oceans_MVCOplankton.pdf},
 poster = {https://www.cim.mcgill.ca/~mrl/content/publications/other/2017_oceans_MVCOplankton_poster.pdf},
 title = {Learning seasonal phytoplankton communities with topic models},
 volume = {},
 year = {2017}
}

@inproceedings{henderson2017icmlworkshop,
 address = {Sydney, Australia},
 author = {Peter Henderson and Wei-Di Chang and Florian Shkurti and Johanna Hansen and David Meger and Gregory Dudek},
 booktitle = {International Conference on Machine Learning (ICML '17), Workshop on Lifelong Learning},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/1708.04352.pdf},
 title = {Benchmark Environments for Multitask Learning in Continuous Domains},
 year = {2017}
}

@inproceedings{Gamboa2017,
 address = {Singapore},
 author = {Gamboa Higuera, Juan Camilo and Meger, David and Dudek, Gregory},
 booktitle = icra,
 pdf = {https://www.cim.mcgill.ca/~mrl/pubs/dmeger/Adapting_learned_robotics_behaviours_through_policy_adjustment.pdf},
 title = {{Adapting Learned Robotics Behaviours through Policy Adjustments}},
 url = {https://ieeexplore.ieee.org/abstract/document/7989686},
 year = {2017}
}

@inproceedings{Kalmbach_icra17,
 author = {Arnold Kalmbach and
Yogesh A. Girdhar and
Heidi M. Sosik and
Gregory Dudek},
 booktitle = {2017 {IEEE} International Conference on Robotics and Automation, {ICRA}
2017, Singapore, Singapore, May 29 - June 3, 2017},
 doi = {10.1109/ICRA.2017.7989568},
 pages = {4906--4913},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/2017_ICRA_planktonhotspots.pdf},
 timestamp = {Wed, 26 Jul 2017 15:17:30 +0200},
 title = {Phytoplankton hotspot prediction with an unsupervised spatial community
model},
 url = {https://doi.org/10.1109/ICRA.2017.7989568},
 year = {2017}
}

@inproceedings{sandeep_crv_2017,
 author = {Sandeep Manjanna and Johanna Hansen and Alberto Quattrini Li and Ioannis Rekleitis and Gregory Dudek},
 booktitle = {CRV '17: Proceedings of the 2017 International Conference on Computer and Robot Vision},
 month = {May},
 pages = {},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/collaborative_sampling_crv2017.pdf},
 publisher = {IEEE Computer Society},
 title = {Collaborative Sampling Using Heterogeneous Marine Robots Driven by Visual Cues},
 year = {2017}
}

@inproceedings{malika2016ssrr,
 address = {Lausanne, Switzerland},
 author = {Malika Meghjani and Sandeep Manjanna and Gregory Dudek},
 booktitle = {Proc. of IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR)},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/SSRR2016.pdf},
 title = {Multi-Target Search Strategies},
 year = {2016}
}

@inproceedings{malika2016iros-multi-target-rendezvous-search,
 address = {Daejeon, Korea},
 author = {Malika Meghjani and Sandeep Manjanna and Gregory Dudek},
 booktitle = {Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/IROS2016_1.pdf},
 title = {Multi-Target Rendezvous Search},
 year = {2016}
}

@inproceedings{malika2016iros-rendezvous-in-street-networks,
 address = {Daejeon, Korea},
 author = {Malika Meghjani and Sandeep Manjanna and Gregory Dudek},
 booktitle = {Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/IROS2016_2.pdf},
 title = {Fast and Efficient Rendezvous in Street Networks},
 year = {2016}
}

@inproceedings{Xu16TACtiC,
 abstract = {In this work, we grant robot agents the capacity to sense and react to their human supervisor.s changing trust state, as a means to maintain the efficiency of their collaboration. We propose the novel formulation of Trust-Aware Conservative Control (TACtiC), in which the agent alters its behaviors momentarily whenever the human loses trust. This trust-seeking robot framework builds upon an online trust inference engine and also incorporates an interactive behavior adaptation technique. We present end-to-end instantiations of trust-seeking robots for distinct task domains of aerial terrain coverage and interactive autonomous driving. Empirical assessments comprise a large-scale controlled interaction study and its extension into field evaluations with an autonomous car. These assessments substantiate the efficiency gains that trustseeking agents bring to asymmetric human-robot teams. },
 author = {Anqi Xu and Gregory Dudek},
 booktitle = {the {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS}'16)},
 pages = {3312--3319},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros2016_tactic.pdf},
 title = {Maintaining Efficient Collaboration with Trust-Seeking Robots},
 year = {2016}
}

@inproceedings{jimmyliICRA2016,
 address = {Stockholm, Sweden},
 author = {Jimmy Li and David Meger and Gregory Dudek},
 booktitle = {Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA '16)},
 month = {May},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ICRA16_Spatial_Rel.pdf},
 title = {{Learning to Generalize 3D Spatial Relationships}},
 year = {2016}
}

@inbook{Manderson2016,
 abstract = {This paper addresses the automated analysis of coral in shallow reef environments up to 90 ft deep. During a series of robotic ocean deployments, we have collected a data set of coral and non-coral imagery from four distinct reef locations. The data has been annotated by an experienced biologist and presented as a representative challenge for visual understanding techniques. We describe baseline techniques using texture and color features combined with classifiers for two vision sub-tasks: live coral image classification and live coral semantic segmentation. The results of these methods demonstrate both the feasibility of the task as well as the remaining challenges that must be addressed through the development of more sophisticated techniques in the future.},
 address = {Cham},
 author = {Manderson, Travis and Li, Jimmy and Cort{\'e}s Poza, David and Dudek, Natasha and Meger, David and Dudek, Gregory},
 booktitle = {Field and Service Robotics: Results of the 10th International Conference},
 doi = {10.1007/978-3-319-27702-8_7},
 editor = {Wettergreen, David S. and Barfoot, Timothy D.},
 isbn = {978-3-319-27702-8},
 pages = {95--108},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/fsr16.pdf},
 publisher = {Springer International Publishing},
 title = {Towards Autonomous Robotic Coral Reef Health Assessment},
 url = {https://doi.org/10.1007/978-3-319-27702-8_7},
 year = {2016}
}

@inproceedings{sandeepFMRS2016,
 abstract = {In this paper we propose a reward-driven finite-horizon model akin to a Markov Decision Process to extract the maximum amount of valuable data in least amount of time. We present a path planning algorithm that generates off-line trajectories for multiple robots to cover a region of interest by visiting the hot-spots in increasing order of their significance. An underlying distribution is assumed to assist the algorithm with recognizing the hot-spots. The trajectories generated are both time and energy efficient. We validate our technique through several simulated experiments. Although this technique can be used in any environmental domain (Air, Water or Land), in this paper we demonstrate the success of our technique using a real robot in surveying coral reefs in the presence of real world conditions.},
 address = {Stockholm, Sweden},
 author = {Sandeep Manjanna, Nikhil Kakodkar, and Gregory Dudek},
 booktitle = {ICRA 2016 Workshop on Fielded Multi-robot Systems Operating on Land, Sea, and Air},
 month = {May},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ICRA2016.pdf},
 title = {{Multi-Robot Path Planning for Selective Coverage}},
 year = {2016}
}

@inproceedings{Koreitem2016,
 author = {Koreitem, Karim and Girdhar, Yogesh and Cho, Walter and Singh, Hanumant and Pineda, Jesus and Dudek, Gregory},
 authored = {true},
 bibtype = {inProceedings},
 booktitle = {2016 13th Conference on Computer and Robot Vision (CRV)},
 citation_key = {Koreitem2016},
 confirmed = {true},
 created = {2016-04-11T02:28:01.000Z},
 file_attached = {true},
 folder_uuids = {9fe02bf3-6f9f-4e86-a6e6-c0190f2a6db9},
 hidden = {false},
 id = {129e0e43-71d7-388a-b462-cfe2615ee4ce},
 identifiers = {[object Object]},
 last_modified = {2018-01-26T05:04:15.918Z},
 month = {6},
 pages = {101-108},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Koreitem_CRV2016-Subsea_Fauna_Enumeration_Using_Vision-Based_Marine_Robots.pdf},
 private_publication = {false},
 profile_id = {2331788d-b144-3e67-ab8c-4abd7ab569c5},
 publisher = {IEEE},
 read = {false},
 starred = {false},
 title = {Subsea Fauna Enumeration Using Vision-Based Marine Robots},
 type = {inProceedings},
 websites = {http://ieeexplore.ieee.org/document/7801509/},
 year = {2016}
}

@inproceedings{manderson_crv_2016,
 author = {Manderson, Travis and Shkurti, Florian and Dudek, Gregory},
 booktitle = {Conference on Computer and Robot Vision (CRV)},
 month = {May},
 pages = {465--463},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/crv_2016_texture_aware_slam.pdf},
 publisher = {IEEE Computer Society},
 title = {Texture-Aware SLAM Using Stereo Imagery And Inertial Information},
 year = {2016}
}

@inproceedings{sandeep_crv_2016,
 abstract = {In this paper we present an efficient method for visual mapping of open water environments using exploration and reward identification followed by selective visual coverage. In particular, we consider the problem of visual mapping a shallow water coral reef to provide an environmental assay. Our approach has two stages based on two classes of sensors: bathymetric mapping and visual mapping. We use a robotic boat to collect bathymetric data using a sonar sensor for the first stage and video data using a visual sensor for the second stage. Since underwater environments have varying visibility, we use the sonar map to select regions of potential value, and efficiently construct the bathymetric map from sparse data using a Gaussian Process model. In the second stage, we collect visual data only where there is good potential pay-off, and we use a reward-driven finite-horizon model akin to a Markov Decision Process to extract the maximum amount of valuable data in the least amount of time. We show that a very small number of sonar readings suffice on a typical fringing reef. We validate and demonstrate our surveying technique using real robot in the presence of real world conditions such as wind and current. We also show that our proposed approach is suitable for visual surveying by presenting a visual collage of the reef.},
 author = {Sandeep Manjanna and Nikhil Kakodkar and Malika Meghjani and and Gregory Dudek},
 booktitle = {CRV '16: Proceedings of the 2016 International Conference on Computer and Robot Vision},
 month = {June},
 pages = {},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/CRV2016.pdf},
 publisher = {IEEE Computer Society},
 title = {Efficient Terrain Driven Coral Coverage Using Gaussian Processes for Mosaic Synthesis},
 year = {2016}
}

@inproceedings{Kalmbach2016,
 author = {Kalmbach, Arnold and Hoeberechts, Maia and Albu, Alexandra Branzan and Glotin, Herve and Paris, Sebastien and Girdhar, Yogesh},
 booktitle = {IEEE Winter Conference on Applications of Computer Vision},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/substratetopics_wacv2016.pdf},
 title = {Learning Deep-Sea Substrate Types With Visual Topic Models},
 year = {2016}
}

@inproceedings{qiwen2015crv,
 address = {Halifax, Canada},
 author = {Qiwen Zhang and Ioannis Rekleitis and Gregory Dudek},
 booktitle = {Proc. of Conference on Computer and Robot Vision},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/crv2015_heuristic.pdf},
 title = {Uncertainty Reduction via Heuristic Search Planning on Hybrid Metric/Topological Map},
 year = {2015}
}

@article{giridhar2015corr-gibbs-sampling,
 archiveprefix = {arXiv},
 author = {Yogesh A. Girdhar and
Gregory Dudek},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/journals/corr/GirdharD15.bib},
 eprint = {1509.03242},
 journal = {CoRR},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/1509.03242v1.pdf},
 timestamp = {Mon, 13 Aug 2018 16:47:39 +0200},
 title = {Gibbs Sampling Strategies for Semantic Perception of Streaming Video
Data},
 url = {http://arxiv.org/abs/1509.03242},
 volume = {abs/1509.03242},
 year = {2015}
}

@article{giridhar2015corr-modeling-curiosity,
 archiveprefix = {arXiv},
 author = {Yogesh A. Girdhar and
Gregory Dudek},
 bibsource = {dblp computer science bibliography, https://dblp.org},
 biburl = {https://dblp.org/rec/journals/corr/GirdharD15a.bib},
 eprint = {1509.07975},
 journal = {CoRR},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/1509.07975.pdf},
 timestamp = {Mon, 13 Aug 2018 16:46:25 +0200},
 title = {Modeling Curiosity in a Mobile Robot for Long-Term Autonomous Exploration
and Monitoring},
 url = {http://arxiv.org/abs/1509.07975},
 volume = {abs/1509.07975},
 year = {2015}
}

@inproceedings{malika2015icra,
 address = {Seattle, Washington},
 author = {Malika Meghjani and Gregory Dudek},
 booktitle = {ICRA Workshop on Persistent Autonomy for Aquatic Robotics},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ICRA2015.pdf},
 title = {Search for a Rendezvous with Lost Target at Sea},
 year = {2015}
}

@inproceedings{sandeepICRA2015,
 abstract = {In this paper, we investigate the question of how a legged robot can walk efficiently by taking advantage of its ability to alter its gait as a function of statistical (large-scale) terrain properties. One of the contributions of this paper is the algorithm to achieve real-time terrain identification and autonomous gait adaptation on a legged robot. We approach this problem by first classifying the terrains based on their proprioceptive responses and identifying the terrain in realtime. Then we choose an optimal gait to best suit the identified terrain type. We exploit our recent findings regarding gaits, estimated from terrain-contact signatures, in order to obtain an optimized mapping between terrain signatures and terrainspecific gaits. We evaluate our algorithm on synthetic data, and real robot data collected on different terrains and naturally occurring terrain transitions. Another key contribution of this work is the statistical verification that precise gait selection can lead to energy savings in practice in legged robots. This assessment of energy efficiency, achieved by gait adaptation, is among the firsts of its kind in gait adaptation literature. We also present an analysis of the effect of terrain transition frequency on our gait adaptation algorithm. Our results are supported by validation using both synthetic data and field testing.},
 address = {Seattle, USA},
 author = {Sandeep Manjanna and Gregory Dudek},
 booktitle = {Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA '15)},
 month = {May},
 pages = {5155 - 5162},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ICRA2015_1.pdf},
 title = {{Autonomous Gait Selection for Energy Efficient Walking}},
 year = {2015}
}

@inproceedings{ICRA15AquaGaitLearning,
 abstract = {We present an end-to-end framework for realizing fully automated gait learning for a complex underwater legged robot. Using this framework, we demonstrate that a hexapod flipper-propelled robot can learn task-specific control policies We present an end-to-end framework for realizing fully automated gait learning for a complex underwater legged robot. Using this framework, we demonstrate that a hexapod flipper-propelled robot can learn task-specific control policies purely from experience data. Our method couples a state-of-theart policy search technique with a family of periodic low-level controls that are well suited for underwater propulsion. We demonstrate the practical efficacy of tabula rasa learning, that is, learning without the use of any prior knowledge, of policies for a six-legged swimmer to carry out a variety of acrobatic maneuvers in three dimensional space. We also demonstrate informed learning that relies on simulated experience from a realistic simulator. In numerous cases, novel emergent gait behaviors have arisen from learning, such as the use of one stationary flipper to create drag while another oscillates to create thrust. Similar effective results have been demonstrated in under-actuated configurations, where as few as two flippers are used to maneuver the robot to a desired pose, or through an acrobatic motion such as a corkscrew. The success of our learning framework is assessed both in simulation and in the field using an underwater swimming robot.},
 address = {Seattle, US},
 author = {David Meger and Juan Camilo Gamboa Higuera and Anqi Xu and Philippe Giguere and Gregory Dudek},
 booktitle = {Proceedings of the 2015 IEEE International Conference on Robotics and Automation ({ICRA} '15)},
 media = {https://www.youtube.com/embed/VHmEdRXSi9M},
 month = {May},
 pages = {2332--2338},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra2015_gait_learning.pdf},
 projectpage = {http://www.cim.mcgill.ca/~dmeger/ICRA2015_GaitLearning/},
 title = {Learning Legged Swimming Gaits from Experience},
 year = {2015}
}

@inproceedings{Xu15TrustSeekingRobots,
 abstract = {We are interested in asymmetric human-robot teams, where a human supervisor occasionally takes over control to aid an autonomous robot in a given task. Our research aims to optimize team efficiency by improving the robot’s task performance, decreasing the human’s workload, and building trust in the team. We envision synergistic collaborations where the robot adapts its behaviors dynamically to optimize efficacy, reduce manual interventions, and actively seek for greater trust. We describe recent works that study two facets of this trust-seeking adaptive methodology: modeling human-robot trust dynamics, and developing interactive behavior adaptation techniques. We also highlight ongoing efforts to combine these works, which will enable future human-robot teams to be maximally trusting and efficient. },
 address = {Portland, USA},
 author = {Anqi Xu and Gregory Dudek},
 booktitle = {the 10th ACM/IEEE International Conference on Human-Robot Interaction Extended Abstract -- the 10th Annual Human-Robot Interaction Pioneers Workshop (HRI Pioneers '15)},
 month = {March},
 pages = {221--222},
 title = {Towards Efficient Collaborations with Trust-Seeking Adaptive Robots},
 year = {2015}
}

@inproceedings{Xu15OPTIMo,
 abstract = {We present OPTIMo: an Online Probabilistic Trust Inference Model for quantifying the degree of trust that a human supervisor has in an autonomous robot “worker”. Represented as a Dynamic Bayesian Network (DBN), OPTIMo infers beliefs over the human’s moment-to-moment latent trust states, based on the history of observed interaction experiences. A separate model instance is trained on each user’s experiences, leading to an interpretable and personalized characterization of that operator’s behaviors and attitudes. Using datasets collected from an interaction study with a large group of roboticists, we empirically assess OPTIMo’s performance under a broad range of configurations. These evaluation results highlight OPTIMo’s advances in both prediction accuracy and responsiveness over several existing trust models. This accurate and near real-time human-robot trust measure makes possible the development of autonomous robots that can adapt their behaviors dynamically, to actively seek greater trust and greater efficiency within future human-robot collaborations. },
 address = {Portland, USA},
 author = {Anqi Xu and Gregory Dudek},
 booktitle = {the 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI '15)},
 month = {March},
 pages = {221--228},
 title = {{OPTIMo}: Online Probabilistic Trust Inference Model for Asymmetric Human-Robot Collaborations},
 year = {2015}
}

@inproceedings{iros2014_megerShkurtiCortesPozaGiguereDudek,
 author = {David Meger and Florian Shkurti and David Cort\'{e}s Poza and Philippe Gigu\`{e}re and Gregory Dudek},
 booktitle = {Proceedings of the IEEE International Conference on Robotics and Intelligent Systems (IROS)},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros2014_megerShkurtiCortesPozaGiguereDudek.pdf},
 title = {3D Trajectory Synthesis and Control for a Legged Swimming Robot},
 year = {2014}
}

@inproceedings{6942988,
 author = {Q. {Zhang} and D. {Whitney} and F. {Shkurti} and I. {Rekleitis}},
 booktitle = {2014 IEEE/RSJ International Conference on Intelligent Robots and Systems},
 number = {},
 pages = {3081-3088},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros2014_gvg.pdf},
 title = {Ear-based exploration on hybrid metric/topological maps},
 volume = {},
 year = {2014}
}

@inproceedings{Xu2014APEXCMDR,
 abstract = {We present an intelligent driving software system that is capable of adapting to dynamic task conditions and adjusting to the steering preferences of a human passenger. This interactive autonomous system is a realization of a natural human-robot interaction paradigm known as Adaptation from Participation (AfP). AfP exploits intermittent input from the human passenger in order to adapt the vehicle’s autonomous behaviors to match the intended navigation targets and driving preferences. In our system, this is realized by dynamically adjusting the parameter settings of a vision-based algorithm for tracking terrain boundaries. We deployed the resulting interactive autonomous vehicle in diverse task settings, and demonstrated its ability to learn to drive on unseen paths, as well as to adapt to unexpected changes to the environment and to the vehicle’s camera placement.},
 address = {Montreal, Canada},
 author = {Anqi Xu and Qiwen Zhang and David Meger and Gregory Dudek},
 booktitle = {Proceedings of the 10th International Conference on Intelligent Unmanned Systems (ICIUS '14)},
 month = {September},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icius2014_apexcommander.pdf},
 media = {https://www.cim.mcgill.ca/~mrl/content/publications/other/slcommander_apex_gravel_road_demo.mp4},
 title = {{Interactive Autonomous Driving through Adaptation from Participation}},
 year = {2014}
}

@mastersthesis{sandeep_masters,
 author = {Sandeep Manjanna},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Sandeep_MSc_eThesis.pdf},
 school = {School of Computer Science, Center for Intelligent Machines, {McGill} University, Montreal, Canada},
 title = {Efficient Robotic Walking by Learning Gaits and Terrain Properties},
 year = {2014}
}

@inproceedings{GirdharICRA2014,
 author = {Girdhar, Yogesh and Whitney, David and Dudek, Gregory},
 booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra2014.pdf},
 title = {{Curiosity Based Exploration for Learning Terrain Models}},
 url = {http://www.cim.mcgill.ca/~yogesh/publications/icra2014.pdf},
 year = {2014}
}

@inproceedings{Girdhar2014CRV,
 address = {Montreal},
 author = {Girdhar, Yogesh and Dudek, Gregory},
 booktitle = {Canadian Conference on Computer and Robot Vision},
 file = {:Users/yogesh/References/Girdhar, Dudek - 2014 - Exploring Underwater Environments with Curiosity(2).pdf:pdf},
 keywords = {Exploration,Semantic Perception,Topic Modeling,Underwater Robotics},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/crv2014.pdf},
 title = {{Exploring Underwater Environments with Curiosity}},
 url = {http://www.cim.mcgill.ca/~yogesh/publications/crv2014.pdf},
 year = {2014}
}

@inproceedings{meghjani2014asymmetric,
 abstract = {In this paper we address the rendezvous problem between an autonomous underwater vehicle (AUV) and a passively floating drifter on the sea surface. The AUV’s mission is to keep an estimate of the floating drifter’s position while exploring the underwater environment and periodically attempting to rendezvous with it. We are interested in the case where the AUV loses track of the drifter, predicts its location and searches for it in the vicinity of the predicted location. We parameterize this search problem with respect to both the uncertainty in the drifter’s position estimate and the ratio between the drifter and the AUV speeds. We examine two search strategies for the AUV, an inward spiral and an outward spiral. We derive conditions under which these patterns are guaranteed to find a drifter, and we empirically analyze them with respect to different parameters in simulation. In addition, we present results from field trials in which an AUV successfully found a drifter after periods of communication loss during which the robot was exploring. },
 author = {Meghjani, Malika and Shkurti, Florian and Higuera, Juan Camilo Gamboa and Kalmbach, Arnold and Whitney, David and Dudek, Gregory},
 booktitle = {Computer and Robot Vision (CRV), 2014 Canadian Conference on},
 organization = {IEEE},
 pages = {175--180},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/CRV2014.pdf},
 title = {Asymmetric Rendezvous Search at Sea},
 year = {2014}
}

@inproceedings{meghjani2014multi,
 abstract = {In this paper we present an algorithm for finding a distance optimal rendezvous location with respect to both initial and target locations of the mobile agents. These agents can be humans or robots, who need to meet and split while performing a collaborative task. Our aim is to embed the meeting process within a background activity such that the agents travel through the rendezvous location while taking the shortest paths to their respective target locations. We analyze this problem in a street network scenario with two agents who are given their individual scheduled routes to complete with an underlying common goal. The agents are allowed to select any combination of the waypoints along their routes as long as they travel the shortest path and pass through the same potential rendezvous location. The total number of path combinations that the agents need to evaluate for the shortest path increases rapidly with the number of waypoints along their routes. We address this computational cost by proposing a combination of Euclidean and street network distances for a trade-off between the number of queries and a distance optimal solution. },
 author = {Meghjani, Malika and Dudek, Gregory},
 booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
 organization = {IEEE},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ICRA2014.pdf},
 title = {Multi-agent rendezvous on street networks},
 year = {2014}
}

@inproceedings{Xu2014APEX,
 abstract = {The problem of Adaptation from Participation (AfP) aims to improve the efficiency of a human-robot team by adapting a robot’s autonomous systems and behaviors based on command-level input from a human supervisor. As a solution to AfP, the Adaptive Parameter EXploration (APEX) algorithm continuously explores the space of all possible parameter configurations for the robot’s autonomous system in an online and anytime manner. Guided by information deduced from the human’s latest intervening commands, APEX is capable of adapting an arbitrary robot system to dynamic changes in task objectives and conditions during a session. We explore this framework within visual navigation contexts where the human-robot team is tasked with covering or patrolling over multiple terrain boundaries such as coastlines and roads. We present empirical evaluations of two separate APEX-enabled systems: the first, deployed on an aerial robot within a controlled environment, and the second, on a wheeled robot operating within a challenging university campus setting. },
 address = {Hong Kong, China},
 author = {Anqi Xu and Arnold Kalmbach and Gregory Dudek},
 booktitle = {Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA '14)},
 month = {May},
 pages = {3315--3322},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra2014_apex.pdf},
 media = {https://www.youtube.com/embed/tmeGLeJ98Ig},
 title = {{Adaptive Parameter EXploration (APEX): Adaptation of Robot Autonomy from Human Participation}},
 year = {2014}
}

@inproceedings{Florian2014MaxViz,
 abstract = {In this paper we address the issue of coordinating the trajectories of two collaborating robots in environments with obstacles so that visibility between them is maximized in the presence of competing constraints. Specifically, we examine the problem of allowing one robot (the "photographer") to follow another robot ("the subject") through a planar environment while maintaining visual contact to the maximum degree consistent with an efficient traversal. This problem has numerous applications, for instance in scenarios where communication between robots requires line-of-sight. We formalize this problem in the context of centralized kinodynamic planning and we present solutions based on the asymptotically optimal sampling-based RRT* planner. We discuss connections to the traditional formulation of pursuit-evasion games where the analysis typically ends the moment the evader manages to escape the pursuer's visibility region. We also illustrate types of environments and other conditions under which allowing the pair of robots to break the line-of-sight is a better option than always requiring the presence of visual contact. },
 address = {Hong Kong},
 author = {Shkurti, Florian and Dudek, Gregory},
 booktitle = {IEEE International Conference on Robotics and Automation},
 month = {May},
 pages = {3771-3776},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra2014_max_viz_planning.pdf},
 title = {{Maximizing Visibility in Collaborative Trajectory Planning}},
 year = {2014}
}

@inproceedings{Xu2013TrustStudy,
 abstract = {We are interested in enhancing the efficiency of human-robot collaborations, especially in .supervisor-worker. settings where autonomous robots work under the supervision of a human operator. We believe that trust serves a critical role in modeling the interactions within these teams, and also in streamlining their efficiency. We propose an operational formulation of human-robot trust on a short interaction time scale, which is tailored to a practical tele-robotics setting. We also report on a controlled user study that collected interaction data from participants collaborating with an autonomous robot to perform visual navigation tasks. Our analyses quantify key correlations between real-time human-robot trust assessments and diverse factors, including properties of failure events reflecting causal trust attribution, as well as strong influences from each user.s personality. We further construct and optimize a predictive model of users. trust responses to discrete events, which provides both insights on this fundamental aspect of real-time human-machine interaction, and also has pragmatic significance for designing trust-aware robot agents. },
 address = {Singapore},
 author = {Anqi Xu and Gregory Dudek},
 booktitle = {the 16th International Symposium on Robotics Research ({ISRR}'13)},
 month = {December},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/isrr2013_trust_study.pdf},
 title = {Towards Modeling Real-Time Trust in Asymmetric Human-Robot Collaborations},
 year = {2013}
}

@article{Girdhar2013IJRR,
 author = {Girdhar, Yogesh and Giguere, Philippe and Dudek, Gregory},
 journal = {International Journal of Robotics Research (Accepted)},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ijrr2013.pdf},
 media = {https://www.cim.mcgill.ca/~mrl/content/publications/other/ijrr2013.mp4},
 title = {{Autonomous Adaptive Exploration using Realtime Online Spatiotemporal Topic Modeling}},
 year = {2013}
}

@inproceedings{Kalmbach2013,
 address = {Karlsruhe, Germany},
 author = {Kalmbach, Arnold and Girdhar, Yogesh and Dudek, Gregory},
 booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra2013.pdf},
 title = {{Unsupervised Environment Recognition and Modeling using Sound Sensing}},
 url = {http://www.cim.mcgill.ca/~yogesh/publications/icra2013.pdf},
 year = {2013}
}

@inproceedings{Gamboa2013,
 address = {Karlsruhe, Germany},
 author = {Gamboa Higuera, Juan Camilo and Dudek, Gregory},
 booktitle = {2013 IEEE International Conference on Robotics and Automation (ICRA '13)},
 title = {{Fair Subdivision of Multi-Robot Tasks}},
 pdf = {http://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ICRA13_2267_FI.pdf},
 year = {2013}
}

@inproceedings{Shkurti13icra,
 address = {Karlsruhe, Germany},
 author = {Shkurti, Florian and Dudek, Gregory},
 booktitle = {IEEE International Conference on Robotics and Automation},
 month = {May},
 pages = {4047--4052},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra2013_pursuit_evasion.pdf},
 title = {{On the Complexity of Searching for an Evader with a Faster Pursuer}},
 year = {2013}
}

@inproceedings{Giguere2013,
 abstract = {For underwater swimming robots, which use the unconventional method of oscillating flippers for propulsion and control, being able to move stably at various velocities is challenging. This stable motion facilitates navigation, avoids blurring the images taken by a camera motion, and enables longterm observations of specific locations. Previous experiments with our swimming robot Aqua have shown that its autopilot system must adapt the control parameters as a function of speed. The reason is that the dynamics of both the robot and the thrusting system vary widely as a function of the overall velocity of the robot. In this paper, we present the results of manually tuning a stable autopilot system for this Aqua swimming robot. We employed a well-known technique called gain scheduling to allow for stable operation for velocities ranging from 0 to 40 cm=s, in real open sea conditions. Thus, our platform is now suitable for vision-based navigation in low light conditions as well as for extended observation through station-keeping. The results presented here are also a proof-of-concept that agile and reactive autonomous hovering is possible for flipper-based propulsion system.},
 author = {Giguere, Philippe and Girdhar, Yogesh and Dudek, Gregory},
 booktitle = {Canadian Conference on Computer and Robot Vision (CRV)},
 keywords = {Control,Gain Scheduling,Oscillating Foils,Station-keeping,Underwater robotics},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/crv2013.pdf},
 title = {{Wide-Speed Autopilot System for a Swimming Hexapod Robot}},
 year = {2013}
}

@inproceedings{sandeep_crv_2013,
 abstract = {In this paper we examine the interplay between terrain classification accuracy and gait in a walking robot, and show how changes in walking speed can be used for both terrain-dependent walk optimizations, as well as to enhance terrain identification. The details of a walking gait have a great influence on the performance of locomotive systems and their interaction with the terrain. Most legged robots can benefit from adapting their gait (and specifically walk speed) to the particular terrain on which they are walking. To achieve this, the agent should first be capable of identifying the terrain in order to choose the optimal speed. In this work we are interested in analyzing the performance of a legged robot on different terrains and with gait parameters. We also discuss the effects of gait parameter, such as speed; can have on the terrain identification by a legged robot. We use an unsupervised classification algorithm to classify terrains based on inertial measurement samples and actuator feedback collected over different terrains and operation speeds. We present the effects of speed on the terrain classification in our classification results.},
 author = {Sandeep Manjanna, Gregory Dudek, and Philippe Giguere},
 booktitle = {CRV '13: Proceedings of the 2013 International Conference on Computer and Robot Vision},
 month = {May},
 pages = {16--22},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/CRV2013.pdf},
 publisher = {IEEE Computer Society},
 title = {Using Gait Change for Terrain Sensing by Robots},
 year = {2013}
}

@inproceedings{Sandeep13iros,
 abstract = {In this paper we propose a design of a class of robotic legs (known as ``Ninja legs'') that enable amphibious operation, both walking and swimming, for use on a class of hexapod robots. Amphibious legs equip the robot with a capability to explore diverse locations in the world encompassing both those that are on the ground as well as underwater. In this paper we work with a hexapod robot of the Aqua vehicle family (based on a body plan first developed by Buehler et al. \cite{georgiades2004aqua}), which is an amphibious robot that employs legs for amphibious locomotion. Many different leg designs have been previously developed for Aqua-class vehicles, including both robust all-terrain legs for walking, and efficient flippers for swimming. But the walking legs have extremely poor thrust for swimming and the flippers are completely unsuitable for terrestrial operations. In this work we propose a single leg design with the advantages of both the walking legs and the swimming flippers. We design a cage-like circular enclosure for the flippers in order to protect the flippers during terrestrial operations. The enclosing structure also plays the role of the walking legs for terrestrial locomotion. The circular shape of the enclosure, as well, has the advantages of an offset wheel. We evaluate the performance of our design for terrestrial mobility by comparing the power efficiency and the physical speed of the robot equipped with the newly designed legs against that with the walking legs which are semi-circular in shape. The swimming performance is examined by measuring the thrust generated by newly designed legs and comparing the same with the thrust generated by the swimming flippers. In the field, we also verified that these legs are suitable for swimming through moderate surf, walking through the breakers on a beach (and thus through slurry), and onto wet and dry sand.},
 address = {Tokyo, Japan},
 author = {Bir Bikram Dey and Sandeep Manjanna and Gregory Dudek},
 booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '13)},
 month = {November},
 pages = {},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/IROS2013.pdf},
 title = {{Ninja Legs: Amphibious One Degree of Freedom Robotic Legs}},
 year = {2013}
}

@inproceedings{GirdharWREM2012,
 author = {Girdhar, Yogesh and Xu, Anqi and Shkurti, Florian and Camilo, Juan and Higuera, Gamboa and Meghjani, Malika and Giguere, Philippe and Rekleitis, Ioannis and Dudek, Gregory},
 booktitle = {RSS 2012 Workshop on Robotics for Environmental Monitoring},
 file = {:home/thalassa/yogesh/References/library/Girdhar et al. - 2012 - Monitoring Marine Environments using a Team of Heterogeneous Robots.pdf:pdf},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/wrem2012.pdf},
 title = {{Monitoring Marine Environments using a Team of Heterogeneous Robots}},
 year = {2012}
}

@inproceedings{GirdharISER2012,
 author = {Girdhar, Yogesh and Gigu\`{e}re, Philippe and Dudek, Gregory},
 booktitle = {International Symposium on Experimental Robotics (ISER)},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iser2012.pdf},
 title = {{Autonomous Adaptive Underwater Exploration using Online Topic Modelling}},
 year = {2012}
}

@inproceedings{meghjani2012multi,
 author = {Meghjani, Malika and Dudek, Gregory},
 booktitle = {Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on},
 organization = {IEEE},
 pages = {5270--5276},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/IROS2012.pdf},
 title = {Multi-robot exploration and rendezvous on graphs},
 year = {2012}
}

@inproceedings{Shkurti12iros,
 abstract = {In this paper we describe a heterogeneous multirobot system for assisting scientists in environmental monitoring tasks, such as the inspection of marine ecosystems. This team of robots is comprised of a fixed-wing aerial vehicle, an autonomous airboat, and an agile legged underwater robot. These robots interact with off-site scientists and operate in a hierarchical structure to autonomously collect visual footage of interesting underwater regions, from multiple scales and mediums. We discuss organizational and scheduling complexities associated with multi-robot experiments in a field robotics setting. We also present results from our field trials, where we demonstrated the use of this heterogeneous robot team to achieve multi-domain monitoring of coral reefs, based on realtime interaction with a remotely-located marine biologist. },
 address = {Algarve, Portugal},
 author = {Florian Shkurti and Anqi Xu and Malika Meghjani and Juan Camilo Gamboa Higuera and Yogesh Girdhar and Philippe Giguere and
Bir Bikram Dey and Jimmy Li and Arnold Kalmbach and Chris Prahacs and Katrine Turgeon and Ioannis Rekleitis and Gregory Dudek},
 booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '12)},
 month = {October},
 pages = {1747--1753},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros2012.pdf},
 title = {{Multi-Domain Monitoring of Marine Environments Using a Heterogeneous Robot Team}},
 year = {2012}
}

@inproceedings{Gamboa2012SocialPlan,
 abstract = {We address the problem of path planning for robot missions based on waypoints suggested by multiple human users. These users may be operating under distinct mission objectives and hence suggest different locations for the robot to visit. We formulate this problem using a constrained optimization approach by imposing various operational considerations, such as the robot’s maximum traversable distance. We then propose an approximative path planning algorithm with parameterized control over the degree of “social fairness” in the selection of waypoints from different users. Through a user study, we compared the performance of the proposed path planner under different fairness settings and for different mission scenarios. },
 address = {Toronto, Canada},
 author = {Juan Camilo Gamboa Higuera and Anqi Xu and Florian Shkurti and Gregory Dudek},
 booktitle = {in Proceedings of the 9th Canadian Conference on Computer and Robot Vision (CRV '12)},
 month = {May},
 pages = {417--424},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/crv2012_social_plan.pdf},
 title = {Socially-Driven Collective Path Planning for Robot Missions},
 year = {2012}
}

@inproceedings{GirdharICRA2012,
 abstract = {We are interested in the task of online summarization of the data observed by a mobile robot, with the goal that these summaries could be then be used for applications such as surveillance, identifying samples to be collected by a planetary rover, and site inspections to detect anomalies. In this paper, we pose the summarization problem as an instance of the well known $k$-center problem, where the goal is to identify $k$ observations so that the maximum distance of any observation from a summary sample is minimized. We focus on the online version of the summarization problem, which requires that the decision to add an incoming observation to the summary be made instantaneously. Moreover, we add the constraint that only a finite number of observed samples can be saved at any time, which allows for applications where the selection of a sample is linked to a physical action such as rock sample collection by a planetary rover. We show that the proposed online algorithm has performance comparable to the offline algorithm when used with real world data. },
 author = {Girdhar, Yogesh and Dudek, Gregory},
 booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra2012.pdf},
 title = {{Efficient on-line data summarization using extremum summaries}},
 year = {2012}
}

@inproceedings{Xu2012Trust,
 abstract = {We describe a model of “trust” in human-robot systems that is inferred from their interactions, and inspired by similar concepts relating to trust among humans. This computable quantity allows a robot to estimate the extent to which its performance is consistent with a human’s expectations, with respect to task demands. Our trust model drives an adaptive mechanism that dynamically adjusts the robot’s autonomous behaviors, in order to improve the efficiency of the collaborative team. We illustrate this trust-driven methodology through an interactive visual robot navigation system. This system is evaluated through controlled user experiments and a field demonstration using an aerial robot. },
 address = {St. Paul, USA},
 author = {Anqi Xu and Gregory Dudek},
 booktitle = {Proceedings of the 2012 IEEE International Conference on Intelligent Robots and Systems (ICRA '12)},
 month = {May},
 pages = {3922--3929},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra2012_trust.pdf},
 title = {{Trust-Driven Interactive Visual Navigation for Autonomous Robots}},
 year = {2012}
}

@inproceedings{Virie2011MatchingInaccurate,
 address = {Phuket, Thailand},
 author = {Chatavut Viriyasuthee and Gregory Dudek},
 booktitle = {Proceedings of the 2011 IEEE International Conference on Robotics and Biomimetics (ROBIO '11)},
 month = {December},
 pages = {2629--2634},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Matching_Inaccurate.pdf},
 title = {{One-to-one Feature Matching with Inaccurate Maps}},
 year = {2011}
}

@article{Chen2011oe,
 abstract = {The acquisition of a three-dimensional (3-D) model in a real-world environment by scanning only sparsely can save us a great ammount range-sensing time. We address a new method for inferring missing range data based on the given intensity image and sparse range data. It is assumed that the known range data are given on a number of scan lines with 1 pixel width. This assumption is natural for a range sensor to acquire range data in a 3-D real-world environment. Both edge information of the intensity image and linear interpolation of the range data are used. Experiments show that this method gives very good results in inferring missing range data. It outperforms both the previous method and bilinear interpolation when a very small percentage of range data are known.},
 author = {Guangyi Chen and Gregory Dudek and Luz Abril Torres-Mendez},
 doi = {10.1117/1.3617455},
 journal = {Optical Engineering},
 keywords = {computer vision, image reconstruction, interpolation, 3D modeling, 3D acquisition, Data modeling, Error analysis, MATLAB, 3D image processing, Data acquisition, Optical engineering, Sensors, 3D scanning},
 number = {9},
 pages = {1 -- 8},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Matching_Inaccurate_1.pdf},
 publisher = {SPIE},
 title = {{Scene reconstruction with sparse range data and intensity information}},
 url = {https://doi.org/10.1117/1.3617455},
 volume = {50},
 year = {2011}
}

@inproceedings{Girdhar2011MARE,
 abstract = {We present MARE, an autonomous airboat robot that is suitable for exploration-oriented tasks, such as inspection of coral reefs and shallow seabeds. The combination of this platform's particular mechanical properties and its powerful software framework enables it to function in a multitude of potential capacities, including autonomous surveillance, mapping, and search operations. In this paper we describe two different exploration strategies and their implementation using the MARE platform. First, we discuss the application of an efficient coverage algorithm, for the purpose of achieving systematic exploration of a known and bounded environment. Second, we present an exploration strategy driven by surprise, which steers the robot on a path that might lead to potentially surprising observations. },
 address = {San Francisco, USA},
 author = {Yogesh Girdhar and Anqi Xu and Bir Bikram Dey and Malika Meghjani and Florian Shkurti and Ioannis Rekleitis and Gregory Dudek},
 booktitle = {Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '11)},
 month = {September},
 pages = {5048--5053},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros2011_boat.pdf},
 media = {https://www.cim.mcgill.ca/~mrl/content/publications/other/iros2011_boat.mp4},
 title = {{MARE: Marine Autonomous Robotic Explorer}},
 year = {2011}
}

@misc{misha2011soyummy,
 abstract = {SoYummy seeks to develop human mind augmentation through computer vision and artificial intelligence. North Americans spend half their day using some type of media, usually video. It would take 72 million years to watch the amount of video that will cross the Internet in 2014 alone. SoYummy responds to the explosion of digital video with radical temporal compression of meaning. Using the concept of visual surprise, their applications create visual summaries to epitomize everything from underwater survey video to popular movies. SoYummy is a group that includes United States artist Misha Rabinovich and Canadian scientists Yogesh Girdhar and Gregory Dudek. },
 author = {Misha Rabinovich and Yogesh Girdhar and Gregory Dudek},
 howpublished = {Subtle Technologies Festival, June 2-5, 2011, Toronto, Canada.},
 title = {SoYummy: real-time temporal semantic compression to further the Synopticon},
 url = {/url{http://subtletechnologies.com/festival-2011/}},
 year = {2011}
}

@inproceedings{GirdharCRV2011,
 abstract = {The idea of an online visual vocabulary is proposed. In contrast to the accepted strategy of generating vocabularies offline, using the k-means clustering over all the features extracted form all the images in a dataset, an online vocabulary is dynamic and evolves iteratively over time as new observations are made.  Hence, it is much more suitable for online robotic applications, such as exploration, landmark detection, and SLAM, where the future is unknown. We present two different strategies for building online vocabularies. The first strategy produces a vocabulary, which optimizes the k-centres objective of minimizing the maximum distance of a a feature from the closest vocabulary word. The second strategy produces a vocabulary by randomly sampling from the current vocabulary and the features in the current observation. We show that both the algorithms are able to produce distance matrices which have positive rank correlation with distance matrices computed using an offline k-means vocabulary. We discover that the online random vocabulary is consistently effective at approximating the behaviour of the offline k-means vocabulary, at least for the moderate sized datasets we examine.
},
 author = {Yogesh Girdhar and Gregory Dudek},
 booktitle = {CRV '11: Proceedings of the 2011 Canadian Conference on Computer and Robot Vision},
 date-added = {2011-03-27 19:11:30 -0400},
 date-modified = {2011-03-27 21:28:17 -0400},
 month = {May},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Girdhar2011CRV.pdf},
 publisher = {IEEE Computer Society},
 title = {Online Visual Vocabularies},
 year = {2011}
}

@inproceedings{5957538,
 author = {C. {Viriyasuthee} and G. {Dudek}},
 booktitle = {2011 Canadian Conference on Computer and Robot Vision},
 number = {},
 pages = {24-31},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Conformative_Filter.pdf},
 title = {Conformative Filter: A Probabilistic Framework for Localization in Reduced Space},
 volume = {},
 year = {2011}
}

@inproceedings{florian_crv_2011,
 abstract = {In this paper we present the computer vision component of a 6DOF pose estimation algorithm to be used by an underwater robot. Our goal is to evaluate which feature trackers enable us to accurately estimate the 3D positions of features, as quickly as possible. To this end, we perform an evaluation of available detectors, descriptors, and matching schemes, over different underwater datasets. We are interested in identifying combinations in this search space that are suitable for use in structure from motion algorithms, and more generally, vision-aided localization algorithms that use a monocular camera. Our evaluation includes frame-by-frame statistics of desired attributes, as well as measures of robustness expressed as the length of tracked features. We compare the fit of each combination based on the following attributes: number of extracted keypoints per frame, length of feature tracks, average tracking time per frame, number of false positive matches between frames. Several datasets were used, collected in different underwater locations and under different lighting and visibility conditions. },
 author = {Florian Shkurti, Ioannis Rekleitis, and Gregory Dudek},
 booktitle = {CRV '11: Proceedings of the 2011 Canadian Conference on Computer and Robot Vision},
 month = {May},
 pages = {160--167},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/crv2011_underwater_ft.pdf},
 publisher = {IEEE Computer Society},
 title = {Feature Tracking Evaluation for Pose Estimation in Underwater Environments},
 year = {2011}
}

@inproceedings{meghjani2011combining,
 abstract = {We consider the problem of exploring an unknown environment with a pair of mobile robots. The goal is to make the robots meet (or rendezvous) in minimum time such that there is a maximum speed gain of the exploration task. The key challenge in achieving this goal is to rendezvous with the least possible dependency on communication. This single constraint involves several sub-problems: finding unique potential rendezvous locations in the environment, ranking these locations based on their uniqueness and synchronizing with the other robot to meet at one of the locations at a scheduled time. In addition, these tasks are to be performed simultaneously while exploring and mapping the environment. We propose an approach for efficiently combining the exploration and rendezvous tasks by considering the cost of reaching a rendezvous location and the reward of its uniqueness. This cost and reward model is combined with a set of deterministic and probabilistic rendezvous strategies for the robots to meet during exploration. Experimental results suggest that the joint tasks of exploration and rendezvous are substantially improved by ranking the potential rendezvous locations based on the combined cost-reward criterion when compared to the ranking solely based on the uniqueness of the location. },
 author = {Meghjani, Malika and Dudek, Gregory},
 booktitle = {Computer and Robot Vision (CRV), 2011 Canadian Conference on},
 organization = {IEEE},
 pages = {80--85},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/CRV2011.pdf},
 title = {Combining multi-robot exploration and rendezvous},
 year = {2011}
}

@inproceedings{Xu2011FTag,
 abstract = {We describe the design and implementation of a fiducial marker system that encodes data in the frequency spectrum of a synthetic image. This distinctive approach to marker synthesis and data encoding allows for partial data extraction in adverse imaging conditions, and can significantly extend the detection range through graceful data degradation. Additional digital encoding and image construction techniques are used to increase the payload capacity, and also to store 3-D pose information in each fiducial marker. This fiducial marker scheme can be configured to match the needs of the target application.We present several experiments investigating the practical range of various parameters as well as the performance of a specific instance of the system. },
 address = {St. John's, Newfoundland, Canada},
 author = {Anqi Xu and Gregory Dudek},
 booktitle = {Proceedings of the 8th Canadian Conference on Computer and Robot Vision (CRV '11)},
 month = {May},
 pages = {40--47},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/crv2011_ftag.pdf},
 title = {Fourier Tag: A Smoothly Degradable Fiducial Marker System with Configurable Payload Capacity},
 year = {2011}
}

@article{5752869,
 abstract = {This paper describes a tactile probe designed for surface identification, in a context of all-terrain low-velocity mobile robotics. The proposed tactile probe is made of a small metallic rod with a single-axis accelerometer attached near its tip. Surface identification is based on analyzing acceleration patterns induced at the tip of this mechanically robust tactile probe, while it is passively dragged along a surface. A training data set was collected over ten different indoor and outdoor surfaces. Classification results for an artificial neural network were positive, with 89.9 % and 94.6 % success rate for 1 and 4 second time-windows of data, respectively. We also demonstrated that the same tactile probe can be used for unsupervised learning of terrains. For 1 second time-windows of data, the classification success rate was only reduced to 74.1 %. Finally, a blind mobile robot, performing real-time classification of surfaces, demonstrated the feasibility of this tactile probe as a guidance mechanism. },
 author = {P. {Giguere} and G. {Dudek}},
 journal = {IEEE Transactions on Robotics},
 number = {3},
 pages = {534-544},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/IeeeTro2011.pdf},
 title = {A Simple Tactile Probe for Surface Identification by Mobile Robots},
 volume = {27},
 year = {2011}
}

@inproceedings{Sattar11ICRA,
 abstract = {We present a technique for robust human-robot interaction taking into consideration uncertainty in input and task execution costs incurred by the robot. Specifically, this research aims to quantitatively model confirmation feedback, as required by a robot while communicating with a human operator to perform a particular task. Our goal is to model human-robot interaction from the perspective of risk minimization, taking into account errors in communication, ``risk'' involved in performing the required task, and task execution costs. Given an input modality with non-trivial uncertainty, we calculate the cost associated with performing the task specified by the user, and if deemed necessary, ask the user for confirmation. The estimated task cost and the uncertainty measure are given as input to a \emph{Decision Function}, the output of which is then used to decide whether to execute the task, or request clarification from the user. In cases where the cost or uncertainty (or both) is estimated to be exceedingly high by the system, task execution is deferred until a significant reduction in the output of the Decision Function is achieved. We test our system through human--interface experiments, based on a framework custom--designed for our family of amphibious robots, and demonstrate the utility of the framework in the presence of large task costs and uncertainties. We also present qualitative results of our algorithm from field trials of our robots in both open-- and closed--water environments. },
 address = {Shanghai, China},
 author = {Junaed Sattar and Gregory Dudek},
 booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation {ICRA}},
 month = {May},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra2011.pdf},
 title = {Towards Quantitative Modeling of Task Confirmations in Human-Robot Dialog},
 year = {2011}
}

@inproceedings{Li2011GSSP,
 abstract = {We describe a framework that combines a software development paradigm, a software visualization technique, and a tool for robot programming. This infrastructure is called "Graphical State Space Programming" (GSSP), and allows robot application programs to be decomposed and visualized within state-dependent views. Our approach simplifies and expedites the programming process for robot routines and behaviors, and we examine the performance improvement that ensues through a set of controlled user studies. The usability and effectiveness of GSSP are also illustrated using a field demonstration with an aerial robotic vehicle. },
 address = {Shanghai, China},
 author = {Jimmy Li and Anqi Xu and Gregory Dudek},
 booktitle = {Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA '11)},
 month = {May},
 pages = {4846--4853},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra2011_gssp.pdf},
 media = {https://www.cim.mcgill.ca/~mrl/content/publications/other/icra2011_gssp.mpg},
 title = {Graphical State Space Programming: A Visual Programming Paradigm for Robot Task Specification},
 year = {2011}
}

@inproceedings{Xu2011Coverage,
 abstract = {We present the adaptation of an optimal terrain coverage algorithm for the aerial robotics domain. The general strategy involves computing a trajectory through a known environment with obstacles that ensures complete coverage of the terrain while minimizing path repetition. We introduce a system that applies and extends this generic algorithm to achieve automated terrain coverage using an aerial vehicle. Extensive experimental results in simulation validate the presented system, along with data from over 100 kilometers of successful coverage flights using a fixed-wing aircraft. },
 address = {Shanghai, China},
 author = {Anqi Xu and Chatavut Viriyasuthee and Ioannis Rekleitis},
 booktitle = {Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA '11)},
 media = {https://www.cim.mcgill.ca/~mrl/content/publications/other/icra2011_optcov_1.mp4},
 month = {May},
 pages = {2513--2519},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra2011_optcov.pdf},
 title = {Optimal Complete Terrain Coverage using an Unmanned Aerial Vehicle},
 year = {2011}
}

@inproceedings{GirdharICRA2011,
 abstract = {In this paper we focus on the task of summarizing observations made by a mobile robot on a trajectory. A navigation summary is the synopsis of these observations. We pose the problem of generating navigation summaries as a sampling problem. The goal is to select a few samples from the set of all observations, which are characteristic of the environment, and capture its mean properties and surprises. We define the surprise score of an observation as its distance to the closest sample in the summary. Hence, an ideal summary is defined to have a low mean and a low max surprise score, measured over all the observations.
We present three different strategies for solving this sampling problem. Of these, we show that the kCover sampling algorithm produces summaries with low mean and max surprise scores; even in the presence of noise. These results are demonstrated on datasets acquired in different robotics context.},
 author = {Yogesh Girdhar and Gregory Dudek},
 booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
 date-added = {2011-02-02 22:20:35 -0500},
 date-modified = {2011-02-02 22:21:13 -0500},
 month = {May},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Girdhar2011ICRA.pdf},
 title = {Offline Navigation Summaries},
 year = {2011}
}

@incollection{GirdharV3D2011,
 annote = {ISBN-13: 9781107001756},
 author = {Yogesh Girdhar and Gregory Dudek},
 booktitle = {Vision in 3D Environments},
 chapter = {11},
 date-added = {2011-02-02 21:40:31 -0500},
 date-modified = {2011-02-02 21:46:40 -0500},
 editor = {Laurence R. Harris and  Michael R. M. Jenkin},
 publisher = {Cambridge University Press},
 title = {A surprising problem in navigation},
 year = {2011}
}

@phdthesis{giguere2010thesis,
 abstract = {In this thesis, we consider the problem of having a mobile robot autonomously learn to perceive differences between terrains. The targeted application is for terrain identification. Robust terrain identification can be used to enhance the capabilities of mobile systems, both in terms of locomotion and navigation. For example, a legged amphibious robot that has learned to differentiate sand from water can automatically select its gait on a beach: walking for sand, and swimming for water. The same terrain information can also be used to guide a robot in order to avoid specific terrain types. The problem of autonomous terrain identification is decomposed into two sub-problems: a sensing sub-problem, and a learning sub-problem. In the sensing sub-problem, we look at extracting terrain information from existing sensors, and at the design of a new tactile probe. In particular, we show that inertial sensor measurements and actuator feedback information can be combined to enable terrain identification for a legged robot. In addition, we describe a novel tactile probe designed for improved terrain sensing. In the learning sub-problem, we discuss how temporal or spatial continuities can be exploited to perform the clustering of both time-series and images. Specifically, we present a new algorithm that can be used to train a number of classifiers in order to perform clustering when temporal or spatial dependencies between samples are present. We combine our sensing approach with this clustering technique, to obtain a computational architecture that can learn autonomously to differentiate terrains. This approach is validated experimentally using several different sensing modalities (proprioceptive and tactile) and with two different robotic platforms (on a legged robot named AQUA and a wheeled robot iRobot™ Create™). Finally, we show that the same clustering technique, when combined with image information, can be used to define a new image segmentation algorithm.},
 address = {CAN},
 author = {Giguere, Philippe},
 isbn = {9780494726310},
 note = {AAINR72631},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/thesis.pgiguere.pdf},
 publisher = {McGill University},
 title = {Unsupervised Learning for Mobile Robot Terrain Classification},
 year = {2010}
}

@book{dudek2010computational,
 abstract = {This book presents a range of strategies for locomotion, sensing, and reasoning. It concentrates on wheeled and legged mobile robots but discusses a variety of other propulsion systems. The new edition includes advances in robotics and intelligent machines over the last ten years, including significant coverage of SLAM (simultaneous localization and mapping) and multi-robot systems. It includes additional mathematical background and an extensive list of sample problems. Various mathematical techniques that were assumed in the first edition are now briefly introduced in appendices at the end of the text to make the book more self-contained. Researchers as well as students in the field of mobile robotics will appreciate this comprehensive treatment of state-of-the-art methods and key technologies. },
 author = {Dudek, Gregory and Jenkin, Michael},
 publisher = {Cambridge university press},
 title = {Computational principles of mobile robotics},
 year = {2010}
}

@article{MarinakisDudek08_TROS,
 abstract = {In this paper we investigate a pure form of the topological mapping problem in mobile robotics. We consider the mapping ability of a robot navigating a graph-like world in which it is able to assign a relative ordering to the edges leaving a vertex with reference to the edge by which it arrived, but is unable to associate a unique label with any vertex or edge. Our work extends and builds upon earlier approaches in this problem domain, which are based on constructing an exploration tree of plausible world-models. The main contributions of the paper are: improved exploration strategies that reduce model ambiguity; a new method of searching through consistent models in the exploration tree that maintains a bounded set of likely hypotheses based on the principle of Occam's Razor; the incorporation of arbitrary feature vectors into the problem formulation; and an investigation of various aspects of this problem through numerical simulations. },
 author = {D. Marinakis and G. Dudek},
 journal = {IEEE Transactions on Robotics},
 month = {Dec},
 number = {6},
 pages = {1051-1064},
 title = {Pure Topological Mapping in Mobile Robotics},
 volume = {26},
 year = {2010}
}

@inproceedings{GirdharIROS2010,
 abstract = {We propose an algorithm for generating naviga- tion summaries. Navigation summaries are a specialization of video summaries, where the focus is on video collected by a mobile robot, on a specified trajectory. We are interested in finding a few images that epitomize the visual experience of a robot as it traverse a terrain. This paper presents a novel approach to generating summaries in form of a set of images, where the decision to include the image in the summary set is made online. Our focus is on the case where the number of observations is infinite or unknown, but the size of the desired summary is known. Our strategy is to consider the images in the summary set as the prior hypothesis of the appearance of the world, and then use the idea of Bayesian Surprise to compute the novelty of an observed image. If the novelty is above a threshold, then we accept the image. We discuss different criterion for setting this threshold. Online nature of our approach allows for several interesting applications such as coral reef inspection, surveying, and surveillance.},
 author = {Yogesh Girdhar and Gregory Dudek},
 booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, {IROS}},
 date-modified = {2011-02-02 21:55:38 -0500},
 month = {Oct},
 pages = {746-751},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Girdhar2010IROS.pdf},
 title = {{ONSUM}: A System for Generating Online Navigation Summaries},
 year = {2010}
}

@inproceedings{GirdharICRA2010,
 abstract = {Our objective is to find a small set of images that summarize a robot's visual experience along a path. We present a novel on-line algorithm for this task. This algorithm is based on a new extension to the classical Secretaries Problem. We also present an extension to the idea of Bayesian Surprise, which we then use to measure the fitness of an image as a summary image.},
 author = {Yogesh Girdhar and Gregory Dudek},
 booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
 month = {May},
 pages = {5035-5040},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Girdhar2010ICRA.pdf},
 title = {Online Navigation Summaries},
 year = {2010}
}

@inproceedings{Xu2010UAVServo,
 abstract = {We present an integration of classical computer vision techniques to achieve real-time autonomous steering of an unmanned aircraft along the boundary of different regions. We present two solutions, based on the same conceptual framework, to track coastlines and to follow roads surrounded by forests. In particular, we exploit color and texture properties to differentiate between the two region types in each of the aforementioned domains. The performance of these methods are evaluated using different experimental approaches, including a fully automated real-time flight outdoors over a 1km trajectory. },
 address = {Taipei, Taiwan},
 author = {Anqi Xu and Gregory Dudek},
 booktitle = {Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '10)},
 month = {October},
 pages = {81--86},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros2010_uav.pdf},
 title = {A Vision-Based Boundary Following Framework for Aerial Vehicles},
 year = {2010}
}

@inbook{Sattar2014,
 abstract = {We present a technique for robust human-robot interaction taking into consideration uncertainty in input and task execution costs incurred by the robot. Specifically, this research aims to quantitatively model confirmation feedback, as required by a robot while communicating with a human operator to perform a particular task. Our goal is to model human-robot interaction from the perspective of risk minimization, taking into account errors in communication, ``risk'' involved in performing the required task, and task execution costs. Given an input modality with non-trivial uncertainty, we calculate the cost associated with performing the task specified by the user, and if deemed necessary, ask the user for confirmation. The estimated task cost and the uncertainty measure is given as input to a Decision Function, the output of which is then used to decide whether to execute the task, or request clarification from the user. In cases where the cost or uncertainty (or both) is estimated to be exceedingly high by the system, task execution is deferred until a significant reduction in the output of the Decision Function is achieved. We test our system through human-interface experiments, based on a framework custom designed for our family of amphibious robots, and demonstrate the utility of the framework in the presence of large task costs and uncertainties.We also present qualitative results of our algorithm from field trials of our robots in both open- and closed-water environments.},
 address = {Berlin, Heidelberg},
 author = {Sattar, Junaed and Dudek, Gregory},
 booktitle = {Experimental Robotics: The 12th International Symposium on Experimental Robotics},
 doi = {10.1007/978-3-642-28572-1_6},
 editor = {Khatib, Oussama and Kumar, Vijay and Sukhatme, Gaurav},
 isbn = {978-3-642-28572-1},
 pages = {81--95},
 publisher = {Springer Berlin Heidelberg},
 title = {Reducing Uncertainty in Human-Robot Interaction: A Cost Analysis Approach},
 url = {https://doi.org/10.1007/978-3-642-28572-1_6},
 year = {2014}
}

@inproceedings{5479176,
 author = {I. {Rekleitis} and G. {Dudek} and Y. {Schoueri} and P. {Giguere} and J. {Sattar}},
 booktitle = {2010 Canadian Conference on Computer and Robot Vision},
 number = {},
 pages = {261-268},
 title = {Telepresence across the Ocean},
 volume = {},
 year = {2010}
}

@inproceedings{Sattar2010GSSP,
 abstract = {We present an interface for controlling mobile robots that combines aspects of graphical trajectory specification and state-based programming. This work is motivated by common tasks executed by our underwater vehicles, although we illustrate a mode of interaction that is applicable to mobile robotics in general. The key aspect of our approach is to provide an intuitive linkage between the graphical visualization of regions of interest in the environment, and activities relevant to these regions. In addition to introducing this novel programming paradigm, we also describe the associated system architecture developed on-board our amphibious robot. We then present a user interaction study that illustrates the benefits in usability of our graphical interface, compared to conventionally established programming techniques. },
 address = {Anchorage, Alaska, USA},
 author = {Junaed Sattar and Anqi Xu and Gregory Dudek and Gabriel Charette},
 booktitle = {Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA '10)},
 month = {May},
 pages = {4609--4614},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra2010_gssp.pdf},
 title = {Graphical State-Space Programmability as a Natural Interface for Robotic Control},
 year = {2010}
}

@inproceedings{meghjani2009bimodal,
 abstract = {We present a bimodal information analysis system for automatic emotion recognition. Our approach is based on the analysis of video sequences which combines facial expressions observed visually with acoustic features to automatically recognize five universal emotion classes: Anger, Disgust, Happiness, Sadness and Surprise. We address the challenges posed during the temporal analysis of the bimodal data and introduce a novel technique for combining the best features of instantaneous and temporal based visual recognition systems. We obtain robust appearance-based visual features which we classify instantaneously and aggregate it temporally to improve the recognition rates when compared to single-frame based instantaneous classification. The performance of the system is further boosted by using the complementary audio information for the bimodal emotion recognition. We combine the two modalities at both feature and score level to compare the respective joint emotion recognition rates. The emotions are instantaneously classified using a Support Vector Machine and sequentially aggregated based on their classification probabilities. This approach is validated on a posed audio-visual database and a natural interactive database. The experiments performed on these databases provide encouraging results with the best combined recognition rate being 82%. },
 author = {Meghjani, Malika and Ferrie, Frank and Dudek, Gregory},
 booktitle = {Applications of Computer Vision (WACV), 2009 Workshop on},
 organization = {IEEE},
 pages = {1--6},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/WACV2009.pdf},
 title = {Bimodal information analysis for emotion recognition},
 year = {2009}
}

@mastersthesis{meghjani2010bimodal,
 abstract = {We present an audio-visual information analysis system for automatic emotion recognition. We propose an approach for the analysis of video sequences which combines facial expressions observed visually with acoustic features to automatically recognize five universal emotion classes: Anger, Disgust, Happiness, Sadness and Surprise. The visual component of our system evaluates the facial expressions using a bank of 20 Gabor filters that spatially sample the images. The audio analysis is based on global statistics of voice pitch and intensity along with the temporal features like speech rate and Mel Frequency Cepstrum Coefficients. We combine the two modalities at feature and score level to compare the respective joint emotion recognition rates. The emotions are instantaneously classified using a Support Vector Machine and the temporal inference is drawn based on scores obtained as the output of the classifier. This approach is validated on a posed audio-visual database and a natural interactive database to test the robustness of our algorithm. The experiments performed on these databases provide encouraging results with the best combined recognition rate being 82%. },
 author = {Meghjani, Malika},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Meghjani09_Masters_Thesis.pdf},
 school = {McGill University},
 title = {Bimodal Information Analysis for Emotion Recognition},
 year = {2010}
}

@phdthesis{marinakis09,
 abstract = {This thesis addresses the problem of using distributed sensing for automatically inferring a representation of the environment, i.e. a map, that can be useful for the self-calibration of intelligence systems, such as sensor networks. The information recovered by such a process allows typical applications such as data collection and navigation to proceed without labour intensive input from a human technician. Simplifying the deployment of large scale sensor networks and other intelligent systems will effectively reduce their cost and improve their widespread availability and hence aid their practical application to tasks such as the monitoring of carbon emissions and greenhouse gases. In our research we focus on algorithms and techniques for recovering two types of information from the immediate environment: topology information that indicates physical connectivity between regions of interest from the point of view of a navigating agent; and a probability distribution function (PDF) describing the position of components of the intelligent system. We consider situations where data is collected from systems that comprise of a number of stationary network components; stationary network components augmented with a mobile robot; or a mobile robot only. Our approaches are, for the most part, based on statistical methods that employ stochastic sampling techniques to provide approximate solutions to problems for which computing the optimal or exact solution is intractable. Numerical simulations and experiments conducted on hardware suggest that this research has promising real world applications in the area of sensor network self-configuration.},
 author = {Dimitri Marinakis},
 document_type = {phdthesis},
 month = {June},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Marinakis09_PhDThesis.pdf},
 school = {McGill University},
 title = {Inferring Environmental Representations Through Limited Sensory Data With Applications To Sensor Network Self-calbration},
 year = {2009}
}

@article{MARINAKIS2009116,
 abstract = {When a network of vision-based sensors is emplaced in an environment for applications such as surveillance or monitoring the spatial relationships between the sensing units must be inferred or computed for self-calibration purposes. In this paper we describe a technique to solve one aspect of this self-calibration problem: automatically determining the topology and connectivity information of a network of cameras based on a statistical analysis of observed motion in the environment. While the technique can use labels from reliable cameras systems, the algorithm is powerful enough to function using ambiguous tracking data. The method requires no prior knowledge of the relative locations of the cameras and operates under very weak environmental assumptions. Our approach stochastically samples plausible agent trajectories based on a delay model that allows for transitions to and from sources and sinks in the environment. The technique demonstrates considerable robustness both to sensor error and non-trivial patterns of agent motion. The output of the method is a Markov model describing the behavior of agents in the system and the underlying traffic patterns. The concept is demonstrated with simulation data for systems containing up to 10 agents and verified with experiments conducted on a six camera sensor network.},
 author = {Dimitri Marinakis and Gregory Dudek},
 doi = {https://doi.org/10.1016/j.imavis.2006.06.009},
 issn = {0262-8856},
 journal = {Image and Vision Computing},
 keywords = {Sensor networks, Perception, Self-calibration, Topology, Learning, Markov chain monte carlo, Expectation maximization},
 note = {Canadian Robotic Vision 2005 and 2006},
 number = {1},
 pages = {116 - 130},
 title = {Self-calibration of a vision-based sensor network},
 url = {http://www.sciencedirect.com/science/article/pii/S0262885606002599},
 volume = {27},
 year = {2009}
}

@article{giguere2009clustering,
 author = {Giguere, Philippe and Dudek, Gregory},
 journal = {Autonomous Robots},
 number = {2-3},
 pages = {171--186},
 publisher = {Springer},
 title = {Clustering sensor data for autonomous terrain identification using time-dependency},
 volume = {26},
 year = {2009}
}

@inproceedings{GirdharCRV2009,
 abstract = {In this paper we present a framework for automatically generating a visual synopsis of the images observed as a robot follows a trajectory. These ``navigation summaries'' are a set of images that are meant to capture the notable visual events observed by the robot independent of task- specific constraints. These small set of images character- ize the visual appearance of the trajectory of a robot. In our effort to create such summaries we use several different functional criteria while choosing among candidate images and then combine these into a single measurement, tuned to make our decision process approximate the selections that might be made by human observers. Our approach also takes into account the location information of the photos to make sure that the summary images are well distributed in the world. The tuning with respect to human performance is attained by collecting human performance data on a similar task using a web-based interface and setting the parameters of our automated system to match the behaviour of the hu- man observers.},
 address = {Kelowna, British Columbia},
 author = {Yogesh Girdhar and Gregory Dudek},
 booktitle = {CRV '09: Proceedings of the 2009 Canadian Conference on Computer and Robot Vision},
 date-added = {2009-10-24 15:50:44 -0400},
 date-modified = {2010-06-22 09:53:52 -0400},
 month = {May},
 pages = {292--298},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/crv2009.pdf},
 publisher = {IEEE Computer Society},
 title = {Optimal Online Data Sampling or How to Hire the Best Secretaries},
 year = {2009}
}

@inproceedings{GiguereCRV09,
 abstract = {We describe a navigation and coverage system based on unsupervised learning driven by visual input. Our objective is to allow a robot to remain continuously moving above a terrain of interest using visual feedback to avoid leaving this region. As a particular application domain, we are interested in doing this in open water, but the approach makes few domain-specific assumptions. Specifically, our system employed an unsupervised learning technique to train a k-Nearest Neighbor classifier to distinguish between images of different terrain types through image segmentation. A simple random exploration strategy was used with this classifier to allow the robot to collect data while remaining confined above a coral reef, without the need to maintain pose estimates. We tested the technique in simulation, and a live deployment was conducted in open water. During the latter, the robot successfully navigated autonomously above a coral reef during a 20 minutes period. },
 address = {Kelowna, British Columbia},
 author = {Philippe Giguere and Gregory Dudek and Christopher Prahacs and Nicolas Plamondon and Katrine Turgeon},
 booktitle = {Canadian Conference on Computer and Robot Vision},
 month = {May},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/crv2009_1.pdf},
 title = {Unsupervised Learning of Terrain Appearance for Automated Coral Reef Exploration},
 year = {2009}
}

@inproceedings{pomerantzD09,
 abstract = {We use a hierarchical Bayesian approach to model user preferences in different contexts or settings. Unlike many previous recommenders, our approach is content-based. We assume that for each context, a user has a different set of preference weights which are linked by a common, ``generic context" set of weights. The approach uses Expectation Maximization (EM) to estimate both the generic context weights and the context specific weights. This improves upon many current recommender systems that do not incorporate context into the recommendations they provide. In this paper, we show that by considering contextual information, we can improve our recommendations, demonstrating that it is useful to consider context in giving ratings. Because the approach does not rely on connecting users via collaborative filtering, users are able to interpret contexts in different ways and invent their own contexts.},
 author = {Daniel Pomerantz and Gregory Dudek},
 bibsource = {DBLP, http://dblp.uni-trier.de},
 booktitle = {Canadian Conference on AI},
 crossref = {DBLP:conf/ai/2009},
 ee = {http://dx.doi.org/10.1007/978-3-642-01818-3_12},
 pages = {98-109},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ai2009ctxrecs.pdf},
 title = {Context Dependent Movie Recommendations Using a Hierarchical
Bayesian Model},
 year = {2009}
}

@inproceedings{GiguereIcra09,
 abstract = {This paper describes an approach to surface identification in the context of mobile robotics, applicable to supervised and unsupervised learning. The identification is based on analyzing the tip acceleration patterns induced in a metallic rod, dragged along a surface that is to be identified. Eight features in time and frequency domains are used for classification. Results show that for ten type of indoor and outdoor surfaces, reliable identification can be achieved 90.0 and 94.6 % for a 1 and 4 seconds time-window, respectively), using a non-sophisticated classifier (artificial neural network). Demonstration is done on how such a sensor and a simple control strategy can be used to guide a blind robot, using a simulation and a real differential drive robot. },
 address = {Kobe, Japan},
 author = {Philippe Giguere and Gregory Dudek},
 booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation},
 month = {May},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/IcraRod2009.pdf},
 title = {Surface Identification Using Simple Contact Dynamics for Mobile Robots},
 year = {2009}
}

@inproceedings{sattar2009underwater,
 author = {Sattar, Junaed and Dudek, Gregory},
 booktitle = {Robotics: Science and Systems},
 title = {Underwater human-robot interaction via biological motion identification.},
 year = {2009}
}

@inproceedings{5230500,
 author = {J. {Sattar} and G. {Dudek}},
 booktitle = {2009 Canadian Conference on Computer and Robot Vision},
 number = {},
 pages = {329-336},
 title = {A Vision-Based Control and Interaction Framework for a Legged Underwater Robot},
 volume = {},
 year = {2009}
}

@inproceedings{5152197,
 author = {J. {Sattar} and G. {Dudek}},
 booktitle = {2009 IEEE International Conference on Robotics and Automation},
 number = {},
 pages = {3583-3588},
 title = {Robust servo-control for underwater robots using banks of visual filters},
 volume = {},
 year = {2009}
}

@inproceedings{Meger09_icra,
 abstract = {In this paper we present an approach for localizing a sensor network augmented with a mobile robot which is capable of providing inter-sensor pose estimates through its odometry measurements. We present a stochastic algorithm that samples efficiently from the probability distribution for the pose of the sensor network by employing Rao-Blackwellization and a proposal scheme which exploits the sequential nature of odometry measurements. Our algorithm automatically tunes itself to the problem instance and includes a principled stopping mechanism based on convergence analysis. We demonstrate the favourable performance of our approach compared to that of established methods via simulations and experiments on hardware. },
 address = {Kobe, Japan},
 author = {D. Meger and D. Marinakis and I. Rekleitis and G. Dudek},
 booktitle = {IEEE Intl. Conf. on Robotics and Automation},
 month = {May},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra09.pdf},
 title = {Inferring a Probability Distribution Function for the Pose of a Sensor Network using a Mobile Robot},
 year = {2009}
}

@article{Marinakis09_IMAVIS,
 abstract = {When a network of vision-based sensors is emplaced in an environment for applications such as surveillance or monitoring the spatial relationships between the sensing units must be inferred or computed for self-calibration purposes. In this paper we describe a technique to solve one aspect of this self-calibration problem: automatically determining the topology and connectivity information of a network of cameras based on a statistical analysis of observed motion in the environment. While the technique can use labels from reliable cameras systems, the algorithm is powerful enough to function using ambiguous tracking data. The method requires no prior knowledge of the relative locations of the cameras and operates under very weak environmental assumptions. Our approach stochastically samples plausible agent trajectories based on a delay model that allows for transitions to and from sources and sinks in the environment. The technique demonstrates considerable robustness both to sensor error and non-trivial patterns of agent motion. The output of the method is a Markov model describing the behavior of agents in the system and the underlying traffic patterns. The concept is demonstrated with simulation data for systems containing up to ten agents and verified with experiments conducted on a six camera sensor network. },
 author = {D. Marinakis and G. Dudek},
 journal = {Image and Vision Computing},
 month = {January},
 pages = {116-130},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/imavis09.pdf},
 title = {Self-calibration of a vision-based sensor network},
 volume = {27},
 year = {2009}
}

@article{sattar2009ijrr,
 abstract = { In this paper, we evaluate a set of core functions that allow an underwater robot to perform surveillance under operator control. Specifically, we are interested in behaviors that facilitate the monitoring of organisms on a coral reef, and we present behaviors and interaction modes for a small underwater robot. In particular, we address some challenging issues arising from the underwater environment: visual processing, interactive communication with an underwater crew and, finally, orientation and motion of the vehicle through a hovering mode. The visual processing consists of target tracking using various techniques (color segmentation, color histogram and mean shift). Communication underwater is achieved through printed cards with robustly identifiable visual markers on them. Finally, the hovering gait developed for this vehicle relies on the planned motion of six flippers to generate the appropriate forces. },
 author = {Junaed Sattar and Philippe Giguère and Gregory Dudek},
 doi = {10.1177/0278364908098560},
 eprint = {https://doi.org/10.1177/0278364908098560},
 journal = {The International Journal of Robotics Research},
 number = {6},
 pages = {701-713},
 title = {Sensor-based Behavior Control for an Autonomous Underwater Vehicle},
 url = {https://doi.org/10.1177/0278364908098560},
 volume = {28},
 year = {2009}
}

@article{torres2008ijcv,
 abstract = {
In this article we present a method for automatically recovering complete and dense depth maps of an indoor environment by fusing incomplete data for the 3D environment modeling problem. The geometry of indoor environments is usually extracted by acquiring a huge amount of range data and registering it. By acquiring a small set of intensity images and a very limited amount of range data, the acquisition process is considerably simplified, saving time and energy consumption. In our method, the intensity and partial range data are registered first by using an image-based registration algorithm. Then, the missing geometric structures are inferred using a statistical learning method that integrates and analyzes the statistical relationships between the visual data and the available depth on terms of small patches. Experiments on real-world data on a variety of sampling strategies demonstrate the feasibility of our method.},
 address = {USA},
 author = {Torres-M\'{e}ndez, Luz A. and Dudek, Gregory},
 doi = {10.1007/s11263-007-0108-2},
 issn = {0920-5691},
 issue_date = {August    2008},
 journal = {Int. J. Comput. Vision},
 keywords = {3D environment modeling, Sensor fusion, Markov random fields},
 month = {aug},
 number = {2},
 numpages = {22},
 pages = {137–158},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Torres-Méndez-Dudek2008_Article_Inter-ImageStatisticsFor3DEnvi.pdf},
 publisher = {Kluwer Academic Publishers},
 title = {Inter-Image Statistics for 3D Environment Modeling},
 url = {https://doi.org/10.1007/s11263-007-0108-2},
 volume = {79},
 year = {2008}
}

@inproceedings{Sattar08iros,
 abstract = {Underwater operations present unique challenges and opportunities for robotic applications. These can be attributed in part to limited sensing capabilities, and to locomotion behaviours requiring control schemes adapted to specific tasks or changes in the environment. From enhancing teleoperation procedures, to providing high-level instruction, all the way to fully autonomous operations, enabling autonomous capabilities is fundamental for the successful deployment of underwater robots. This paper presents an overview of the approaches used during underwater sea trials in the coral reefs of Barbados, for two amphibious mobile robots and a set of underwater sensor nodes. We present control mechanisms used for maintaining a preset trajectory during enhanced teleoperations and discuss their experimental results. This is followed by a discussion on amphibious data gathering experiments conducted on the beach. We then present a tetherless underwater communication approach based on pure vision for high-level control of an underwater vehicle. Finally the construction details together with preliminary results from a set of distributed underwater sensor nodes are outlined. },
 address = {Nice, France},
 author = {Junaed Sattar and Gregory Dudek and Olivia Chiu and Ioannis Rekleitis and Philippe Gigu\`ere and Alec Mills and Nicolas Plamondon and Chris Prahacs and Yogesh Girdhar and Meyer Nahon and John-Paul Lobos},
 booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, {IROS}},
 month = {September},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros2008Aqua.pdf},
 title = {Enabling Autonomous Capabilities in Underwater Robotics},
 year = {2008}
}

@inproceedings{Giguere-RSS08,
 abstract = {In this paper we are interested in autonomous systems that can automatically develop terrain classifiers without human interaction or feedback. A key issue is clustering of sensor data from the same terrain. In this context, we present a novel off-line windowless clustering algorithm exploiting time-dependency between samples. In terrain coverage, sets of sensory measurements are returned that are spatially, and hence temporally correlated. Our algorithm works by finding a set of parameter values for a user-specified classifier that minimize a cost function. This cost function is related to change in classifier probability outputs over time. The main advantage over other existing methods is its ability to cluster data for fast-switching systems that either have high process or observation noise, or complex distributions that cannot be properly characterized within the average duration of a state. The algorithm was evaluated using three different classifiers (linear separator, mixture of Gaussians and k-Nearest Neighbor), over both synthetic data sets and mobile robot contact feedback sensor data, with success. },
 address = {Zurich, Switzerland},
 author = {Philippe Giguere and Gregory Dudek},
 booktitle = {Proceedings of Robotics: Science and Systems IV},
 month = {June},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/rss2008.pdf},
 title = {Clustering Sensor Data for Terrain Identification using a Windowless Algorithm},
 year = {2008}
}

@inproceedings{Sattar08Iser,
 abstract = {We present an application of the ensemble learning algorithm in the area of visual tracking and servoing. In particular, we investigate an approach based on the Boosting technique for robust visual tracking of color objects in an underwater environment. To this end, we use AdaBoost, the most common variant of the Boosting algorithm, to select a number of low-complexity but moderately accurate color feature trackers and we combine their outputs. From a significantly large number of ``weak'' color trackers, the training process selects those which exhibit reasonably good performance (in terms of mistracking and false positives), and assigns positive weights to these trackers. The tracking process applies these trackers on the input video frames, and the final tracker output is chosen based on the weights of the final array of trackers. By using computationally inexpensive but somewhat accurate trackers as members of the ensemble, the system is able to run at quasi-real time, and thus, is deployable on-board our underwater robot. We present quantitative cross-validation results of our visual tracker, and conclude by pointing out some difficulties faced and subsequent shortcomings in the experiments we performed, along with directions of future research on the area of ensemble tracking in real-time. },
 address = {Athens, Greece},
 author = {Junaed Sattar and Gregory Dudek},
 booktitle = {Proceedings of the 11th International Symposium on Experimental Robotics, {ISER}},
 month = {July},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iser2008final.pdf},
 title = {A Boosting Approach to Visual Servo-Control of an Underwater Robot},
 year = {2008}
}

@inproceedings{SattarICRA08,
 abstract = {A gesture-based interaction framework is presented for controlling mobile robots. This natural interaction paradigm has few physical requirements, and thus can be deployed in many restrictive and challenging environments. We present an implementation of this scheme in the control of an underwater robot by an on-site human operator. The operator performs discrete gestures using engineered visual targets, which are interpreted by the robot as parametrized actionable commands. By combining the symbolic alphabets resulting from several visual cues, a large vocabulary of statements can be produced. An Iterative Closest Point algorithm is used to detect these observed motions, by comparing them with an established database of gestures. Finally, we present quantitative data collected from human participants indicating accuracy and performance of our proposed scheme. },
 address = {Pasadena, California},
 author = {Anqi Xu and Gregory Dudek and Junaed Sattar},
 booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation, {ICRA}},
 month = {May},
 pages = { 3557-3563},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra2008.pdf},
 title = {A Natural Gesture Interface for Operating Robotic Systems},
 year = {2008}
}

@article{MarinakisDudek08_TROS,
 abstract = {We present a method for inferring the topology of a sensor network given non-discriminating observations of activity in the monitored region. This is accomplished based on no prior knowledge of the relative locations of the sensors and weak assumptions regarding environmental conditions. Our approach employs a two-level reasoning system made up of a stochastic Expectation Maximization algorithm and a higher level search strategy employing the principle of Occam's Razor to look for the simplest solution explaining the data. The result of the algorithm is a Markov model describing the behavior of agents in the system and the underlying traffic patterns. Numerical simulations and experimental assessment conducted on a real sensor network suggest that the technique could have promising real world applications in the area of sensor network self-configuration. },
 author = {D. Marinakis and G. Dudek},
 journal = {IEEE Transactions on Robotics},
 month = {April},
 number = {2},
 pages = {293 - 306},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ieeeTrans08.pdf},
 title = {Occam's Razor Applied to Network Topology Inference},
 volume = {24},
 year = {2008}
}

@inproceedings{jsgd_iros07,
 abstract = {We present an algorithm for underwater robots to track mobile targets, and specifically human divers, by detecting periodic motion. Periodic motion is typically associated with propulsion underwater and specifically with the kicking of human swimmers. By computing local amplitude spectra in a video sequence, we find the location of a diver in the robot’s field of view. We use the Fourier transform to extract the responses of varying intensities in the image space over time to detect characteristic low frequency oscillations to identify an undulating flipper motion associated with typical gaits. In case of detecting multiple locations that exhibit large lowfrequency energy reponses, we combine the gait detector with other methods to eliminate false detections. We present results of our algorithm on open-ocean video footage of swimming divers, and also discuss possible extensions and enhancements of the proposed approach for tracking other objects that exhibit low-frequency oscillatory motion. },
 address = {San Diego, California, USA},
 author = {Junaed Sattar and Gregory Dudek},
 booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems {(IROS07)}},
 month = {October},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros07.pdf},
 title = {Where is your dive buddy: tracking humans underwater using spatio-temporal features},
 year = {2007}
}

@article{4069194,
 author = {G. {Dudek} and P. {Giguere} and C. {Prahacs} and S. {Saunderson} and J. {Sattar} and L. {Torres-Mendez} and M. {Jenkin} and A. {German} and A. {Hogue} and A. {Ripsman} and J. {Zacher} and E. {Milios} and H. {Liu} and P. {Zhang} and M. {Buehler} and C. {Georgiades}},
 journal = {Computer},
 number = {1},
 pages = {46-53},
 title = {AQUA: An Amphibious Autonomous Robot},
 volume = {40},
 year = {2007}
}

@inproceedings{jsebpggd_crv07,
 abstract = {In this paper we introduce the Fourier tag, a synthetic fiducial marker used to visually encode information and provide controllable positioning. The Fourier tag is a synthetic target akin to a bar-code that specifies multi-bit information which can be efficiently and robustly detected in an image. Moreover, the Fourier tag has the beneficial property that the bit string it encodes has variable length as a function of the distance between the camera and the target. This follows from the fact that the effective resolution decreases as an effect of perspective. This paper introduces the Fourier tag, describes its design, and illustrates its properties experimentally. },
 address = {Montreal, Quebec, Canada},
 author = {Junaed Sattar and Eric Bourque and Philippe Giguere and Gregory Dudek},
 booktitle = {Fourth Canadian Conference on Computer and Robot Vision (CRV)},
 month = {May},
 pages = {165-174},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/crv07.pdf},
 title = {Fourier tags: Smoothly degradable fiducial markers for use in human-robot interaction},
 year = {2007}
}

@inproceedings{gdjsax_cra07,
 abstract = {We describe an interaction paradigm for controlling a robot using hand gestures. In particular, we are interested in the control of an underwater robot by an onsite human operator. Under this context, vision-based control is very attractive, and we propose a robot control and programming mechanism based on visual symbols. A human operator presents engineered visual targets to the robotic system, which recognizes and interprets them. This paper describes the approach and proposes a specific gesture language called “RoboChat”. RoboChat allows an operator to control a robot and even express complex programming concepts, using a sequence of visually presented symbols, encoded into fiducial markers. We evaluate the efficiency and robustness of this symbolic communication scheme by comparing it to traditional gesture-based interaction involving a remote human operator. },
 address = {Rome, Lazio, Italy},
 author = {Gregory Dudek and Junaed Sattar and Anqi Xu},
 booktitle = {IEEE International Conference on Robotics and Automation {(ICRA07)}},
 month = {April},
 pages = {2507-2513},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra07.pdf},
 title = {A Visual Language for Robot Control and Programming: A Human-Interface Study},
 year = {2007}
}

@inproceedings{Marinakis07_aaai,
 abstract = {In this paper, we consider a hybrid solution to the sensor network position inference problem, which combines a real-time filtering system with information from a more expensive, global inference procedure to improve accuracy and prevent divergence. Many online solutions for this problem make use of simplifying assumptions, such as Gaussian noise models and linear system behaviour and also adopt a filtering strategy which may not use available information optimally. These assumptions allow near real-time inference, while also limiting accuracy and introducing the potential for ill-conditioning and divergence. We consider augmenting a particular real-time estimation method, the extended Kalman filter (EKF), with a more complex, but more highly accurate, inference technique based on Markov Chain Monte Carlo (MCMC) methodology. Conventional MCMC techniques applied to this problem can entail significant and time consuming computation to achieve convergence. To address this, we propose an intelligent bootstrapping process and the use of parallel, communicative chains of different temperatures, commonly referred to as parallel tempering. The combined approach is shown to provide substantial improvement in a realistic simulated mapping environment and when applied to a complex physical system involving a robotic platform moving in an office environment instrumented with a camera sensor network. },
 address = {Vancouver, Canada},
 author = {Dimitri Marinakis and David Meger and Ioannis Rekleitis and Gregory Dudek},
 booktitle = {AAAI National Conference on Artificial Intelligence},
 month = {July},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/aaai07_selfloc.pdf},
 title = {Hybrid Inference for Sensor Network Localization using a Mobile Robot},
 year = {2007}
}

@inproceedings{Dudek07_aaai,
 abstract = {In this paper, we consider the exploration of topological environments by a robot with weak sensory capabilities. We assume only that the robot can recognize when it has reached a vertex, and can assign a cyclic ordering to the edges leaving a vertex with reference to the edge it arrived from. Given this limited sensing capability, and without the use of any markers or additional information, we will show that the construction of a topological map is still feasible. This is accomplished through both the exploration strategy which is designed to reveal model inconsistencies and by a search process that maintains a bounded set of believable world models throughout the exploration process. Plausible models are selected through the use of a ranking heuristic function based on the principle of Occam's Razor. We conclude with numerical simulations demonstrating the performance of the algorithm. },
 address = {Vancouver, Canada},
 author = {G. Dudek and D. Marinakis},
 booktitle = {AAAI National Conference on Artificial Intelligence},
 month = {July},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/aaai07_topo.pdf},
 title = {Topological Mapping with Weak Sensory Data},
 year = {2007}
}

@inproceedings{MarinakisGiguereDudek07_canAI,
 abstract = {In this paper, we present an approach for recovering a topological map of the environment using only detection events from a deployed sensor network. Unlike other solutions to this problem, our technique operates on timestamp free observational data; i.e. no timing information is exploited by our algorithm except the ordering. We first give a theoretical analysis of this version of the problem, and then we show that by considering a sliding window over the observations, the problem can be re-formulated as a version of set-covering. We present two heuristics based on this set-covering formulation and evaluate them with numerical simulations. The experiments demonstrate that promising results can be obtained using a greedy algorithm. },
 address = {Montreal, Canada},
 author = {D. Marinakis and P. Giguere and G. Dudek},
 booktitle = {In Proceedings of the Canadian Conference on Artificial Intelligence},
 month = {May},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/canAI07.pdf},
 title = {Learning Network Topology from Simple Sensor Data},
 year = {2007}
}

@inproceedings{MarinakisDudek2007_ijcai,
 abstract = {In this paper we address the problem of inferring the topology, or inter-node navigability, of a sensor network given non-discriminating observations of activity in the environment. By exploiting motion present in the environment, our approach is able to recover a probabilistic model of the sensor network connectivity graph and the underlying traffic trends. We employ a reasoning system made up of a stochastic Expectation Maximization algorithm and a higher level search strategy employing the principle of Occam's Razor to look for the simplest solution explaining the data. The technique is assessed through numerical simulations and experiments conducted on a real sensor network. },
 address = {Hyderabad, India},
 author = {D. Marinakis and G. Dudek},
 booktitle = {International Joint Conference on Artificial Intelligence},
 month = {Jan.},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ijcai07.pdf},
 title = {Topological Mapping through Distributed, Passive Sensors},
 year = {2007}
}

@inproceedings{Giguere_IROS_2006,
 abstract = {In order to better understand the behavior of the underwater robot developed at our laboratory, a simple but relatively good model of the underwater behavior of the robot had to be developed. In order to be useful for model-based control techniques onboard the robot, the model had to have low computing requirements, yet to be complex enough to capture the transient response of the robot. To achieve this, a system identification approach was taken by first capturing the robot response to various inputs, and then matching them to a simple model. },
 author = {Philippe Giguere and Gregory Dudek and Chris Prahacs},
 booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
 month = {Oct.},
 pages = {3000-3005},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros06.pdf},
 title = {Characterization and Modeling of Rotational Responses for an Oscillating Foil Underwater Robot},
 year = {2006}
}

@inproceedings{Giguere-RSS-06,
 abstract = {In this paper, we explore the idea of using inertial and actuator information to accurately identify the environment of an amphibious robot. In particular, in our work with a legged robot we use internal sensors to measure the dynamics and interaction forces experienced by the robot. From these measurements we use simple machine learning methods to probabilistically infer properties of the environment, and therefore identify it. The robot's gait can then be automatically selected in response to environmental changes. Experimental results show that for several environments (sand, water, snow, ice, etc.), the identification process is over 90 % accurate. The requisite data can be collected during a half-leg rotation (about 250 ms), making it one of the fastest and most economical environment identifiers for a dynamic robot. For the littoral setting, a gait-change experiment is done as a proof-of-concept of a robot automatically adapting its gait to suit the environment. },
 address = {Philadelphia, USA},
 author = {P. Giguere and G. Dudek and S. Saunderson and C. Prahacs},
 booktitle = {Proceedings of Robotics: Science and Systems},
 month = {August},
 pages = {271-278},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/rss06.pdf},
 title = {Environment Identification for a Running Robot Using Inertial and Actuator Cues},
 year = {2006}
}

@inproceedings{torres2006aiai,
 abstract = {In mobile robotics, the inference of the 3D layout of large-scale indoor environments is a critical problem for achieving exploration and navigation tasks. This article presents a framework for building a 3D model of an indoor environment from partial data using a mobile robot. The modeling of a large-scale environment involves the acquisition of a huge amount of range data to extract the geometry of the scene. This task is physically demanding and time consuming for many real systems. Our approach overcomes this problem by allowing a robot to rapidly collect a set of intensity images and a small amount of range information. The method integrates and analyzes the statistical relationships between the visual data and the limited available depth on terms of small patches and is capable of recovering complete dense range maps. Experiments on real-world data are given to illustrate the suitability of our approach.},
 address = {Berlin, Heidelberg},
 author = {Torres-M{\'e}ndez, Luz A. and Dudek, Gregory},
 booktitle = {MICAI 2006: Advances in Artificial Intelligence},
 editor = {Gelbukh, Alexander and Reyes-Garcia, Carlos Alberto},
 isbn = {978-3-540-49058-6},
 pages = {715--725},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/micai06_latm.pdf},
 publisher = {Springer Berlin Heidelberg},
 title = {Statistics of Visual and Partial Depth Data for Mobile Robot Environment Modeling},
 year = {2006}
}

@inproceedings{jsgd_icra06,
 abstract = {In this paper, we present behaviors and interaction modes for a small underwater robot. In particular, we address some challenging issues arising from the underwater environment: visual processing, interactive communication with an underwater crew, and finally orientation and motion of the vehicle through a hovering mode. The visual processing consist of target tracking using various technique (color blob, color histogram and mean shift). The underwater communication is achieved through printed cards with virtual markers (ARTag). Finally, the hovering "gait" developed for this vehicle relies on the planned motion of six flippers to generate the appropriate forces. },
 address = {Rio de Janeiro, Brazil},
 author = {Gregory Dudek and Philippe Giguere and Junaed Sattar},
 booktitle = {International Symposium on Experimental Robotics{(ISER06)}},
 month = {July},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iser06.pdf},
 title = {SensorBased Behavior Control for an Autonomous Underwater Vehicle},
 year = {2006}
}

@article{theberge2006spectrum,
 author = {Michelle Theberge and Gregory Dudek},
 journal = {IEEE Spectrum},
 month = {June},
 pages = {38-43},
 title = {Gone Swimmin'},
 year = {2006}
}

@article{REKLEITIS2006921,
 abstract = {In this paper we examine issues of localization, exploration, and planning in the context of a hybrid robot/camera-network system. We exploit the ubiquity of camera networks to use them as a source of localization data. Since the Cartesian position of the cameras in most networks is not known accurately, we consider the issue of how to localize such cameras. To solve this hybrid localization problem, we divide it into a local problem of camera-parameter estimation combined with a global planning and navigation problem. We solve the local camera-calibration problem by using fiducial markers attached to the robot and by selecting robot trajectories in front of each camera that provide good calibration and field-of-view accuracy. We propagate information among the cameras and the successive positions of the robot using an Extended Kalman filter. Finally, we move the robot between the camera positions to explore the network using heuristic exploration strategies. The paper includes experimental data from an indoor office environment as well as tests on simulated data sets.},
 author = {Ioannis Rekleitis and David Meger and Gregory Dudek},
 doi = {https://doi.org/10.1016/j.robot.2006.05.009},
 pdf = {https://doi.org/10.1016/j.robot.2006.05.009},
 issn = {0921-8890},
 journal = {Robotics and Autonomous Systems},
 keywords = {Sensor networks, Cooperative localization},
 note = {Planning Under Uncertainty in Robotics},
 number = {11},
 pages = {921 - 932},
 title = {Simultaneous planning, localization, and mapping in a camera sensor network},
 url = {http://www.sciencedirect.com/science/article/pii/S0921889006000996},
 volume = {54},
 year = {2006}
}

@inproceedings{meger2006dars,
 address = {},
 author = {David Meger and Ioannis Rekleitis and Gregory Dudek},
 booktitle = {Proc. of The 8th International Symposium on Distributed Autonomous Robotic Systems (DARS)},
 pages = {155-164},
 publisher = {},
 series = {},
 title = {Simultaneous Planning, Localization, and Mapping in a Camera Sensor Network},
 pdf = {https://link.springer.com/chapter/10.1007/4-431-35881-1_16},
 year = {2006}
}

@inproceedings{GardenDudek2006,
 abstract = {We describe a recommender system which uses a unique combination of content-based and collaborative methods to suggest items of interest to users, and also to learn and exploit item semantics. Recommender systems typically use techniques from collaborative filtering, in which proximity measures between users are formulated to generate recommendations, or content-based filtering, in which users are compared directly to items. Our approach uses similarity measures between users, but also directly measures the attributes of items that make them appealing to specific users. This can be used to directly make recommendations to users, but equally importantly it allows these recommendations to be justified. We introduce a method for predicting the preference of a user for a movie by estimating the user's attitude toward features with which other users have described that movie. We show that this method allows for accurate recommendations for a sub-population of users, but not for the entire user population. We describe a hybrid approach in which a user-specific recommendation mechanism is learned and experimentally evaluated. It appears that such a recommender system can achieve significant improvements in accuracy over alternative methods, while also retaining other advantages. },
 address = {Boston, Massachusetts},
 author = {Matthew Garden and Gregory Dudek},
 booktitle = {Proceedings of the 21st AAAI National Conference on Artificial Intelligence (AAAI'06)},
 month = {July},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/AAAI06GardenM.pdf},
 title = {Mixed Collaborative and Content-Based Filtering with User-Contributed Semantic Features},
 year = {2006}
}

@inproceedings{MarinakisDudek06_aaai,
 abstract = {This paper describes a technique for the probabilistic self-localization of a sensor network based on noisy inter-sensor range data. Our method is based on a number of parallel instances of Markov Chain Monte Carlo (MCMC). By combining estimates drawn from these parallel chains, we build up a representation of the underlying probability distribution function (PDF) of the network pose. Our approach includes sensor data incrementally in order to avoid local minima and is shown to produce meaningful results efficiently. We return a distribution over sensor locations rather than a single maximum likelihood estimate. This can then be used for subsequent exploration and validation. },
 address = {Boston, Massachusetts},
 author = {D. Marinakis and G. Dudek},
 booktitle = {AAAI National Conference on Artificial Intelligence},
 month = {July},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/aaai06.pdf},
 title = {Probabilistic Self-Localization for Sensor Networks},
 year = {2006}
}

@inproceedings{JohnsDudek06_crv,
 abstract = {We consider the problem of vision-based position estimation in urban environments. In particular, we are interested in position estimation from visual cues, but using only limited computational resources. Our particular solution to this problem is based on representing the variability of the "horizon" of the cityscape when seen from within the city; that is, the outlines of the rooftops of adjacent buildings. By encoding the image using only such a one-dimensional contour, we obtain an image encoding that is exceedingly compact. This, in turn, allows us to both efficiently transmit this representation to a remote "recognition engine" as well as allowing for an efficient storage and matching process. We outline our approach and representation, and provide experimental data supporting its feasibility. },
 address = {Quebec City, Quebec},
 author = {D. Johns and G. Dudek},
 booktitle = {Canadian Conference on Computer and Robot Vision},
 month = {June},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Johns_Dudek_CRV06.pdf},
 title = {Urban Position Estimation from One Dimensional Visual Cues},
 year = {2006}
}

@inproceedings{burfoot2006rrt,
 abstract = {We propose a randomized STRIPS planning algorithm called RRT-Plan. This planner is inspired by the idea of Rapidly exploring Random Trees, a concept originally designed for use in continuous path planning problems. Issues that arise in the conversion of RRTs from continuous to discrete spaces are discussed, and several additional mechanisms are proposed to improve performance. Our experimental results indicate that RRT-Plan is competitive with the state of the art in STRIPS planning.},
 author = {Burfoot, Daniel and Pineau, Joelle and Dudek, Gregory},
 booktitle = {ICAPS},
 pages = {362--365},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icaps06.pdf},
 title = {RRT-Plan: A Randomized Algorithm for STRIPS Planning.},
 year = {2006}
}

@mastersthesis{burfoot2006,
 abstract = {This thesis explores limitations of heuristic search planning, and presents techniques to overcome those limitations. The two halves of the thesis discuss problems in standard propositional planning (STRIPS) and in planning with numeric state variables respectively.

In the context of STRIPS, the primary focus is on the widely used relaxed plan heuristic ($h^{+}$). A variety of cases are shown in which $h^{+}$ provides systematically bad estimates of goal distance. To address this breakdown, a planning system called RRT-Plan is presented. This system is inspired by the concept of Rapidly-exploring Random Trees, which was originally developed for use in mobile robot path planning. Experimental results show that RRT-Plan is comparable to leading planners in terms of number of problems solved and plan quality. We conclude that the effectiveness of RRT-Plan is based on its ability to search the space of artificial goal orderings.

The second half of the work considers heuristic search planning in numeric domains. Two particularly significant obstacles are identified. The Curse of Affluence is due to the vast blowup in the search space caused by the addition of numeric variables. The Curse of Poverty relates to the difficulty of finding relevant lower bounds on resource consumption.

Exploration of the Curse of Affluence leads to the new concepts of reduced search and enhanced states. In reduced search, certain simple operators are not used to expand states. Instead, enhanced states are constructed which represent all possible states which could be achieved by suitably inserting simple operators in the plan. Enhanced states are represented by a set of constant discrete variables, and a convex hull of numeric values. This representation can be queried and updated in a natural way. Experimental results show that there are domains for which reduced search gives order of magnitude performance improvements over Metric-FF, a leading heuristic search planner for numeric domains.

},
 author = {Daniel Burfoot},
 document_type = {mastersthesis},
 month = {August},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/thesis.pdf},
 school = {McGill University},
 title = {Limitations of and Extensions to Heuristic Search Planning},
 year = {2006}
}

@inproceedings{MarinakisDudek06_icra,
 abstract = {When a network of robots or static sensors is emplaced in an environment, the spatial relationships between the sensing units must be inferred or computed for most key applications. In this paper we present a Monte Carlo Expectation Maximization algorithm for recovering the connectivity information (i.e. topological map) of a network using only detection events from deployed sensors. The technique is based on stochastically reconstructing samples of plausible agent trajectories allowing for the possibility of transitions to and from sources and sinks in the environment. We demonstrate robustness to sensor error and non-trivial patterns of agent motion. The result of the algorithm is a probabilistic model of the sensor network connectivity graph and the underlying traffic trends. We conclude with results from numerical simulations and an experiment conducted with a heterogeneous sensor network. },
 address = {Orlando, Florida},
 author = {D. Marinakis and G. Dudek},
 booktitle = {IEEE Intl. Conf. on Robotics and Automation},
 month = {May},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra06.pdf},
 title = {A Practical Algorithm for Network Topology Inference},
 year = {2006}
}

@inproceedings{jsgd_icra06,
 abstract = {We consider the use of visual target tracking for autonomous steering of an underwater robot. In this context, we consider a performance comparison for three key visual tracking algorithms used for servo control. We present a comparative study of the performance in underwater environments of three tracking algorithms that are widely used in vision applications. Variations in illumination, suspended particles and a resulting reduction in visibility hinders vision systems from performing satisfactorily in marine environments; at least not as well as they do in terrestrial (i.e. non-underwater) surroundings. Our work focuses on quantitatively measuring the performance of three color-based tracking algorithms– color blob tracker, color histogram tracker and mean-shift tracker, in tracking objects underwater in different levels lighting and visibility. We also present results demonstrating the effect of suspended particles underwater, and in conclusion we summarize the three tracking algorithms by comparing their pros and cons. },
 address = {Orlando, Florida},
 author = {Junaed Sattar and Gregory Dudek},
 booktitle = {International Conference on Robotics and Automation {(ICRA06)}},
 month = {May},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra06_1.pdf},
 title = {On the Performance of Color Tracking Algorithms for Underwater Robots under Varying Lighting and Visibility},
 year = {2006}
}

@mastersthesis{sattar2005visual,
 author = {Sattar, Junaed},
 school = {McGill University},
 title = {A Visual Servoing System for an Amphibious Legged Robot},
 year = {2005}
}

@article{prahacs2004towards,
 abstract = {New areas of research focus on bridging the gap between mobile robotics on land and at sea. This paper describes the evolution of RHex, a power-autonomous legged land-based robot, into one capable of both sea and land-based locomotion. In working towards an amphibious robot, three versions of RHex with increasing levels of aquatic capability were created. While all three platforms share the same underlying software, electronic and mechanical architectures, varying emphasis on aspects of similar design criteria resulted in the development of varied platforms with increasing ability of amphibious navigation.},
 author = {Prahacs, Chris and Saudners, Aaron and Smith, Matthew K and McMordie, Dave and Buehler, Martin},
 journal = {Journal of Engineering Design and Innovation},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/jedi05CP.pdf},
 title = {Towards Legged Amphibious Mobile Robotics},
 volume = {1P},
 year = {2005}
}

@inproceedings{MarinakisDudek05_nipsws,
 address = {Whistler, Canada},
 author = {D. Marinakis and G. Dudek},
 booktitle = {Neural Information Processing Systems Workshop: Intelligence Beyond the Desktop},
 month = {Dec},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/nipsws05.pdf},
 title = {Sensor Network Topology Inference},
 year = {2005}
}

@inproceedings{torres2005cvpr,
 abstract = {In this paper, we consider the problem of color restoration using statistical priors. This is applied to color recovery for underwater images, using an energy minimization formulation. Underwater images present a challenge when trying to correct the blue-green monochrome look to bring out the color we know marine life has. For aquatic robot tasks, the quality of the images is crucial and needed in real-time. Our method enhances the color of the images by using a Markov Random Field (MRF) to represent the relationship between color depleted and color images. The parameters of the MRF model are learned from the training data and then the most probable color assignment for each pixel in the given color depleted image is inferred by using belief propagation (BP). This allows the system to adapt the color restoration algorithm to the current environmental conditions and also to the task requirements. Experimental results on a variety of underwater scenes demonstrate the feasibility of our method.},
 address = {Berlin, Heidelberg},
 author = {Torres-M{\'e}ndez, Luz A. and Dudek, Gregory},
 booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition},
 editor = {Rangarajan, Anand and Vemuri, Baba and Yuille, Alan L.},
 isbn = {978-3-540-32098-2},
 pages = {60--73},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/emmcvpr05_latm.pdf},
 publisher = {Springer Berlin Heidelberg},
 title = {Color Correction of Underwater Images for Aquatic Robot Inspection},
 year = {2005}
}

@article{torres2005statistical,
 author = {Torres-M{\'e}ndez, Luz A and Dudek, Gregory},
 issn = {1665-9899},
 journal = {Research on computer science},
 pages = {151-160},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/micai05_latm.pdf},
 title = {A statistical learning-based method for color correction of underwater images},
 volume = {17},
 year = {2005}
}

@inproceedings{torres2006micai,
 abstract = {In mobile robotics, the inference of the 3D layout of large-scale indoor environments is a critical problem for achieving exploration and navigation tasks. This article presents a framework for building a 3D model of an indoor environment from partial data using a mobile robot. The modeling of a large-scale environment involves the acquisition of a huge amount of range data to extract the geometry of the scene. This task is physically demanding and time consuming for many real systems. Our approach overcomes this problem by allowing a robot to rapidly collect a set of intensity images and a small amount of range information. The method integrates and analyzes the statistical relationships between the visual data and the limited available depth on terms of small patches and is capable of recovering complete dense range maps. Experiments on real-world data are given to illustrate the suitability of our approach.},
 address = {Berlin, Heidelberg},
 author = {Torres-M{\'e}ndez, Luz A. and Dudek, Gregory},
 booktitle = {MICAI 2006: Advances in Artificial Intelligence},
 editor = {Gelbukh, Alexander and Reyes-Garcia, Carlos Alberto},
 isbn = {978-3-540-49058-6},
 pages = {715--725},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/PhDThesis05_Abril.pdf},
 publisher = {Springer Berlin Heidelberg},
 title = {Statistics of Visual and Partial Depth Data for Mobile Robot Environment Modeling},
 year = {2006}
}

@inproceedings{1545282,
 author = {J. {Sattar} and P. {Giguere} and G. {Dudek} and C. {Prahacs}},
 booktitle = {2005 IEEE/RSJ International Conference on Intelligent Robots and Systems},
 number = {},
 pages = {1483-1488},
 title = {A visual servoing system for an aquatic swimming robot},
 volume = {},
 year = {2005}
}

@inproceedings{1545231,
 author = {G. {Dudek} and M. {Jenkin} and C. {Prahacs} and A. {Hogue} and J. {Sattar} and P. {Giguere} and A. {German} and  {Hui Liu} and S. {Saunderson} and A. {Ripsman} and S. {Simhon} and L. -. {Torres} and E. {Milios} and P. {Zhang} and I. {Rekletis}},
 booktitle = {2005 IEEE/RSJ International Conference on Intelligent Robots and Systems},
 number = {},
 pages = {3604-3609},
 title = {A visually guided swimming robot},
 volume = {},
 year = {2005}
}

@inproceedings{MarinakisDudek05_crv,
 abstract = {In this paper we describe a technique to infer the topology and connectivity information of a network of cameras based on observed motion in the environment. While the technique can use labels from reliable cameras systems, the algorithm is powerful enough to function using ambiguous tracking data. The method requires no prior knowledge of the relative locations of the cameras and operates under very weak environmental assumptions. Our approach stochastically samples plausible agent trajectories based on a delay model that allows for transitions to and from sources and sinks in the environment. The technique demonstrates considerable robustness both to sensor error and non-trivial patterns of agent motion. The output of the method is a Markov model describing the behavior of agents in the system and the underlying traffic patterns. The concept is demonstrated with simulation data and verified with experiments conducted on a six camera sensor network. },
 address = {Victoria, Canada},
 author = {D. Marinakis and G. Dudek},
 booktitle = {In Proc. of Canadian Conference on Computer and Robot Vision},
 month = {May},
 pages = {121-128},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/crv05.pdf},
 title = {Topology Inference for a Vision-Based Sensor Network},
 year = {2005}
}

@inproceedings{MarinakisDudekFleet2005_networktopology,
 abstract = {We consider the problem of inferring sensor positions and a topological (i.e. qualitative) map of an environment given a set of cameras with non-overlapping fields of view. In this way, without prior knowledge of the environment nor the exact position of sensors within the environment, one can infer the topology of the environment, and common traffic patterns within it. In particular, we consider sensors stationed at the junctions of the hallways of a large building. We infer the sensor connectivity graph and the travel times between sensors (and hence the hallway topology) from the sequence of events caused by unlabeled agents (i.e. people) passing within view of the different sensors. We do this based on a first-order semi-Markov model of the agent's behavior. The paper describes a problem formulation and proposes a stochastic algorithm for its solution. The result of the algorithm is a probabilistic model of the sensor network connectivity graph and the underlying traffic patterns. We conclude with results from numerical simulations. },
 address = {Barcelona, Spain},
 author = {D. Marinakis and G. Dudek and D. Fleet},
 booktitle = {IEEE Intl. Conf. on Robotics and Automation},
 month = {April},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra05.pdf},
 title = {Learning Sensor Network Topology through Monte Carlo Expectation Maximization},
 year = {2005}
}

@inproceedings{garden-dudek2005,
 abstract = {In this paper we discuss the Recommendz recommender system. This domain-independent system combines the advantages of collaborative and content-based filtering in a novel way. By allowing users to provide feedback not only about an item as a whole, but also properties of an item that motivated their opinion, increased performance seems to be achieved. The features used to describe items are specified by the users of the system rather than predetermined using manual knowledge-engineering. We describe a method for combining descriptive features and simple ratings, and provide a performance analysis.},
 address = {Hong Kong, China},
 author = {Matthew Garden and Gregory Dudek},
 booktitle = {Proceedings of the IEEE International Conference on e-Technology, e-Commerce, and e-Service (EEE05)},
 month = {March},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/garden-semantic-eee05.pdf},
 title = {Semantic feedback for hybrid recommendations in Recommendz},
 year = {2005}
}

@inproceedings{1389962,
 abstract = {This paper describes an underwater walking robotic system being developed under the name AQUA, the goals of the AQUA project, the overall hardware and software design, the basic hardware and sensor packages that have been developed, and some initial experiments. The robot is based on the RHex hexapod robot and uses a suite of sensing technologies, primarily based on computer vision and INS, to allow it to navigate and map clear shallow-water environments. The sensor-based navigation and mapping algorithms are based on the use of both artificial floating visual and acoustic landmarks as well as on naturally occurring underwater landmarks and trinocular stereo. },
 author = {C. {Georgiades} and A. {German} and A. {Hogue} and  {Hui Liu} and C. {Prahacs} and A. {Ripsman} and R. {Sim} and L. -. {Torres} and  {Pifu Zhang} and M. {Buehler} and G. {Dudek} and M. {Jenkin} and E. {Milios}},
 booktitle = {2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)},
 number = {},
 pages = {3525-3531 vol.4},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros04_aqua.pdf},
 title = {AQUA: an aquatic walking robot},
 volume = {4},
 year = {2004}
}

@inproceedings{1389816,
 abstract = {We address the problem of computing dense range maps of indoor locations using only intensity images and partial depth. We allow a mobile robot to navigate the environment, take some pictures and few range data. Our method is based on interpolating the existing range data using statistical inferences learned from the available intensity image and from those (sparse) regions where both range and intensity information is present. The spatial relationships between the variations in intensity and range can be efficiently captured by the neighborhood system of a Markov Random Field (MRF). In contrast to classical approaches to depth recovery (i.e. stereo, shape from shading), we can afford to make only weak assumptions regarding specific surface geometries or surface reflectance functions since we compute the relationship between existing range data and the images we start with. Experimental results show the feasibility of our method. },
 author = {L. A. {Torres-Mendez} and G. {Dudek}},
 booktitle = {2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566)},
 number = {},
 pages = {2699-2706 vol.3},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros04_latm.pdf},
 title = {Statistical inference and synthesis in the image domain for mobile robot environment modeling},
 volume = {3},
 year = {2004}
}

@article{sim2004ijcv,
 abstract = {We present a method for learning a set of generative models which are suitable for representing selected image-domain features of a scene as a function of changes in the camera viewpoint. Such models are important for robotic tasks, such as probabilistic position estimation (i.e. localization), as well as visualization. Our approach entails the automatic selection of the features, as well as the synthesis of models of their visual behavior. The model we propose is capable of generating maximum-likelihood views, as well as a measure of the likelihood of a particular view from a particular camera position. Training the models involves regularizing observations of the features from known camera locations. The uncertainty of the model is evaluated using cross validation, which allows for a priori evaluation of features and their attributes. The features themselves are initially selected as salient points by a measure of visual attention, and are tracked across multiple views. While the motivation for this work is for robot localization, the results have implications for image interpolation, image-based scene reconstruction and object recognition. This paper presents a formulation of the problem and illustrative experimental results.},
 author = {Sim, Robert and Dudek, Gregory},
 doi = {10.1023/B:VISI.0000027789.58057.14},
 issn = {1573-1405},
 journal = {International Journal of Computer Vision},
 number = {1},
 pages = {45-61},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/sim_ijcv04.pdf},
 title = {Learning Generative Models of Scene Features},
 url = {https://doi.org/10.1023/B:VISI.0000027789.58057.14},
 volume = {60},
 year = {2004}
}

@inproceedings{torres2004statistics,
 abstract = {This paper addresses the problem of estimating dense range maps of indoor locations using only intensity images and sparse partial depth information. Unlike shape-from-shading, we infer the relationship between intensity and range data and use it to produce a complete depth map. We extend prior work by incorporating geometric information from the available range data, specifically, we add surface normal information to reconstruct surfaces whose variations are not captured in the initial range measurements. In addition, the order on which we synthesize range values is based on a best-first approach that uses edge information from the intensity images, and isophotes lines from the available range. Our method uses Markov Random Fields to learn the statistical relationships from the available intensity and from those sparse regions where both range and intensity information is present. In contrast to classical approaches to depth recovery (i.e. stereo, shape from shading), we make only weak assumptions regarding specific surface geometries or surface reflectance functions since we compute the relationship between existing range data and the images we start with. Preliminary results on real data demonstrate that our method works reasonably well when incorporating geometric information. },
 author = {Torres-M{\'e}ndez, Luz A and Dudek, Gregory},
 booktitle = {Proceedings of 4th the International Symposium of Robotics and Automation (ISRA)(Queretaro, Mexico)},
 pages = {699--706},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/isra04_latm.pdf},
 title = {Statistics in the image domain for mobile robot environment modeling},
 year = {2004}
}

@inproceedings{simhon2004pen,
 abstract = {This paper presents a smart interface that automatically extracts and rnes pen strokes from images of hand drawn sketches. The interface allows users to digitize hand-drawn material such sketches of wcharts, cartoons or other pen based drawings and automatically isolate and rne the individual strokes making up the sketch. First, we present a method for extracting pen strokes based on learned constraints on curves. The approach consists of using a training set that shows good examples of curves and how a user would draw them. Given an image of a hand-drawn sketch, the system selects the pen stroke that is most statistically consistent with the examples in the training set and ranks the others based on their likelihood. Users can keep the selected candidate or they may scroll through the other top candidates to select a preferred solution. Second, we present an overview of our rnement procedure and its application on the extracted pen strokes. Using the same database of examples, the extracted pen stroke is rned to make it 'look' more like those in the database. },
 author = {Simhon, Saul and Dudek, Gregory},
 booktitle = {Eurographics Workshop on Sketch-Based Interfaces and Modeling},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/sbm04.pdf},
 title = {Pen Stroke Extraction and Refinement using Learned Models.},
 year = {2004}
}

@inproceedings{simhon2004sketch,
 abstract = {We present a system for generating 2D illustrations from hand drawn outlines consisting of only curve strokes. A user can draw a coarse sketch and the system would automatically augment the shape, thickness, color and surrounding texture of the curves making up the sketch. The styles for these refinements are learned from examples whose semantics have been pre-classified. There can be several styles applicable on a curve and the system automatically identifies which one to use and how to use it based on a curve's shape and its context in the illustration. Our approach is based on a Hierarchical Hidden Markov Model. We present a two level hierarchy in which the refinement process is applied at: the curve level and the scene level. },
 author = {Simhon, Saul and Dudek, Gregory},
 booktitle = {Eurographics Symposium on Rendering},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/egsr04.pdf},
 title = {Sketch Interpretation and Refinement Using Statistical Models.},
 year = {2004}
}

@article{rao2007ijrr,
 abstract = { The problem of minimum distance localization in environments that may contain self-similarities is addressed. A mobile robot is placed at an unknown location inside a 2D self-similar polygonal environment P. The robot has a map of P and can compute visibility data through sensing. However, the self-similarities in the environment mean that the same visibility data may correspond to several different locations. The goal, therefore, is to determine the robot's true initial location while minimizing the distance traveled by the robot. Two randomized approximation algorithms are presented that solve minimum distance localization. The performance of the proposed algorithms is evaluated empirically. },
 author = {Malvika Rao and Gregory Dudek and Sue Whitesides},
 doi = {10.1177/0278364907081234},
 eprint = {https://doi.org/10.1177/0278364907081234},
 journal = {The International Journal of Robotics Research},
 number = {9},
 pages = {917-933},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/finalWAFR.pdf},
 title = {Randomized Algorithms for Minimum Distance Localization},
 url = {https://doi.org/10.1177/0278364907081234},
 volume = {26},
 year = {2007}
}

@mastersthesis{Rao2004e,
 abstract = { This thesis addresses the problem of minimum distance localization in environments that may have structural self-similarities. In other words, how can a robot most efficiently collect sensor data to estimate its position and rule out ambiguity (as opposed to merely increasing accuracy). The formalism is that a mobile robot is placed at an unknown location inside a 2D self-similar environment modeled by a simple polygon P. The robot has a map of P, can sense its environment and hence compute visibility data. However, the self-similarities in the environment mean that the same visibility data may correspond to several different locations. The goal, therefore, is to determine the true initial location of the robot by distinguishing amongst several possibilities consistent with the sensed visibility data, while minimizing the distance traveled by the robot. We present two randomized approximation algorithms that efficiently solve the problem of minimum distance localization. The performance of our localization algorithms is validated and explored via extensive experiments on a range of simulated environments. },
 author = {Malvika Rao},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/thesis_1.pdf},
 school = {McGill University},
 title = {A Randomized Approach to Minimum Distance Localization},
 year = {2004}
}

@article{bourque2004cgf,
 abstract = {Abstract We present a technique for creating a smoothly varying sequence of procedural textures that interpolates between arbitrary input samples of texture. This texture transformation uses a library of procedural shaders and selects the correct shaders and associated parameters to accomplish the task. In general, selecting a procedural texture from a library, or finding the correct parameters to produce a smooth texture transition can be complex and time consuming. We propose a strategy for automating this process. While superficially this problem appears intractable for both humans and computational systems, its natural characteristics make a computational solution feasible. We present an algorithm and experimental results demonstrating this approach. Transformation between two textures can then be achieved procedurally, while enforcing perceptual similarity constraints between adjacent texture frames. We describe a technique for efficiently sampling the parameter domain of a shader based on a texture similarity function to create a smooth path through its texture range. In the case of evolving between several shaders, a method is described to obtain the best jump-points which can be used to connect different shaders smoothly in texture space. Several examples of the technique are shown, and future directions as well as potential problems are discussed. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Texture},
 author = {Bourque, Eric and Dudek, Gregory},
 doi = {10.1111/j.1467-8659.2004.00777.x},
 eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-8659.2004.00777.x},
 journal = {Computer Graphics Forum},
 number = {3},
 pages = {461-468},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/eg2004.pdf},
 title = {Procedural Texture Matching and Transformation},
 url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8659.2004.00777.x},
 volume = {23},
 year = {2004}
}

@inproceedings{sim2004self,
 abstract = {This paper deals with automatically learning the spatial distribution of a set of images. That is, given a sequence of images acquired from well-separated locations, how can they be arranged to best determine where they were viewed from, relative to one onother. The solution to this problem can be viewed as an instance of robot mapping although it can also be used in other contexts. We examine the problem where only limited prior odometric information is available, employing a feature-based method derived from a probabilistic pose estimation framework. Initially, a set of visual features are selected from the images and correspondences are found across the ensemble. The images are then localized by first assembling the small subset of images for which odometric confidence is high, and sequentially inserting the remaining images, localizing each against the previous estimates, and taking advantage of any priors that are available. We present experimental results validating the approach, and demonstrating metrically and topologically accurate results over two large image ensembles. Finally, we discuss the results, their relationship to the autonomous exploration of an unknown environment, and their utility for robot localization and navigation. },
 author = {Sim, Robert and Dudek, Gregory},
 booktitle = {Proc. American Association for Artificial Intelligence (AAAI)},
 pages = {470--475},
 title = {Self-organizing visual maps},
 year = {2004}
}

@inproceedings{simhon2004analogical,
 abstract = {We present a probabilistic method for path planning that considers trajectories constrained by both the environment and an ensemble of restrictions or preferences on preferred motions for a moving robot. Our system learns constraints and preference biases on a robotÞ²s motion from examples, and then synthesizes behaviors that satisfy these constraints. This behavior can encompass motions that satisfy diverse requirements such as a sweep pattern for floor coverage, or, in particular in our experiments, satisfy restrictions on the robotÞ²s physical capabilities such as restrictions on its turning radius. Given an approximate path that may not satisfy the required conditions, our system computes a refined path that satisfies the constraints and also avoids obstacles. Our approach is based on a Bayesian framework for combining a prior probability distribution on the trajectory with environmental constraints. The prior distribution is generated by decoding a Hidden Markov Model, which is itself is trained over a particular set of preferred motions. Environmental constraints are modeled using a potential field over the configuration space.

This paper poses the requisite theoretical framework and demonstrates its effectiveness with several examples. },
 author = {Simhon, Saul and Dudek, Gregory},
 booktitle = {Proc. American Association for Artificial Intelligence (AAAI)},
 pages = {537--543},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/aaai04.pdf},
 title = {Analogical path planning},
 year = {2004}
}

@inproceedings{1301479,
 abstract = {This paper developed prior work which incrementally completes a sparse depth map based on inter-image statistics information. In that prior work, we have observed that pixel ordering of the incremental recovery is critical to the quality of the final results. In this paper we demonstrate improved performance using an information-driven recovery policy to determine this ordering. We have also observed that the reconstruction across depth discontinuities was often problematic as there was comparatively little constraint for probabilistic inference at those locations. Further, such locations are often identified with edges in both the range and intensity maps. We address this problem by deferring the reconstruction of voxels close to intensity or depth discontinuities, leading to improved results. We also show that color information can improve reconstruction quality. Experimental results are presented to demonstrate the quality of the recover and to illustrate some new application domains such as deblurring and underwater scattering compensation. },
 author = {L. A. {Torres-Mendez} and G. {Dudek} and P. {Di Marco}},
 booktitle = {First Canadian Conference on Computer and Robot Vision, 2004. Proceedings.},
 number = {},
 pages = {432-439},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/crv04_latm.pdf},
 title = {Inter-image statistics for scene reconstruction},
 volume = {},
 year = {2004}
}

@inproceedings{torres2004reconstruction,
 abstract = {This paper addresses the probabilistic inference of geometric structures from images. Specifically, of synthesizing range data to enhance the reconstruction of a 3D model of an indoor environment by using video images and (very) partial depth information. In our method, we interpolated the available range data using statistical inferences learned from the concurrently available video images and from those (sparse) regions where both range and intensity information is available. The spatial relationships between the variations in intensity and range can be efficiently captured by the neighborhood system of a Markov Random Field (MRF). In contrast to classical approaches to depth recovery (i.e. stereo, shape from shading), we can afford to make only weak prior assumptions regarding specific surface geometries or surface reflectance functions since we compute the relationship between existing range data and the images we start with. Experimental results show the feasibility of our method. },
 author = {Torres-M{\'e}ndez, Luz Abril and Dudek, Gregory},
 booktitle = {PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE},
 organization = {Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999},
 pages = {476--481},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/AAAI04TorresL.pdf},
 title = {Reconstruction of 3d models from intensity images and partial depth},
 year = {2004}
}

@mastersthesis{garden2004,
 abstract = {This thesis presents a new approach to recommender systems. Previous recommender systems based on collaborative filtering typically solicit user feedback on domain items as overall ratings which are then recorded as numeric values. This paradigm limits the semantic richness of the user's interaction with the system and the depth to which the system can understand user preferences. We propose a new recommender system, Recommendz, which allows the user to comment not only about the overall quality of the item but also about the {\em quantity} and {\em quality} of features of the item. This allows the user to justify his or her ratings and allows the system to compare users not only with respect to overall preference, but also to compare the reasons behind those preferences. We have developed an implmentation of our approach, and have collected extensive empirical data based on movie ratings. We demonstrate the effectiveness of our approach, and describe the details of the implementation. },
 author = {Matthew Garden},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/thesis_2.pdf},
 school = {McGill University},
 title = {On the use of semantic feedback in recommender systems},
 year = {2004}
}

@inproceedings{1308078,
 author = {R. {Sim} and G. {Dudek} and N. {Roy}},
 booktitle = {IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004},
 number = {},
 pages = {1758-1763},
 title = {Online control policy optimization for minimizing map uncertainty during exploration},
 volume = {2},
 year = {2004}
}

@inproceedings{Rekleitis2003c,
 abstract = {This paper presents a first detailed case study of collaborative exploration of a substantial environment. We use a pair of cooperating robots to test multi-robot environment mapping algorithms based on triangulation of free space (see video). The robots observe one another using a robot tracking sensor based on laser range sensing (LIDAR). The environment mapping itself is accomplished using sonar sensing. The results of this mapping are compared to those obtained using scanning laser range sensing and the scan matching algorithm. We show that with appropriate outlier rejection policies, the sonar-based map obtained using collaborative localization can be as good or, in fact, better than that obtained using what is typically considered to be a superior sensing technology.},
 address = {Las Vegas, NV},
 author = {Ioannis Rekleitis and Gregory Dudek and Evangelos Milios},
 booktitle = {IEEE/RSJ/GI International Conference on Intelligent Robots and Systems},
 month = {Oct. 27-31},
 organization = {IEEE/RSJ},
 pages = {1777--1782},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros03-453.pdf},
 title = {Experiments in Free-Space Triangulation Using Cooperative Localization},
 year = {2003}
}

@inproceedings{1249653,
 author = {R. {Sim} and G. {Dudek}},
 booktitle = {Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453)},
 number = {},
 pages = {3224-3231 vol.3},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/sim_iros03.pdf},
 title = {Effective exploration strategies for the construction of visual maps},
 volume = {4},
 year = {2003}
}

@inproceedings{sim2003examining,
 author = {Sim, Robert and Dudek, Gregory},
 booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Reasoning with Uncertainty in Robotics (RUR)},
 pages = {69--76},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/sim_rur03.pdf},
 title = {Examining exploratory trajectories for minimizing map uncertainty},
 year = {2003}
}

@inproceedings{sim2003comparing,
 author = {Sim, Robert and Dudek, Gregory},
 booktitle = {Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI)},
 pages = {1560--1562},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/sim_ijcai03.pdf},
 title = {Comparing image-based localization methods},
 year = {2003}
}

@misc{sim2004self,
 author = {Sim, Robert and Dudek, Gregory},
 howpublished = {Workshop on Advances in Machine Learning},
 note = {presentation, no published proceedings},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/sim_waml03.pdf},
 title = {Self-organizing visual maps},
 year = {2003}
}

@inproceedings{1242121,
 author = {G. {Dudek} and  {Robert Sim}},
 booktitle = {2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)},
 number = {},
 pages = {3434-3440 vol.3},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra03.pdf},
 title = {RoboDaemon - a device independent, network-oriented, modular mobile robot controller},
 volume = {3},
 year = {2003}
}

@inproceedings{torres03iros,
 abstract = {This paper examines a novel method we have developed for computing range data in the context of mobile robotics. Our objective is to compute dense range maps of locations in the environment, but to do this using intensity images and very limited range data as input. We develop a statistical learning method for inferring and extrapolating range data from a combination of a single video intensity image and a limited amount of input range data. Our methodology is to compute the relationship between the observed range data and the variations in the intensity image, and use this to extrapolate new range values. These variations can be efficiently captured by the neighborhood system of a Markov Random Field (MRF) without making any strong assumptions about the kind of surfaces in the world. Experimental results show the feasibility of our method. },
 address = {Las Vegas, NV},
 author = {L.A. Torres-Méndez and G. Dudek},
 booktitle = {Proceedings of the IEEE/RSJ Conference on Intelligent Robots
and Systems (IROS)},
 month = {October},
 pages = {8},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros03.pdf},
 publisher = {IEEE Press},
 title = {Range Synthesis for 3D Environment Modeling},
 url = {http://www.cim.mcgill.ca/~latorres/Publications/latm_iros03.pdf},
 year = {2003}
}

@inproceedings{Simhon03,
 abstract = {In this paper we present a novel method for robot path planning based on learning motion patterns. A motion pattern is defined as the path that results from applying a set of probabilistic constraints to a ``raw'' input path. For example, a user can sketch an approximate path for a robot without considered issues such as bounded radius of curvature and our system would then elaborate it to include such a constraint. In our approach, the constraints that generate a path are learned by capturing the statistical properties of a set of training examples using supervised learning. Each training example consists of a pair of paths: an unconstrained (raw) path and an associated preferred path. Using a Hidden Markov Model in combination with multi-scale methods, we compute a probability distribution for successive path segments as a function of their context within the path and the raw path that guides them. This learned distribution is then used to synthesize a preferred path from an arbitrary input path by choosing some mixture of the training set biases that produce the maximum likelihood estimate. We present our method and applications for robot control and non-holonomic path planning. },
 address = {Taipei, Taiwan},
 author = {Saul Simhon and Gregory Dudek},
 booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation},
 month = {May},
 pages = {??},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra03_1.pdf},
 title = {Path Planning Using Learned Constraints and Preferences},
 year = {2003}
}

@inproceedings{Rekleitis2003a,
 abstract = {In this paper we present a probabilistic framework for the reduction in the uncertainty of a moving robot pose during exploration by using a second robot to assist. A Monte Carlo Simulation technique (specifically, a Particle Filter) is employed in order to model and reduce the accumulated odometric error. Furthermore, we study the requirements to obtain an accurate yet timely pose estimate. A team of two robots is employed to explore an indoor environment in this paper, although several aspects of the approach have been extended to larger groups. The concept behind our exploration strategy has been presented previously and is based on having one robot carry a sensor that acts as a .robot tracker. to estimate the position of the other robot. By suitable use of the tracker as an appropriate motion-control mechanism we can sweep areas of free space between the stationary and the moving robot and generate an accurate graph-based description of the environment. This graph is used to guide the exploration process. Complete exploration without any overlaps is guaranteed as a result of the guidance provided by the dual graph of the spatial decomposition (triangulation) of the environment. We present experimental results from indoor experiments in our laboratory and from more complex simulated experiments.},
 address = {Taipei, Taiwan},
 author = {Ioannis Rekleitis and Gregory Dudek and Evangelos Milios},
 booktitle = {IEEE International Conference on Robotics and Automation},
 month = {Sept.},
 organization = {IEEE},
 pages = {1907--1912},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra03_2.pdf},
 title = {Probabilistic Cooperative Localization and Mapping in Practice},
 year = {2003}
}

@inproceedings{torres03a,
 abstract = {This paper presents a statistical learning method for computing range data as an initial solution to the environment modeling problem in the context of mobile robotics. Unlike other methods that are based on a set of geometric primitives, our method computes dense range maps of locations in the environment using only intensity images and very limited amount of range data as an input. This is achieved by exploiting the following assumptions: 1) the observed range and intensity images are correlated and, 2) variations of pixels in the range and intensity images are related to the values elsewhere in the image(s). These variations can be efficiently captured by the neighborhood system of a Markov Random Field (MRF). Experimental results show the feasibility of our method. },
 address = {Acapulco, Mexico},
 author = {Luz A. Torres-Méndez and G. Dudek},
 booktitle = {Proceedings of the International Joint Conference on Artificial
Intelligence (IJCAI) Workshop on Reasoning with Uncertainty in Robotics (RUR)},
 month = {August},
 pages = {85--92},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/rur03.pdf},
 publisher = {Morgan Kaufmann},
 title = {A Statistical Learning Method for Mobile Robot Environment Modeling},
 year = {2003}
}

@inproceedings{dudek-garden2003,
 abstract = {In this paper we present an overview of a recommender system that attempts to predict user preferences based on several sources including prior choices and selected user-defined features. By using a combination of collaborative filtering and semantic features, we hope to provide performance superior to either alone. Further, our set of semantic features is acquired and updated using a learning-based procedure that avoids the need for manual knowledge-engineering. Our system is implemented in a web-based application server environment and can be used with abitrary domains, although the test data reported here is restricted to recommendations of movies. },
 address = {Halifax, Nova Scotia, Canada},
 author = {Gregory Dudek and Matthew Garden},
 booktitle = {WI/IAT 2003 Workshop on Applications, Products, and Services of Web-based Support Systems},
 editor = {J.T. Yao and P. Lingras},
 month = {October},
 organization = {Web Intelligence Consortium and IEEE},
 pages = {139--143},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/wi2003.pdf},
 publisher = {Department of Mathematics and Computing Science, Saint Mary's University},
 title = {On User Recommendations Based On Multiple Cues},
 year = {2003}
}

@inproceedings{Torres-Mendez2002b,
 abstract = {In this paper a range synthesis algorithm is proposed as an initial solution to the problem of 3D environment modeling from sparse data. We develop a statistical learning method for inferring and extrapolating range data from as little as one intensity image and from those (sparse) regions where both range and intensity information is available. Our work is related to methods for texture synthesis using Markov Random Field methods. We demonstrate that MRF methods can also be applied to general intensity images with little associated range information and used to estimate range values where needed without making any strong assumptions about the kind of surfaces in the world. Experimental results show the feasibility of our method. },
 address = {Orlando,FL},
 author = {Luz~A. Torres-Mendez and Gregory Dudek},
 booktitle = {IEEE Workshop on Applications of Computer Vision (WACV'2002)},
 month = {December},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/wacv2002.pdf},
 publisher = {IEEE Computer Society},
 title = { Range Synthesis for 3D Environment Modelling},
 year = {2002}
}

@inproceedings{Rekleitis2002c,
 abstract = {This paper examines the tradeoffs between different classes of sensing strategy and motion control strategy in the context of terrain mapping multiple robots. We consider a larger group of robots that can mutually estimate one another's position (in 2D or 3D) and uncertainty using a sample-based (particle filter) model of uncertainty. Our prior work has dealt with a pair of robots that estimate one another's position using visual tracking and coordinated motion. Here we extend these results and consider a richer set of sensing and motion options. In particular, we focus on issues related to confidence estimation for groups of more than two robots. },
 address = {Lausanne, Switzerland},
 author = {Ioannis M. Rekleitis and Gregory Dudek and Evangelos Milios},
 booktitle = {IEEE/RSJ/ International Conference on Intelligent Robots and Systems},
 month = {October},
 organization = {IEEE/RSJ},
 pages = {2690-2695},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros02pdf.pdf},
 title = {Multi-Robot Cooperative Localization:
A Study  of Trade-offs Between  Efficiency and Accuracy},
 year = {2002}
}

@misc{torres2002automated,
 abstract = {The acquisition of a 3D model of a real environment can be accomplished using range sensors. In practice, suitable sensors to densely cover a large environment are often impractical. This paper presents ongoing work on the synthesis of 3D environment models from as little as one intensity image and sparse range data. Our method is based on interpolating the available range data using statistical inferences learned from the available intensity image and from those (sparse) regions where both range and intensity information is available. Since we compute the relationship between extisting range data and the images we start with, we do not need to make any strong assumptions about the kind of surfaces in the world (for example we do not need to assume the world exhibits only diffuse reflectance). Experimental results show the feasibility of our method. },
 author = {Luz A. Torres-M\u00e9ndez and Gregory Dudek},
 howpublished = {In Short Presentations of Eurographics (CD-ROM), Saarbr\u00fccken, Germany.},
 note = {},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/eg2002.pdf},
 title = {Automated Enhancement of 3D Models},
 year = {2002}
}

@misc{Rekleitis2002a,
 abstract = {This paper deals with terrain mapping and position estimation using multiple robots. Here we will discuss work where a larger group of robots can mutually estimate one another's position (in 2D or 3D) and uncertainty using a sample-based (particle filter) model of uncertainty. Our prior work has dealt with a pair of robots that estimate one another's position using visual tracking and coordinated motion and we extend these results and consider a richer set of sensing and motion options. In particular, we focus on issues related to confidence estimation for groups of more than two robots. },
 author = {Ioannis~M. Rekleitis, Gregory Dudek, and Evangelos~E. Milios},
 howpublished = {Multi-Robot Systems Workshop, Naval Research Laboratory, Washington, DC, USA},
 month = {March 18-20},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/mrworkshop.pdf},
 title = {On the Positional Uncertainty of Multi-Robot Cooperative Localization},
 year = {2002}
}

@inproceedings{bourque2002eurographics,
 address = {Pisa, Italy},
 author = {Bourque, Eric and Gregory Dudek},
 booktitle = {Proc. 13th Eurographics Workshop on Rendering},
 pages = {},
 publisher = {},
 series = {},
 title = {Automated Parameter Estimation for Procedural Texturing},
 year = {2002}
}

@inproceedings{Bourque2001b,
 abstract = {In this paper we describe an approach to the automated creation of procedural textures to be used in image synthesis. Procedural textures exhibit many advantages over traditional surface texturing techniques, but unfortunately finding the correct procedural texture and appropriate parameters to create the desired texture can be a daunting task for even the most experienced computer graphics artists. The method we propose here, which we refer to as image-based procedural texturing, allows the specification of the desired texture in the form of a digital input image. From this sample texture, a corresponding procedural texture is found which produces a texture which is perceptually similar to the input sample. },
 author = {Eric Bourque and Gregory Dudek},
 booktitle = {Proceedings of the 14th International Conference on Vision In\terface},
 month = {June},
 pages = {181--187},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/vi2001.pdf},
 title = {Image-Driven Procedural Texture Specification},
 year = {2001}
}

@article{Rekleitis2001c,
 abstract = {This paper presents a new sensing modality for multirobot exploration. The approach is based on using a pair of robots that observe each other, and act in concert to reduce odometry errors. We assume the robots can both directly sense nearby obstacles and see each other. The proposed approach improves the quality of the map by reducing the inaccuracies that occur over time from dead reckoning errors. Furthermore, by exploiting the ability of the robots to see each other, we can detect opaque obstacles in the environment independently of their surface reflectance properties. Two different algorithms, based on the size of the environment, are introduced, with a complexity analysis, and experimental results in simulation and with real robots. },
 author = {Ioannis M. Rekleitis and Gregory Dudek and Evangelos Milios},
 journal = {Annals of Mathematics and Artificial Intelligence},
 number = {1-4},
 pages = {7-40},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/Multi-robot-collaboration-for-robust-exploration-Rekleitis-2001.pdf},
 title = {Multi-Robot Collaboration for Robust Exploration},
 volume = {31},
 year = {2001}
}

@inproceedings{Rekleitis2001b,
 abstract = {We examine the problem of learning a visual map of the environment while maintaining an accurate pose estimate. Our approach is based on using two robots in a simple collaborative scheme; in practice, one of these robots can be much less capable than the other. In many mapping contexts, a robot moves about collecting data (images, in particular) which are later used to assemble a map; we can think of map construction as a training process. Without outside information, as a robot collects training images, its position estimate accumulates errors, thus corrupting its knowledge of the positions from which observations are taken. We address this problem by deploying a second robot to observe the first one as it explores, thereby establishing a \emph{virtual tether}, and enabling an accurate estimate of the robot's position while it constructs the map. We refer to this process as \emph{cooperative localization}. The images collected during this process are assembled into a representation that allows vision-based position estimation from a single image at a later date. In addition to developing a formalism and concept, we validate our results experimentally and present quantitative results demonstrating the performance of the method in over 90 trials. },
 address = {Maui, Hawaii, USA},
 author = {Ioannis Rekleitis and Robert Sim and Gregory Dudek and Evangelos Milios},
 booktitle = {2001 IEEE/RSJ International Conference on Intelligent
Robots and Systems},
 month = {October},
 note = {IEEE Catalog No: 01CH37180C},
 number = {ISBN 0-7803-6614-X},
 organization = {IEEE/RSJ},
 pages = {1269-1274},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros01.pdf},
 publisher = {IEEE},
 title = {Collaborative exploration for the construction of visual maps},
 volume = {3},
 year = {2001}
}

@inproceedings{Rekleitis2001a,
 abstract = {We consider the problem of map learning while maintaining ground-truth pose estimates. Map learning is important in tasks that require a model of the environment or some of its features. As a robot collects data, uncertainty about its position accumulates and corrupts its knowledge of the positions from which observations are taken. We address this problem by employing <i>cooperative localization</i>; that is, deploying a second robot to observe the other as it explores, thereby establishing a <i>virtual tether</i>, and enabling an accurate estimate of the robot's position while it constructs the map. This paper presents our approach to this problem in the context of learning a set of visual landmarks useful for pose estimation. In addition to developing a formalism and concept, we validate our results experimentally and present quantitative results demonstrating the performance of the method. },
 address = {Banff, Alberta, Canada},
 author = {Ioannis Rekleitis and Robert Sim and Gregory Dudek and Evangelos Milios},
 booktitle = {2001 IEEE International Symposium on Computational
Intelligence in Robotics and Automation},
 month = {July},
 note = {IEEE Catalog No: 01EX515C},
 number = {ISBN 0-7803-7203-4},
 organization = {IEEE},
 pages = {296-301},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/cira01.pdf},
 publisher = {IEEE},
 title = {Collaborative Exploration for Map Construction},
 year = {2001}
}

@inproceedings{simhon2001amt,
 abstract = {This work considers an approach for artificially enhancing the richness and level of detail of graphical scenes. In particular, we examine a method for automatically generating high-resolution novel curves from manually sketched drawings of those curves. The essential idea is to augment the hand-drawn curves using prior knowledge to produce a more elaborated picture. Our method uses multi-scale analysis of a class of training data to capture statistical properties of the set. These properties are then conditioned at a coarse scale by the hand-drawn curve to steers the synthesis according to the overall shape of the curve. Given an approximation sketch, the algorithm generates the most likely scene by propagating probabilities over a Markov Chain. Users without artistic capabilities can then describe scenes in a more natural way to build impressive graphics.},
 address = {Berlin, Heidelberg},
 author = {Simhon, Saul and Dudek, Gregory},
 booktitle = {Active Media Technology},
 editor = {Liu, Jiming and Yuen, Pong C. and Li, Chun-hung and Ng, Joseph and Ishida, Toru},
 isbn = {978-3-540-45336-9},
 pages = {355--364},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/amt_2001.pdf},
 publisher = {Springer Berlin Heidelberg},
 title = {On the Elaboration of Hand-Drawn Sketches},
 year = {2001}
}

@article{Sim01Journal,
 abstract = { We present a method for learning a set of environmental features which are useful for pose estimation. The landmark learning mechanism is designed to be applicable to a wide range of environments, and generalized for different sensing modilities. In the context of computer vision, each landmark is detected as a local extremum of a measure of distinctiveness and represented by an appearance-based encoding which is exploited for matching. The set of obtained landmarks can be parameterized and then evaluated in terms of their utility for the task at hand. The method is used to motivate a general approach to task-oriented sensor fusion. We present experimental evidence that demonstrates the utility of the method. },
 author = {R. Sim and G. Dudek},
 journal = {Image and Vision Computing, Elsevier Press},
 number = {11},
 pages = {733--739},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ivc.pdf},
 title = {Learning environmental features for pose estimation},
 volume = {19},
 year = {2001}
}

@inproceedings{sim01cvpr,
 abstract = {We present a method for learning a set of generative models which are suitable for representing variations of selected image-domain features of the scene as a function of changes in the camera viewpoint. Such models are important for robotic tasks, such as probabilistic position estimation (i.e. localization), as well as visualization. Our approach entails the selection of image-domain features, as well as the synthesis of models of their visual behavior. The model we propose is capable of generating maximum likelihood views of automatically selected features, as well as a measure of the likelihood of a particular view from a particular camera position. Training the models involves regularizing observations of the features from known camera locations. The uncertainty of the model is evaluated using cross validation. The features themselves are initially selected automatically as salient points by a measure of visual attention, and are tracked across multiple views. While the motivation for this work is for robot localization, the results have implications for image interpolation, virtual scene reconstruction and object recognition. This paper presents a formulation of the problem and illustrative experimental results. },
 address = {Hawaii},
 author = {R. Sim and G. Dudek},
 booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
 month = {December},
 pages = {406--412},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/cvpr01.pdf},
 publisher = {IEEE Press},
 title = {Learning Generative Models of Scene Features},
 url = {http://www.cim.mcgill.ca/~simra/publications/cvpr01.ps.gz},
 year = {2001}
}

@article{Roy00AR,
 author = {Nicholas Roy and Gregory Dudek},
 journal = {Autonomous Robots},
 month = {Sept.},
 number = {2},
 pages = {117-136},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ARJournal.pdf},
 title = {Collaborative Exploration and Rendezvous: Algorithms,
Performance Bounds and Observations},
 volume = {11},
 year = {2001}
}

@inproceedings{Rekleitis2000b,
 abstract = {We present an approach to multi-robot exploration of large environments. Our method is designed to be robust in the face of arbitrarily large odometry errors or objects with poor reflectance characteristics. The algorithm achieves its robustness by using a team of cooperating agents. The critical aspect of our method is the use of a vision system that sweeps areas of free space and generates a graph-based description of the environment. This graph is used to guide the exploration process and can also be used for subsequent tasks such as place recognition or path planning. As a result of the guidance provided by the dual graph of the triangulated environment, our system can guarantee complete exploration without any overlaps. We present an algorithmic solution, simulation results, as well as a cost analysis and experimental data. In this approach a pair of robots observe one another's behavior, thus greatly reducing odometry errors. We assume the robots can both directly sense nearby obstacles and see one another (if their view is not obstructed). We have implemented both these capabilities with actual robots in our lab. },
 address = {Knoxville, Tennessee, USA},
 author = {Ioannis M. Rekleitis and Gregory Dudek and Evangelos Milios},
 booktitle = {5th International Symposium on Distributed Autonomous
Robotic Systems (DARS)},
 month = {October 4-6},
 pages = {241-250},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/darsU131.pdf},
 publisher = {Springer},
 title = {Graph-Based Exploration using Multiple Robots},
 year = {2000}
}

@inproceedings{Rekleitis2000a,
 abstract = {This paper presents a new sensing modality and stratagem for multirobot exploration. The approach is based on using pairs of robots that observe each other's behavior, acting in concert to reduce odometry errors. We assume the robots can both directly sense nearby obstacles and see each other. This allows the robots to obtain a map of higher accuracy than would be possible with robots acting independently bu reducing inaccuracies that occur over time from dead reckoning errors. Furthermore, by exploiting the ability of the robots to see each other, we can detect opaque obstacles in the environment independently of their surface reflectance properties. Two different algorithms, based on the size of the environment, are introduced, with a complexity analysis, and experimental results in simulation and with real robots. },
 address = {San Francisco, USA},
 author = {Ioannis M. Rekleitis, and Gregory Dudek, and Evangelos Milios},
 booktitle = {Proceedings of International Conference in Robotics and
Automation},
 month = {April},
 pages = {3164-3169},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra00.pdf},
 title = {Multi-Robot Collaboration for Robust Exploration},
 year = {2000}
}

@misc{saul2000poster,
 author = {Saul Simhon and Gregory Dudek},
 howpublished = {(Abstract - Poster) Graphics Interface, Montreal},
 month = {May},
 note = {},
 title = {Stochastic Reconstruction from Coarse Data},
 year = {2000}
}

@article{torres-mendez2000a,
 abstract = {A method for object recognition, invariant under translation, rotation, and scaling, is addressed. The first step of the method (preprocessing) takes into account the invariant properties of the normalized moment of inertia and a novel coding that extracts topological object characteristics. The second step (recognition) is achieved by using a holographic nearest-neighbor algorithm (HNN), in which vectors obtained in the preprocessing step are used as inputs to it. The algorithm is tested in character recognition, using the 26 upper case letters of the alphabet. Only four different orientations and one size (for each letter) were used for training. Recognition was tested with 17 different sizes and 14 rotations. The results are encouraging, since we achieved 98% correct recognition. Tolerance to boundary deformations and random noise was tested. Results for character recognition in "real" images of car plates are presented as well. },
 author = {L.A. Torres-Méndez and J. C. Ruiz-Suárez and Luis E. Sucar and G. Gómez},
 journal = {IEEE Transactions on Systems, Man and Cybernetics},
 month = {February},
 pages = {125-130},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/smc2000.pdf},
 title = {Translation, Rotation, and Scale-Invariant Object Recognition},
 volume = {30},
 year = {2000}
}

@inproceedings{844735,
 author = {G. {Dudek} and D. {Jugessur}},
 booktitle = {Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)},
 number = {},
 pages = {1030-1035 vol.2},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icraFinal.pdf},
 title = {Robust place recognition using local appearance based methods},
 volume = {2},
 year = {2000}
}

@inproceedings{855907,
 author = {D. {Jugessur} and G. {Dudek}},
 booktitle = {Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662)},
 number = {},
 pages = {834-839 vol.1},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/cvprFinal.pdf},
 title = {Local appearance for robust object recognition},
 volume = {1},
 year = {2000}
}

@inproceedings{895271,
 author = {M. {Jenkin} and G. {Dudek}},
 booktitle = {Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113)},
 number = {},
 pages = {2042-2047 vol.3},
 title = {The paparazzi problem},
 volume = {3},
 pdf = {https://doi.org/10.1109/IROS.2000.895271},
 year = {2000}
}

@inproceedings{846371,
 author = {E. {Bourque} and G. {Dudek}},
 booktitle = {Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)},
 number = {},
 pages = {2310-2315 vol.3},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/online_construction_of_iconic_maps.pdf},
 title = {On-line construction of iconic maps},
 volume = {3},
 year = {2000}
}

@article{BourqueVacationSnap2000,
 abstract = {For many tasks, we wish to record or recover the description of a remote environment so that it can be inspected by a person. This is the problem we address in this paper, referring to it by the name the "Vacation Snapshot Problem". Rather than recovering a geometric description of an environment, as many robotics systems attempt to do, we seek to recover a model of an environment in terms of its appearance from a set of carefully selected viewpoints. Our hope is that this type of model is both more accessible to humans for many realistic tasks, and also more readily achieved with automated systems. These viewpoints are locations in the environment associated with views containing maximal visual interest. This approach to environment representation is analogous to image compression. Our goal is to obtain a set of representative views resembling those that would be selected by a human observer given the same task. Our computational procedure is inspired by models of human visual attention appearing in the literature on human psychophysics. We make use of the underlying edge structure of a scene, as it is largely unaffected by variations in illumination. Our implementation uses a mobile robot to traverse the environment, and then builds an image-based virtual representation of the environment, only keeping the views whose responses were highest. We demonstrate the effectiveness of our attention operator on both single images, and in viewpoint selection within an unknown environment.


See: http://www.cim.mcgill.ca/~mrl/publications.html#vacationSnapshot },
 address = {Hingham, MA, USA},
 author = {Bourque,, Eric and Dudek,, Gregory},
 doi = {http://dx.doi.org/10.1023/A:1008939729190},
 issn = {0929-5593},
 journal = {Autonomous Robots},
 number = {2},
 pages = {173--190},
 publisher = {Kluwer Academic Publishers},
 title = {On the Automated Construction of Image-Based Maps},
 volume = {8},
 year = {2000}
}

@article{dudek2000visualscience,
 author = {Gregory Dudek and Sandra Polifroni and F. Ferrie},
 journal = {Investigative Opthalmology and Visual Science (The Association for Research in Vision and Opthalmology)},
 number = {},
 pages = {196-197},
 title = {Evaluation of Computation Attention Operators using Human Image Recognition},
 volume = {},
 year = {2000}
}

@techreport{Burlington-AAAI,
 abstract = {In this paper we consider the problem of robotic search: that is, having a mobile robot find a target object in an unknown environment with obstacles. As a side-effect, the robot explores the environment. Based on previous results, we present a formal description of our problem and an algorithm to solve it. Our work is a derivative of spiral search techniques developed in more abstract contexts. We show that this algorithm has good worst-case performance, in terms of its competitive ratio. We also show experimental data validating the feasibility of our approach and typical results. },
 author = {Scott Burlington and Gregory Dudek},
 instituion = {Center for Intelligent Machines, McGill University},
 month = {January},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/burl-aaai99.pdf},
 title = {Spiral Search as an Efficient Mobile Robotic Search Technique},
 year = {1999}
}

@inproceedings{Rekleitis1999,
 abstract = { We consider the robot exploration of a planar graph-like world. The robot's goal is to build a complete map of its environment. The environment is modeled as an arbitrary undirected planar graph which is initially unknown to the robot. The robot cannot distinguish vertices and edges that it has explored from the unexplored ones. The robot is assumed to be able to autonomously traverse graph edges, recognize when it has reached a vertex, and enumerate edges incident upon the current vertex. The robot cannot measure distances nor does it have a compass, but it is equipped with a single marker that it can leave at a vertex and sense if the marker is present at a newly visited vertex. The total number of edges traversed while constructing a map of a graph is used as a measure of performance. We present an efficient algorithm for learning an unknown, undirected planar graph by a robot equipped with one marker. One of the main results of this paper is to show that our strategy leads to performance that is typical linear in the size of the graph. Experimental results obtained by running a large collection of example worlds are also presented.},
 address = {Detroit, USA},
 author = {Ioannis M. Rekleitis, and Vida Dujmovi\'c and Gregory Dudek},
 booktitle = {Proceedings of International Conference in Robotics and Automation},
 month = {May},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra99.pdf},
 title = {Efficient Topological Exploration},
 year = {1999}
}

@inproceedings{sim99a,
 abstract = {We present an approach to vision-based mobile robot localization, even without an a-priori pose estimate. This is accomplished by learning a set of visual features called image-domain landmarks. The landmark learning mechanism is designed to be applicable to a wide range of environments. Each landmark is detected as a local extremum of a measure of uniqueness and represented by an appearance-based encoding. Localization is performed using a method that matches observed landmarks to learned prototypes and generates independent position estimates for each match. The independent estimates are then combined to obtain a final position estimate, with an associated uncertainty. Quantitative experimental evidence is presented that demonstrates that accurate pose estimates can be obtained, despite changes to the environment. },
 address = {Detroit, MI},
 author = {Robert Sim and Gregory Dudek},
 booktitle = {International Conference on Robotics and Automation},
 month = {May},
 pages = {},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra99_1.pdf},
 title = {Learning Visual Landmarks for Pose Estimation},
 year = {1999}
}

@article{zelek1996spott,
 abstract = {A robot control architecture called SPOTT is proposed and implemented as a soft real-time system of concurrently executing and co-operating modules. What distinguishes SPOTT from other architectures with a behavioral control component is that (1) it is able to guarantee task completion in certain scenarios; and (2) it is scalable to different tasks, control laws, computational resources, and robot platforms. The dynamic path planning module's optimality properties give SPOTT its task completion guarantees. Scalability claims are made possible because of the (1) modularity of the behavioral control programs; and (2) SPOTT's software architecture.

SPOTT consists of various components with both deliberative and reactive properties: a behavioral controller, a local dynamic path planner, and a global path planner. In addition, other components include a map database and a graphical user interface. The behavioral control formalism is called TR$+$ and is based on an adaptation and extension of the Teleo-Reactive (TR) formalism. TR$+$ control rules (i.e., condition-action pairs) are structured as modular software components. TR$+$ conditions are based on sensed and internal model states and TR$+$ actions either affect actuator control or perform map database maintenance. The local dynamic path planner is based on a potential field method using harmonic functions, which are guaranteed to have no spurious local minima. The global planning module advises the local planning module on the local effect of the global goal. A real-time and parallel implementation of SPOTT using a message passing software package called PVM has been developed and tested across a collection of interchangeable heterogeneous workstations. Navigational experiments have consisted of moving two different robot platforms in a structured office and an unstructured laboratory environment to known spatial locations with no or a partial a priori map. },
 author = {Zelek, John S and Levine, Martin D},
 journal = {IEEE Transactions on Robotics and Automation submitted},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ra99.pdf},
 title = {SPOTT: a predictable and scalable architecture for autonomous mobile robot control},
 year = {1996}
}

@inproceedings{790419,
 author = {R. {Sim} and G. {Dudek}},
 booktitle = {Proceedings of the Seventh IEEE International Conference on Computer Vision},
 number = {},
 pages = {1217-1222 vol.2},
 title = {Learning and evaluating visual features for pose estimation},
 volume = {2},
 year = {1999}
}

@article{SIM2001733,
 abstract = {We present a method for learning a set of environmental features which are useful for pose estimation. The landmark learning mechanism is designed to be applicable to a wide range of environments, and generalized for different sensing modalities. In the context of computer vision, each landmark is detected as a local extremum of a measure of distinctiveness and represented by an appearance-based encoding which is exploited for matching. The set of obtained landmarks can be parameterized and then evaluated in terms of their utility for the task at hand. The method is used to motivate a general approach to task-oriented sensor fusion. We present experimental evidence that demonstrates the utility of the method.},
 author = {R Sim and G Dudek},
 doi = {https://doi.org/10.1016/S0262-8856(00)00109-8},
 issn = {0262-8856},
 journal = {Image and Vision Computing},
 keywords = {Environmental features, Pose estimation, Landmark detection},
 number = {11},
 pages = {733 - 739},
 title = {Learning environmental features for pose estimation},
 url = {http://www.sciencedirect.com/science/article/pii/S0262885600001098},
 volume = {19},
 year = {2001}
}

@article{BadraDudekZernike,
 author = {Fady Badra and
Ala Qumsieh and
Gregory Dudek},
 bibsource = {MRL, http://www.cim.mcgill.ca/~mrl/publications.htm},
 journal = {International Journal of Pattern Recognition and Artificial Intelligence - IJPRAI},
 number = {5},
 pages = {685-704},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/zernike_wacv.pdf},
 title = {Robust Mosaicing Using Zernike Moments.},
 volume = {13},
 year = {1999}
}

@inproceedings{Bourque1998c,
 abstract = {This paper describes an integrated system for the automatic construction of image-based virtual realities to describe a real environment. A mobile robot autonomously navigates through the environment and uses a camera to make observations. At locations that are deemed sufficiently interesting, panoramic images are collected that are used to construct a multi-node VR movie.

Images of the environment are classified in terms of two features related to human attention: edge element density and edge orientation. The system deems locations interesting if they are sufficiently different from the surrounding environment. The parameterization of the surrounding environment is computed either in a pre-computation pass, or on-line using a technique termed alpha-backtracking. The panoramic images that describe the environment are automatically joined together in a navigable movie that simulates motion in the real environment.},
 address = {Victoria, Canada},
 author = {Eric Bourque and Gregory Dudek},
 booktitle = {IROS},
 month = {October},
 pages = {526-532},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros98.pdf},
 title = {{V}iewpoint {S}election -- {A}n Autonomous Robotic System for Virtual
Environment Creation},
 volume = {1},
 year = {1998}
}

@inproceedings{dudek1998topological,
 author = {Dudek, G and Jenkin, M and Milios, E and Wilkes, D},
 booktitle = {7th International Symposium on Robotics with Application (ISORA)},
 number = {3},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/isora98.pdf},
 title = {Topological exploration with multiple robots},
 volume = {3},
 year = {1998}
}

@article{dudek1998siam,
 abstract = {We consider the problem of localizing a robot in a known environment modeled by a simple polygon P. We assume that the robot has a map of P but is placed at an unknown location inside P. From its initial location, the robot sees a set of points called the visibility polygon V of its location. In general, sensing at a single point will not suffice to uniquely localize the robot, since the set H of points in P with visibility polygon V may have more than one element. Hence, the robot must move around and use range sensing and a compass to determine its position (i.e., localize itself). We seek a strategy that minimizes the distance the robot travels to determine its exact location.

We show that the problem of localizing a robot with minimum travel is NP-hard. We then give a polynomial time approximation scheme that causes the robot to travel a distance of at most (k - 1)d, where k = |H|, which is no greater than the number of reflex vertices of P, and d is the length of a minimum length tour that would allow the robot to verify its true initial location by sensing. We also show that this bound is the best possible. },
 author = {Dudek, Gregory and Romanik, Kathleen and Whitesides, Sue},
 doi = {10.1137/S0097539794279201},
 eprint = {https://doi.org/10.1137/S0097539794279201},
 journal = {SIAM Journal on Computing},
 number = {2},
 pages = {583-604},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/siamj_abs.pdf},
 title = {Localizing a Robot with Minimum Travel},
 url = {https://doi.org/10.1137/S0097539794279201},
 volume = {27},
 year = {1998}
}

@mastersthesis{Bourque1998e,
 abstract = {This thesis presents an approach to the automated construction of visual maps, in the form of walk-throughs, of an unknown environment. Our approach is based on the selection of informative viewpoints within the environment. These viewpoints are locations in the environment which correspond to views containing maximal visual interest. This approach to environment representation is analogous to image compression. Our goal is to obtain a set of selections resembling those made by a human observer given the same task.

Our computational procedure is inspired by models of human visual attention outlined in the literature on human psychophysics. We make use of the underlying edge structure of a scene, as it is largely unaffected by variations in illumination. Our implementation uses a mobile robot to traverse the environment, and then builds an image-based virtual representation of the environment, only keeping the views whose responses were highest. We demonstrate the effectiveness of our attention operator on both single images, and in viewpoint selection within an unknown environment.},
 author = {Eric Bourque},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/thesis_3.pdf},
 school = {McGill University},
 title = {On the automated construction of image based maps},
 year = {1998}
}

@inproceedings{Bourque1998b,
 abstract = { We describe an approach to the automated construction of visual maps of an unknown environment. These maps take the form of image-based ``walk-throughs'' rather than 2D or 3D models. Our approach is based on the selection of informative viewpoints within the environment. These viewpoints are locations in the environment associated with views containing maximal visual interest. This approach to environment representation is analogous to image compression. Our goal is to obtain a set of representative views resembling those that would be selected by a human observer given the same task. Our computational procedure is inspired by models of human visual attention appearing in the literature on human psychophysics. We make use of the underlying edge structure of a scene, as it is largely unaffected by variations in illumination. Our implementation uses a mobile robot to traverse the environment, and then builds an image-based virtual representation of the environment, only keeping the views whose responses were highest. We demonstrate the effectiveness of our attention operator on both single images, and in viewpoint selection within an unknown environment. },
 author = {Eric Bourque and Gregory Dudek},
 booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition--Workshop on Perception of Mobile Agents},
 month = {June},
 pages = {61-70},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/wpma98.pdf},
 title = {Automated Image-Based Mapping},
 year = {1998}
}

@inproceedings{Bourque1998,
 abstract = {This paper describes the fully automatic creation of an environment's description using an image-based representation. This representation is a collection of cylindrical sample images combined into an "image-based virtual reality". The locations at which the environment will be sampled are chosen automatically using an operator inspired by models of human visual attention and saccadic motion.

The image acquisition is performed by a mobile robot. The selection of vantage points is based on an analysis of the edge structure of sampled panoramic images. In order to trade off the optimality of the generated description of the navigation effort required in solving the on-line problem, a concept referred to as alpha-backtracking is introduced. The paper illustrates sample data acquired by the procedure.},
 address = {Leuven, Belgium},
 author = {Eric Bourque and Gregory Dudek and Philippe Ciaravola},
 booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation},
 month = {May},
 pages = {3186-3191},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra98.pdf},
 title = {Robotic Sightseeing - A Method for Automatically Creating Virtual Environments},
 volume = {4},
 year = {1998}
}

@techreport{Burlington-AAAI,
 abstract = {This paper looks at the problem of searching the plane for an object with multiple agents, in co-ordinated search. Also the problem of how to deal with noise within the context of Sandor Fakete's Kissing Circles algorithm is addressed and a method of coping is conjectured. The relative performance of a family of redezvous search algorithms for mulit-agent search in the plane. Some very strict assumptions have been made to idealize the circumstance in order to simplify the analysis. },
 author = {Scott Burlington},
 instituion = {Center for Intelligent Machines, McGill University},
 month = {March},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/burl-cimtech.pdf},
 title = {Semi-Planar Performance Analysis and Kissing Circle Sensitivity},
 year = {1998}
}

@inproceedings{698646,
 abstract = { In this paper we discuss new results on the Shape From Darkness problem: using the motion of cast shadows to recover scene structure. Our approach is based on collecting a set of images from a fixed viewpoint as a known light source moves ``across the sky''. Previously published solutions to this problem have performed the reconstruction only for cross sections of the scene. In this paper, we present a reconstruction algorithm and discuss the reconstruction of an entire 3-D scene under various light source trajectories. We also consider the constraints on reconstruction. We conclude with experimental results that illustrate the convergence properties of the solution process and its robustness properties. },
 author = {M. {Daum} and G. {Dudek}},
 booktitle = {Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231)},
 number = {},
 pages = {461-468},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/cvpr98.pdf},
 title = {On 3-D surface reconstruction using shape from shadows},
 volume = {},
 year = {1998}
}

@inproceedings{daum1997accv,
 abstract = {Shape From Darkness refers to using the shadows cast by a scene to reconstruct the structure of the scene. A collection of images associated with different light source positions is used. Previously published solutions to this problem have performed the reconstruction only for cross sections of the scene.},
 address = {Berlin, Heidelberg},
 author = {Daum, M. and Dudek, G.},
 booktitle = {Computer Vision --- ACCV'98},
 editor = {Chin, Roland and Pong, Ting-Chuen},
 isbn = {978-3-540-69669-8},
 pages = {72--79},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/accv98.pdf},
 publisher = {Springer Berlin Heidelberg},
 title = {Out of the dark: Using shadows to reconstruct 3D surfaces},
 year = {1997}
}

@inproceedings{724877,
 abstract = {In this article a simple performance index for proper manipulator motion planning is presented. This index is derived by establishing a simple upper bound for a {\em standard} condition number of the Jacobian matrix. Here the proposed index behaviour is analyzed/tested on a planar redundant manipulator executing tasks under various scenarios. },
 author = {R. V. {Mayorga} and L. A. {Torres}},
 booktitle = {Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190)},
 number = {},
 pages = {1913-1918 vol.3},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros98_1.pdf},
 title = {Upper bound conditioning as a performance index for manipulator motion planning},
 volume = {3},
 year = {1998}
}

@inproceedings{Rekleitis1998,
 abstract = { This paper describes a technique for multi-agent exploration of an unknown environment, that improves the quality of the map by reducing the inaccuracies that occur over time from dead reckoning errors. We present an algorithmic solution, simulation results, as well as a cost analysis and experimental data. The approach is based on using a pair of robots that observe one another's behaviour, thus greatly reducing odometry errors. We assume the robots can both directly sense nearby obstacles and see one another. We have implemented both these capabilities with actual robots in our lab. By exploiting the ability of the robots to see one another, we can detect opaque obstacles in the environment independent of their surface reflectance properties.},
 address = {Vancouver, Canada},
 author = {Ioannis M. Rekleitis and Gregory Dudek and Evangelos E. Milios },
 booktitle = {Proceedings of Vision Interface 1998},
 month = {June},
 pages = {455-461},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/vi98.pdf},
 title = {On Multiagent Exploration},
 year = {1998}
}

@article{siddiqi1999shock,
 abstract = { A number of active contour models have been proposed which unify the curve evolution framework with classical energy minimization techniques for segmentation, such as snakes. The essential idea is to evolve a curve (in 2D) or a surface (in 3D) under constraints from image forces so that it clings to features of interest in an intensity image. Recently the evolution equation has been derived from first principles as the gradient flow that minimizes a modified length functional, tailored to features such as edges. However, because the flow may be slow to converge in practice, a constant (hyperbolic) term is added to keep the curve/surface moving in the desired direction. In this paper, we derive a modification of this term based on the gradient flow derived from a weighted area functional, with image dependent weighting factor. When combined with the earlier modified length gradient flow we obtain a pde which offers a number of advantages, as illustrated by several examples of shape segmentation on medical images. In many cases the weighted area flow may be used on its own, with significant computational savings. },
 author = {Siddiqi, Kaleem and Shokoufandeh, Ali and Dickinson, Sven J and Zucker, Steven W},
 journal = {International Journal of Computer Vision},
 number = {1},
 pages = {13--32},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/itip98_tr.pdf},
 publisher = {Springer},
 title = {Shock graphs and shape matching},
 volume = {35},
 year = {1999}
}

@article{siddiqi2001psychophysics,
 abstract = {We earlier introduced an approach to categorical shape description based on the singularities (shocks) of curve evolution equations. We now consider the simplest compositions of shocks, and show that they lead to three classes of parametrically ordered shape sequences, organized along the sides of a shape triangle. By conducting several psychophysical experiments we demonstrate that shock-based descriptions are predictive of performance in shape perception. Most significantly, the experiments reveal a fundamental difference between perceptual effects dominated by when shocks form with respect to one another, versus those dominated by where they form. The shock-based theory provides a foundation for unifying tasks as diverse as shape bisection, recognition, and categorization. },
 author = {Siddiqi, Kaleem and Kimia, Benjamin B and Tannenbaum, Allen and Zucker, Steven W},
 journal = {Vision Research},
 number = {9},
 pages = {1153--1178},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/yaletr98.pdf},
 publisher = {Elsevier},
 title = {On the psychophysics of the shape triangle},
 volume = {41},
 year = {2001}
}

@article{siddiqi1999shock,
 abstract = { We have been developing a theory for the generic representation of 2-D shape, where structural descriptions are derived from the shocks (singularities) of a curve evolution process, acting on bounding contours. We now apply the theory to the problem of shape matching. The shocks are organized into a directed, acyclic shock graph, and complexity is managed by attending to the most significant (central) shape components first. The space of all such graphs is highly structured and can be characterized by the rules of a shock graph grammar. The grammar permits a reduction of a shock graph to a unique rooted shock tree. We introduce a novel tree matching algorithm which finds the best set of corresponding nodes between two shock trees in polynomial time. Using a diverse database of shapes, we demonstrate our system's performance under articulation, occlusion, and changes in viewpoint. },
 author = {Siddiqi, Kaleem and Shokoufandeh, Ali and Dickinson, Sven J and Zucker, Steven W},
 journal = {International Journal of Computer Vision},
 number = {1},
 pages = {13--32},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ijcv98_tr.pdf},
 publisher = {Springer},
 title = {Shock graphs and shape matching},
 volume = {35},
 year = {1999}
}

@mastersthesis{Sim1998e,
 abstract = { We present an approach to vision-based mobile robot localisation. That is, the task of obtaining a precise position estimate for a robot in a previously explored environment, even without an a priori estimate. Our approach combines the strengths of statistical and feature-based methods. This is accomplished by learning a set of visual features called landmarks, each of which is detected as a local extremum of a measure of uniqueness and represented by an appearance-based encoding. Localisation is performed using a method that matches observed landmarks to learned prototypes and generates independent position estimates for each match. The independent estimates are then combined to obtain a final position estimate, with an associated uncertainty. Experimental evidence shows that an estimate accurate to a fraction of the environment sampling density can be obtained for a wide range of parameterisations, even under scaling of the explored region, and changes in sampling density. },
 author = {Robert Sim},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/thesis_4.pdf},
 school = {McGill University},
 title = {Mobile robot localization using learned landmarks},
 year = {1998}
}

@inproceedings{Sim1998c,
 abstract = { This paper presents an approach to vision-based mobile robot localization. In an attempt to capitalize on the benefits of both image and landmark-based methods, we describe a method that combines their strengths. Images are encoded as a set of visual features called landmarks. Potential landmarks are detected using an attention mechanism implemented as a measure of uniqueness. They are then selected and represented by an appearance-based encoding. Localization is performed using a landmark tracking and interpolation method which obtains an estimate accurate to a fraction of the environment sampling density. Experimental results are shown to confirm the feasibility and accuracy of the method. },
 address = {Victoria, Canada},
 author = {Robert Sim and Gregory Dudek},
 booktitle = {IROS},
 month = {October},
 pages = {1060--1065},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros98_2.pdf},
 title = {Mobile robot localization from learned landmarks},
 volume = {2},
 year = {1998}
}

@inproceedings{Simhon1998c,
 abstract = { In this paper we describe a method of mapping large scale static environments using a hybrid topological-metric model. A global map is formed from a set of local maps organized in a topological structure. Each local map contains quantitative environment information using a local reference frame. They are denoted as islands of reliability because they provide accurate metric information of the environment. The mapping problem then becomes where to place the islands of reliability and to what extent should they cover the environment. This is accomplished by defining the placement criteria in terms of the task the islands of reliability portray. },
 address = {Victoria, Canada},
 author = {Saul Simhon and Gregory Dudek},
 booktitle = {IROS},
 month = {October},
 pages = {1708--1714},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros98_3.pdf},
 title = {A Global Topological Map formed by Local Metric Maps},
 volume = {3},
 year = {1998}
}

@article{article,
 author = {Dudek, Gregory and Romanik, Kathleen and Whitesides, Sue},
 doi = {10.1137/S0097539794279201},
 journal = {SIAM Journal on Computing},
 month = {12},
 pages = {},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/global_localization_siam.pdf},
 title = {Localizing a Robot with Minimum Travel},
 volume = {27},
 year = {1996}
}

@mastersthesis{Simhon1998e,
 abstract = { This thesis describes a method for mapping unknown large scale static environments using a hybrid of topological and metric representations. A global map is formed from a set of local maps for sub-regions of the environments. Each local map contains quantitative environment information used to define a local reference frame. These maps are referred to as islands of reliability because they are associated with the sub-regions whose local structure is best matched to the sensors we are using. The connectivity of these islands is represented topologically. The key mapping problem we consider is where to place the islands of reliability and to what extent they should cover the environment. This is accomplished by defining the placement criteria in terms of the task to be satisfied and the uncertainties of the mapping agent. Islands are distributed about the environment at areas suitable for extracting metric information relevant to a localization task. },
 author = {Saul Simhon},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/thesis_5.pdf},
 school = {McGill University},
 title = {Islands of Reliability for Dual Topological-Metric Mapping},
 year = {1998}
}

@inproceedings{Simhon1998,
 address = {Leuven, Belgium},
 author = {Saul Simhon and Gregory Dudek},
 booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation},
 month = {May},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra98_1.pdf},
 title = {Selecting Targets for Local Reference Frames},
 volume = {4},
 year = {1998}
}

@article{dudek1998profile,
 author = {Dudek, Gregory},
 journal = {AI Magazine},
 number = {3},
 pages = {21},
 publisher = {Association for the Advancement of Artificial Intelligence},
 title = {Profile of a Winner: McGill University},
 volume = {19},
 year = {1998}
}

@inproceedings{badra1998zernike,
 address = {Vancouver, BC, Canada},
 author = {Badra and Fady and Ala Qumsieh and Gregory Dudek},
 booktitle = {Proc. of Vision Interface},
 pages = {},
 publisher = {},
 series = {},
 title = {Image Registration Using Zernike Moments},
 year = {1998}
}

@inproceedings{698646,
 author = {M. {Daum} and G. {Dudek}},
 booktitle = {Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231)},
 number = {},
 pages = {461-468},
 title = {On 3-D surface reconstruction using shape from shadows},
 volume = {},
 year = {1998}
}

@article{lecours1998agnosia,
 author = {Lecours, S and M. Arguin and D. Bub and G. Dudek and S. Caille and S. Fontaine},
 journal = {Brain and Cognition},
 number = {1},
 pages = {138-141},
 title = {A semantic proximity effect on object recognition in visual agnosia for biological kinds},
 volume = {37},
 year = {1998}
}

@inproceedings{727439,
 author = {R. {Sim} and G. {Dudek}},
 booktitle = {Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190)},
 number = {},
 pages = {1060-1065 vol.2},
 title = {Mobile robot localization from learned landmarks},
 volume = {2},
 year = {1998}
}

@inproceedings{bolduc1997autonomous,
 author = {Bolduc, Marc and Bourque, Eric and Dudek, Gregory and Roy, Nicholas and Sim, Robert},
 booktitle = {AAAI/IAAI},
 pages = {779--780},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/aaai97.pdf},
 title = {Autonomous exploration: An integrated systems approach},
 year = {1997}
}

@inproceedings{Bourque1997,
 abstract = {While virtual reality is a powerful tool for a range of applications, it has the following two associated overheads that fundamentally limit its usefulness:

1. The creation of realistic synthetic virtual environment models is difficult and labour intensive;
2. The computing resources needed to render realistic complex environments in real time are substantial.

In this paper, we describe an approach to the fully automated creation of image based virtual reality (VR) models: collections of panoramic images (cylindrical or spherical images) that illustrate an environment. Traditionally, a key bottleneck for this kind of modelling is the selection and acquisition of sample data. Our approach is based on using a small mobile robot to navigate in the environment and collect the image data of interest. A critical issue is selecting the appropriate sample locations of the modelling process: this is addressed using a computational mechanism that resembles human attention. Our objective is to select regions that differ from the surrounding environment. We do this using statistical properties of the output of an edge operator.

Specifically, we guide a camera-carrying mobile robot through an environment and have it acquire data with which we construct a VR model. We then demonstrate the effectiveness of our approach using real data.},
 address = {Pittsburgh, PA},
 author = {Eric Bourque and Gregory Dudek},
 booktitle = {Proc. SPIE Conference on Intelligent Systems and Manufacturing},
 month = {October},
 pages = {292-303},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/spie97.pdf},
 title = {Automated Creation of Image-Based Virtual Reality},
 year = {1997}
}

@inproceedings{Lacroix97,
 abstract = {In this paper, we are interested in inferring the sources of various types of sonar features typically observed by a mobile robot. After a brief discussion of terrestrial sonar sensing, we develop a set of operators that associates arc-shaped features extracted from sonar scans with real world primitives. Our classification scheme is probabilistic and is based on empirical data: the confidence of the association hypotheses produced by the operators is evaluated statistically. Some of our experimental results suggest that methods based on models of perfect sonar sensors may not be completely consistent with observed data. The management and merging of a collection of hypotheses concerning various sonar features allows the system to produce a coherent and mutually-compatible set of inferences for the entire observed environment.},
 address = {Grenoble, France},
 author = {Simon Lacroix and Gregory Dudek},
 booktitle = {Proc. IEEE/RSJ International Conference on Intelligent
Robots and Systems},
 month = {September},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros97.pdf},
 title = {On the Identification of Sonar Features},
 year = {1997}
}

@mastersthesis{lagarde1997thesis,
 abstract = {This thesis deals with the problem of recovering the local structure of surfaces from discrete range data. It is assumed that this recovery is done mostly in a bottom-up fashion, that is, without the help of a priori knowledge about the viewed surface.

Because the problem is ill-posed, we nevertheless need to place constraints on the recovered structure to get a unique solution. In a bottom-up approach, these constraints must come from generic assumptions that apply to all surfaces.

Many methods of bottom-up surface reconstruction have been proposed up to now, some of them dealing with intensity surfaces, some with range surfaces. Each of these methods either explicitly or implicitly applies a set of constraints on the data. The way in which the constraints are applied also varies from method to method. The main contribution of this thesis is some success at unifying a number of those methods under a common formalism of energy minimization, which will permit to better compare the choice of constraints between methods. We also show that the most successful surface reconstruction methods form idempotent operators, which we argue is to be expected.

One method, Sander's curvature consistency, is studied in more detail than the others because it has not been studied much elsewhere yet. },
 address = {},
 author = {Jean W. Lagarde},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/lagarde-local-structure.pdf},
 school = {McGill University},
 title = {Constraints and Their Satisfaction in the Recovery of Local Surface Structure},
 year = {1997}
}

@inproceedings{rekleitis1997ijcai,
 abstract = { This paper deals with the intelligent exploration of an unknown environment by autonomous robots. In particular, we present an algorithm and associated analysis for collaborative exploration using two mobile robots. Our approach is based on robots with range sensors limited by distance. By appropriate behavioural strategies, we show that odometry (motion) errors that would normally present problems for mapping can be severely reduced. Our analysis includes polynomial complexity bounds and a discussion of possible heuristics.},
 address = {Nagoya, Japan},
 author = {Ioannis Rekleitis, and Gregory Dudek, and Evangelos Milios},
 booktitle = {International Joint Conference in Artificial Intelligence},
 editor = {IJCAI},
 month = {August},
 pages = {1340-1345},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ijcai97.pdf},
 publisher = {Morgan Kaufmann Publishers, Inc. },
 title = {Multi-Robot Exploration of an Unknown Environment, Efficiently Reducing the Odometry Error},
 volume = {2},
 year = {1997}
}

@inproceedings{Roy1997ewlr,
 abstract = {We consider the problem of rendezvous between two robots collaborating in learning the layout of an unknown environment. That is, how can two autonomous exploring agents that cannot communicate with one another over long distances meet if they start exploring at different locations in an unknown environment. The intended application is collaborative map exploration. Ours is the first work to formalize the characteristics of the rendezvous problem, and we approach it by proposing several alternative algorithms that the robots could use in attempting to rendezvous quickly while continuing to explore. The algorithms are based on the assumption that potential rendezvous locations, referred to as landmarks, can be determined by the robots as they explore; these locations are based on a distinctiveness measure computed with an arbitrary sensor. We consider the performance of our proposed algorithms analytically with respect to both expected- and worst-case behaviour. We then examine their behaviour under a wider set of conditions in simulation.},
 address = {Brighton, England},
 author = {Nicholas Roy and Gregory Dudek},
 booktitle = {Proc. 6th European Workshop on Learning Robots},
 month = {Aug.},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ewlr97.pdf},
 refnumber = {3 conference paper},
 title = {Learning to Rendezvous during Multi-agent Exploration},
 year = {1997}
}

@article{dudek1991shape,
 author = {Dudek, Gregory and Tsotsos, John K},
 journal = {Computer Vision Graphics and Image Processing: Image Understanding},
 number = {1},
 pages = {170-189},
 title = {Shape representation and recognition from curvature},
 volume = {68},
 year = {1997}
}

@inproceedings{dudek1997aaai,
 address = {Providence, RI},
 author = {Dudek, G. and Roy, N.},
 booktitle = {Proc. of American Association for Artificial Intelligence (AAAI) Workshop on On-Line Search},
 pages = {22-29},
 title = {On-line rendezvous selection for robot exploration},
 year = {1997}
}

@inproceedings{dudek1997icar,
 address = {Bremen, Germany},
 author = {Dudek, G. and M. Jenkin and E. Milios and D. Wilkes},
 booktitle = {Proc. 3rd ECPD Int. Conf. on Advanced Robotics, Intelligent Automation and Active Systems},
 pages = {132-144},
 title = {On building and navigating with a globally topological but locally metric map},
 year = {1997}
}

@article{DUDEK1997159,
 abstract = {This paper deals with the validation of topological maps of an environment by an active agent (such as a mobile robot), and the localization of an agent in a given map. The agent is assumed to have neither compass nor other instruments for measuring orientation or distance, and, therefore, no associated metrics. The topological maps considered are similar to conventional graphs. The robot is assumed to have enough sensory capability to traverse graph edges autonomously, recognize when it has reached a vertex, and enumerate edges incident upon the current vertex, locating them relative to the edge via which it entered the current vertex. In addition, the robot has access to a set of visually detectable, portable, distinct markers. We present algorithms, along with worst case complexity bounds and experimental results for representative classes of graphs for two fundamental problems. The first problem is the validation problem: if the robot is given an input map and its current position and orientation with respect to the map, determine whether the map is correct. The second problem is the self-location problem: Given only a map of the environment, determine the position of the robot and its “orientation” (i.e., the correspondence between edges of the map and edges in the world at the robot's position). Finally, we consider the power of some other non-metric aids in exploration.},
 author = {G Dudek and M Jenkin and E Milios and D Wilkes},
 doi = {https://doi.org/10.1016/S0921-8890(97)00030-4},
 issn = {0921-8890},
 journal = {Robotics and Autonomous Systems},
 keywords = {Mobile robotics, Robot navigation, Robot mapping, Robot exploration, Topological mapping, Non-metric mapping},
 number = {2},
 pages = {159 - 178},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/1-s2.0-S0921889097000304-main.pdf},
 title = {Map validation and robot self-location in a graph-like world},
 url = {http://www.sciencedirect.com/science/article/pii/S0921889097000304},
 volume = {22},
 year = {1997}
}

@incollection{dudek1997coding,
 author = {Dudek, Gregory and Arguin, Martin and Dixon, Michael and Bub, Daniel},
 booktitle = {Computational and psychophysical mechanisms of visual coding},
 pages = {44--60},
 publisher = {Cambridge University Press New York},
 title = {Coding simple shapes for recognition and the integration of shape descriptors},
 year = {1997}
}

@article{article,
 author = {Oore, Sageev and Hinton, Geoffrey and Dudek, Gregory},
 doi = {10.1162/neco.1997.9.3.683},
 journal = {Neural Computation},
 month = {03},
 pages = {683-699},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ohd97.pdf},
 title = {A Mobile Robot that Learns its Place.},
 volume = {9},
 year = {1997}
}

@article{BadraDudekZernike,
 author = {Fady Badra and
Ala Qumsieh and
Gregory Dudek},
 bibsource = {MRL, http://www.cim.mcgill.ca/~mrl/publications.htm},
 journal = {International Journal of Pattern Recognition and Artificial Intelligence - IJPRAI},
 number = {5},
 pages = {685-704},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/zernike_wacv_1.pdf},
 title = {Robust Mosaicing Using Zernike Moments.},
 volume = {13},
 year = {1999}
}

@inproceedings{546147,
 abstract = {This paper introduces a monocular optical flow algorithm that has been shown to perform well at nearly real-time frame rates (4 FPS on a 100 MHz SGI Indy workstation), using natural image sequences. The system is completely bottom-up, using pixel region-matching techniques. A coordinated gradient descent method is broken down into two stages; pixel region matching error measures are locally minimized, and flow field consistency constraints apply non-linear adaptive diffusion, causing confident measurements to influence their less confident neighbors. Convergence is usually accomplished with one iteration for an image frame pair. Temporal integration and Kalman filtering predicts upcoming flow fields. The algorithm is designed for flexibility: large displacements are tracked as easily as sub-pixel displacements, and higher-level information can feed flow field predictions into the measurement process.

Note: this version (TR-CIM-96-09) has more theoretical discussion and more evolved experimental results than the earlier version (TR-CIM-95-12). },
 author = {S. M. {Benoit} and F. P. {Ferrie}},
 booktitle = {Proceedings of 13th International Conference on Pattern Recognition},
 number = {},
 pages = {864-868 vol.1},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/benoit-monocular-optflow2.pdf},
 title = {Monocular optical flow for real-time vision systems},
 volume = {1},
 year = {1996}
}

@inproceedings{dudek1996vision-interface,
 abstract = {This paper describes an approach to combining range data from both a set of sonar sensors as well as from a directional laser range finder to efficiently take advantage of the characteristics of both types of devices when exploring and mapping unknown worlds. We call our approach ``just in time sensing'' because it uses the more accurate but constrained laser range sensor only as needed, based upon a preliminary interpretation of sonar data. In this respect, it resembles ``just in time'' inventory control which attempts to judiciously obtain materials for industrial manufacturing only when and as needed. Experiments with a mobile robot equipped with sonar and a laser rangefinder demonstrate that by judiciously using the more accurate but more complex laser rangefinder to deal with the well-known ambiguity which arises in sonar data, we are able to obtain a much better map of an interior space at little additional cost (in terms of time and computational expense). },
 address = {Toronto},
 author = {Gregory Dudek, and Paul Freedman and Ioannis M. Rekleitis},
 booktitle = {Vision Interface},
 month = {May},
 pages = {175-182},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/vi96b.pdf},
 title = {Environment Exploration Using ``Just-in-Time'' Sensor Fusion},
 year = {1996}
}

@inproceedings{dudek1996icra,
 abstract = { This paper describes an approach to combining range data from both a set of sonar sensors as well as from a directional laser range finder to efficiently take advantage of the characteristics of both types of devices when exploring and mapping unknown worlds. We call our approach ``just in time sensing'' because it uses the more accurate but constrained laser range sensor only as needed, based upon a preliminary interpretation of sonar data. In this respect, it resembles ``just in time'' inventory control which attempts to judiciously obtain materials for industrial manufacturing only when and as needed. Experiments with a mobile robot equipped with sonar and a laser rangefinder demonstrate that by judiciously using the more accurate but more complex laser rangefinder to deal with the well-known ambiguity which arises in sonar data, we are able to obtain a much better map of an interior space at little additional cost (in terms of time and computational expense). },
 author = {Gregory Dudek, and Paul Freedman and Ioannis M. Rekleitis},
 booktitle = {International Conference in Robotics and Automation},
 month = {April},
 organization = {IEEE},
 pages = {667-671},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra96.pdf},
 title = {Just-in-time sensing: efficiently combining sonar and laser range data for exploring unknown worlds},
 volume = {1},
 year = {1996}
}

@mastersthesis{rekleitis1995thesis,
 abstract = { When the relative velocity between the different objects in a scene and the camera is relative large -- compared with the camera's exposure time -- in the resulting image we have a distortion called motion blur. In the past, a lot of algorithms have been proposed for estimating the relative velocity from one or, most of the time, more images. The motion blur is generally considered an extra source of noise and is eliminated, or is assumed nonexistent. Unlike most of these approaches, it is feasible to estimate the Optical Flow map using only the information encoded in the motion blur. This thesis presents an algorithm that estimates the velocity vector of an image patch using the motion blur only, in two steps. The information used for the estimation of the velocity vectors is extracted from the frequency domain, and the most computationally expensive operation is the Fast Fourier Transform that transforms the image from the spatial to the frequency domain. Consequently, the complexity of the algorithm is bound by this operation into O(n log(n)). The first step consists of using the response of a family of steerable filters applied on the log of the Power Spectrum in order to calculate the orientation of the velocity vector. The second step uses a technique called Cepstral Analysis. More precisely, the log power spectrum is treated as another signal and we examine the Inverse Fourier Transform of it in order to estimate the magnitude of the velocity vector. Experiments have been conducted on artificially blurred images and with real world data, and an error analysis on these results is also presented.},
 address = {Montreal, Quebec, Canada},
 author = {Ioannis M. Rekleitis},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icip96.pdf},
 school = {School of Computer Science, McGill University},
 title = {Visual Motion Estimation based on Motion Blur Interpretation},
 year = {1995}
}

@inproceedings{rekleitis1996vision-interface,
 abstract = { This paper considers the explicit use of motion blur to compute the Optical Flow. In the past, many algorithms have been proposed for estimating the relative velocity from one or more images. The motion blur is generally considered an extra source of noise and is eliminated, or is assumed nonexistent. Unlike most of these approaches, it is feasible to estimate the Optical Flow map using only the information encoded in the motion blur. An algorithm that estimates the velocity vector of an image patch using the motion blur only is presented; all the required information comes from the frequency domain. The first step consists of using the response of a family of steerable filters applied on the log of the Power Spectrum in order to calculate the orientation of the velocity vector. The second step uses a technique called Cepstral Analysis. More precisely, the log power spectrum is treated as another signal and we examine the Inverse Fourier Transform of it in order to estimate the magnitude of the velocity vector. Experiments have been conducted on artificially blurred images and with real world data. },
 address = {Toronto},
 author = {Ioannis M. Rekleitis},
 booktitle = {Vision Interface},
 month = {May},
 pages = {159-166},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/vi96a.pdf},
 title = {Steerable Filters and Cepstral Analysis for Optical Flow Calculation from a Single Blurred Image},
 year = {1996}
}

@inproceedings{roy1996mobile,
 author = {Roy, Nicholas and Dudek, Gregory and Daum, Michael},
 booktitle = {PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE},
 pages = {1356--1356},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/aaai96.pdf},
 title = {Mobile robot navigation and control: A case study},
 year = {1996}
}

@inproceedings{506874,
 abstract = {We consider the problem of rendezvous between two robots exploring an unknown environment. That is, how can two autonomous exploring agents that cannot communicate with one another over long distances meet if they start exploring at different locations in an unknown environment. The intended application is collaborative map exploration.

Ours is the first work to formalize the characteristics of the rendezvous problem, and we approach it by proposing several alternative algorithms that the robots could use in attempting to rendezvous quickly while continuing to explore. The algorithms are based on the assumption that potential rendezvous locations, called landmarks, can be selected by the robots as they explore; these locations are based on a distinctiveness measure computed with an arbitrary sensor.

We consider the performance of our proposed algorithms analytically with respect to both expected- and worst-case behaviour. We then examine their behaviour under a wider set of conditions in simulation. },
 author = {N. {Roy} and G. {Dudek} and P. {Freedman}},
 booktitle = {Proceedings of IEEE International Conference on Robotics and Automation},
 number = {},
 pages = {1224-1228 vol.2},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra96_1.pdf},
 title = {Surface sensing and classification for efficient mobile robot navigation},
 volume = {2},
 year = {1996}
}

@mastersthesis{wu1996,
 abstract = {A new approach for computing qualitative part-based descriptions of 3D objects from single- and multi-view range data is presented. This research is motivated by both a theory of human image understanding (Recognition-by-Components) and the need for qualitative recognition by an autonomous robot in order for it to efficiently interact with its environment.

\vspace{3mm} Object descriptions are obtained in two consecutive steps: (1) object segmentation into parts and (2) part model identification. Segmentation is achieved by first computing the simulated electrical charge density distribution on a tessellated triangular mesh of the object surface. The algorithm then detects the object part boundaries where the the charge density achieves a local minimum. The charge density distribution can simultaneously provide an indication of the gross and fine object structures. Parametric geons are introduced as the part models, which indicate both qualitative shape and quantitative attribute information. Model recovery is achieved by fitting all parametric geons to a part and then selecting the best model based on the minimum fitting residual. A new objective function used for model recovery is optimised by a global optimisation technique (Very Fast Simulated Re-Annealing).

\vspace{3mm} The advantages of this approach are demonstrated through experimentation. By using a physical analogy to the well known transversality principle, part segmentation does not require an assumption of surface smoothness or the choice of a particular scale to compute local surface features. The formulation for parametric geons provides a global shape constraint, which ensures reliable part model recovery even when the part shape is not an exact instance of a parametric geon. By directly comparing a part with all candidate models, this approach explicitly verifies the shape of the resultant part descriptions. The computed part-based descriptions are well suited for the object recognition task carried out by an autonomous robot. },
 author = {Kenong Wu},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/thesis_6.pdf},
 school = {McGill University},
 title = {Computing Parametric Geon Descriptions of 3D Multi-Part Objects},
 year = {1996}
}

@inproceedings{503576,
 abstract = {We consider the problem of locating a robot in an initially-unfamiliar environment from visual input. The robot is not given a map of the environment, but it does have access to a collection of training examples, each of which specifies the video image observed when the robot is at a particular location and orientation.

We address two variants of this problem: how to estimate translation of a moving robot assuming the orientation is known, and how to estimate translation and orientation for a mobile robot. Performing scene reconstruction to construct a metric map of the environment using only video images is difficult. We avoid this by using an approach in which the robot learns to convert a set of image measurements into a representation of its pose (position and orientation). This provides a metric estimate of the robot's location within a region covered by the statistical map we build. Localization can be performed on-line without a prior location estimate. The conversion from visual data to camera pose is implemented using a multi-layer neural network that is trained using backpropagation. An aspect of the approach is the use of an inconsistency measure to eliminate incorrect data and estimate components of the pose vector. The experimental data reported in this paper suggests that the accuracy and flexibility of the technique is good, while the on-line computational cost is very low. },
 author = {G. {Dudek} and C. {Zhang}},
 booktitle = {Proceedings of IEEE International Conference on Robotics and Automation},
 number = {},
 pages = {76-82 vol.1},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra96_2.pdf},
 title = {Vision-based robot localization without explicit object models},
 volume = {1},
 year = {1996}
}

@article{dudek1996jrs,
 author = {Dudek, Gregory and Paul Freedman and Souad Hadjres},
 journal = {Journal of Robotic Systems},
 number = {8},
 pages = {539-559},
 title = {Using multiple models for environmental mapping},
 volume = {13},
 year = {1996}
}

@inproceedings{daum1996shape,
 address = {Nagoya, Japan},
 author = {Daum, Michael and Gregory Dudek},
 booktitle = {Proc. US/Japan Student Forum (in affiliation with RSJ/IEEE IROS conference)},
 title = {Shape from Darkness in 3 Dimensions Using Information from Solar Trajectories},
 year = {1996}
}

@article{arguin1996vc,
 abstract = { A series of experiments was conducted on a patient (ELM) with bilateral inferior temporal lobe damage and category-specific visual agnosia in order to specify the nature of his functional impairment. In Experiment 1, ELM performed a task of picture/word matching that used line drawings of fruits and vegetables as stimuli. The pattern of confusions exhibited by the patient suggested a failure in processing the full range of shape features necessary for the unique specification of the target relative to other structurally related items. This hypothesis of a shape integration impairment was tested and verified by subsequent visual recognition experiments (Experiments 2-4), which used synthetic stimuli with shapes precisely defined on the dimensions of elongation, curvature, and tapering. Furthermore, it was determined (Experiment 5) that the integration deficit is specific to the retrieval of shape knowledge from memory and does not affect the encoding of the properties of visual stimuli. It is argued that these findings have critical implications for cognitive theories of visual object recognition and for an interpretation of the visual function of the inferior temporal cortex. Finally, it was shown that the patient's deficit for structural knowledge integration is modulated by the semantic properties of the objects (Experiment 6), thereby demonstrating the applicability of the present findings to an explanation of category-specific visual agnosia. },
 author = {Martin Arguin and Daniel Bub and Gregory Dudek},
 doi = {10.1080/713756740},
 eprint = { https://doi.org/10.1080/713756740},
 journal = {Visual Cognition},
 number = {3},
 pages = {221-276},
 publisher = {Routledge},
 title = {Shape Integration for Visual Object Recognition and Its Implication in Category-Specific Visual Agnosia},
 url = {https://doi.org/10.1080/713756740},
 volume = {3},
 year = {1996}
}

@article{542415,
 author = {G. {Dudek}},
 journal = {Proceedings of the IEEE},
 number = {11},
 pages = {1684-1704},
 title = {Environment representation using multiple abstraction levels},
 volume = {84},
 year = {1996}
}

@inproceedings{nigel1996vision,
 address = {Toronto ON, Canada},
 author = {Nigel Ayoung-Chee and Gregory Dudek and Frank Ferrie},
 booktitle = {Proc. of Vision Interface},
 pages = {104-109},
 title = {Enhanced 3D Representation Using Multiple Models},
 year = {1996}
}

@incollection{nigel1996lecturenotes,
 author = {Nigel Ayoung-Chee and Gregory Dudek and Frank Ferrie},
 booktitle = {Lecture Notes in Computer Science},
 editor = {Jean Ponce and Andrew Zisserman and Martial Hebert},
 notes = {Based on ECCV '96 International Workshop Cambridge, U.K., April, 1996 Proceedings},
 pages = {321-333},
 title = {A hybrid approach to 3D representation},
 volume = {1144},
 year = {1996}
}

@inproceedings{546091,
 author = {N. {Ayoung-Chee} and G. {Dudek} and F. P. {Ferrie}},
 booktitle = {Proceedings of 13th International Conference on Pattern Recognition},
 number = {},
 pages = {575-579 vol.1},
 title = {Enhanced 3D representation using a hybrid model},
 volume = {1},
 year = {1996}
}

@article{dudek1996taxonomy,
 abstract = {A key difficulty in the design of multi-agent robotic systems is the size and complexity of the space of possible designs. In order to make principled design decisions, an understanding of the many possible system configurations is essential. To this end, we present a taxonomy that classifies multi-agent systems according to communication, computational and other capabilities. We survey existing efforts involving multi-agent systems according to their positions in the taxonomy. We also present additional results concerning multi-agent systems, with the dual purposes of illustrating the usefulness of the taxonomy in simplifying discourse about robot collective properties, and also demonstrating that a collective can be demonstrably more powerful than a single unit of the collective.},
 author = {Dudek, Gregory and Jenkin, Michael R. M. and Milios, Evangelos and Wilkes, David},
 doi = {10.1007/BF00240651},
 issn = {1573-7527},
 journal = {Autonomous Robots},
 number = {4},
 pages = {375-397},
 title = {A taxonomy for multi-agent robotics},
 url = {\url{https://doi.org/10.1007/BF00240651}},
 volume = {3},
 year = {1996}
}

@inproceedings{nigel1996iwor,
 address = {Cambridge, England},
 author = {Nigel Ayoung-Chee and Gregory Dudek and Frank Ferrie},
 booktitle = {Proceedings of the IEEE International Workshop on Object Representation},
 pages = {202-209},
 title = {A Hybrid Approach to 3D Representation},
 year = {1996}
}

@inproceedings{arbel1996eccv,
 abstract = {In this paper we introduce a method for distinguishing between informative and uninformative viewpoints as they pertain to an active observer seeking to identify an object in a known environment. The method is based on a generalized inverse theory using a probabilistic framework where assertions are represented by conditional probability density functions. Experimental results are presented showing how the resulting algorithms can be used to distinguish between informative and uninformative viewpoints, rank a sequence of images on the basis of their information (e.g. to generate a set of characteristic views), and sequentially identify an unknown object.},
 address = {Berlin, Heidelberg},
 author = {Arbel, Tal and Ferrie, Frank P.},
 booktitle = {Computer Vision --- ECCV '96},
 editor = {Buxton, Bernard and Cipolla, Roberto},
 isbn = {978-3-540-49949-7},
 pages = {467--481},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/arbel-informative-views2.pdf},
 publisher = {Springer Berlin Heidelberg},
 title = {Informative views and sequential recognition},
 year = {1996}
}

@inproceedings{576328,
 abstract = {This thesis describes a new framework for parametric shape recognition. The key result is a method for generating classifiers in the form of conditional probability densities for recognizing an unknown from a set of reference models. Our procedure is automatic. Off-line, it invokes an autonomous process to estimate reference model parameters and their statistics. On-line, during measurement, it combines these with a priori context-dependent information, as well as the parameters and statistics estimated for an unknown object, into a conditional probability density function, which represents the belief in the assertion that the unknown is a particular reference model. Consequently, the method also permits the assessment of the beliefs associated with a set of assertions based on data acquired from a particular viewpoint. The importance of this result is that it provides a basis by which an external agent can assess the quality of the information from a particular viewpoint, and make informed decisions as to what action to take using the data at hand.

The thesis also describes the implementation of this procedure in a system for automatically generating and recognizing 3D part-oriented models. We show that recognition performance is near perfect for cases in which complete surface information is accessible to the algorithm, and that it falls off gracefully when only partial information is available. This leads to a sequential recognition strategy in which evidence is accumulated over successive viewpoints (at the level of the belief distribution) until a definitive assertion can be made. Experimental results are presented showing how the resulting algorithms can be used to distinguish between informative and uninformative viewpoints, rank a sequence of images on the basis of their information (e.g. to generate a set of characteristic views), and sequentially identify an unknown object. },
 author = {T. {Arbel} and P. {Whaite} and F. P. {Ferrie}},
 booktitle = {Proceedings of 12th International Conference on Pattern Recognition},
 number = {},
 pages = {470-476 vol.1},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/arbel-masters-thesis.pdf},
 title = {Recognizing volumetric objects in the presence of uncertainty},
 volume = {1},
 year = {1994}
}

@inproceedings{Dudek1995b,
 abstract = {We consider the problem of localizing a robot in a known environment modeled by a simple polygon $P$. We assume that the robot has a map of $P$ but is placed at an unknown location inside $P$. From its initial location, the robot sees a set of points called the visibility polygon $V$ of its location. In general, sensing at a single point will not suffice to uniquely localize the robot, since the set $H$ of points in $P$ with visibility polygon $V$ may have more than one element. Hence, the robot must move around and use range sensing and a compass to determine its position (i.e.\ localize itself). We seek a strategy that minimizes the distance the robot travels to determine its exact location. We show that the problem of localizing a robot with minimum travel is NP-hard. We then give a polynomial time approximation scheme that causes the robot to travel a distance of at most $(k-1)d$, where $k = |H|$ and $d$ is the length of a minimum length tour that would allow the robot to verify its true initial location by sensing. We also show that this bound is the best possible. },
 address = {San Francisco, CA},
 author = {Gregory Dudek and Kathleen Romanik and Sue Whitesides},
 booktitle = {Proc. Sixth ACM-SIAM Symposium of Discrete Algorithms (SODA)},
 month = {January},
 pages = {437-446},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/soda95.pdf},
 title = {Localizing a Robot with Minimum Travel},
 url = {ftp://ftp.cim.mcgill.ca/pub/mobile-robot/paper:soda-localization-complexity.ps.Z},
 year = {1995}
}

@inproceedings{Dudek1995c,
 abstract = {This paper deals with coordinating behaviour in a multi-autonomous robot system. When two or more autonomous robots must interact in order to accomplish some common goal, communication between the robots is essential. Different inter-robot communications strategies give rise to different overall system performance and reliability. After a brief consideration of some theoretical approaches to multiple-robot collections, we present concrete implementations of different strategies for convoy-like behaviour. The convoy system is based around two RWI B12 mobile robots and uses only passive visual sensing for inter-robot communication. The issues related to different communication strategies are considered. },
 address = {Pittsburgh, PA},
 author = {Gregory Dudek and Michael Jenkin and Evangelos Milios and David Wilkes},
 booktitle = {Proceedings IEEE International Conference on Intelligent Robots and Systems (IROS)},
 month = {August},
 pages = {268-273},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros95.pdf},
 refnumber = {3 conference},
 title = {Experiments in sensing and communication for robot convoy navigation},
 volume = {2},
 year = {1995}
}

@inproceedings{Langer1995,
 abstract = {We address the problem of estimating 3-d space occupancy using video imagery in the context of mobile robotics. A stationary robot observes a cluttered scene from a single viewpoint, and a second robot illuminates the scene from a sequence of directions thus producing a sequence of grey-level images. Differences of successive images are used to compute a sequence of shadowimages. The problem is to compute free space and occupied space from these shadowimages. Solutions to this problem are known for the special case of terrain scenes. We generalize these solutions to non-terrain scenes by making two key observations. First, there is a subset constraint on the shadowimages of a non-terrain scene, which allows the visible surfaces of a non-terrain scene to be recovered by a terrain-based technique. Second, the remaining regions of the shadowimages provide a conservative estimate of the occupied space hidden by these visible surfaces. },
 address = {Pittsburgh, PA},
 author = {Michael Langer and Gregory Dudek and Steven W. Zucker},
 booktitle = {Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
 month = {August},
 pages = {390-396},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/iros95_1.pdf},
 publisher = {IEEE Press},
 refnumber = {3 conference paper},
 title = {Space occupancy using multiple shadowimages},
 year = {1995}
}

@inproceedings{Pateras1995,
 address = {Detroit, MI},
 author = {Claudia Pateras and Gregory Dudek and Renato DeMori},
 booktitle = {Proc. of the IEEE Conference of Acoustics, Speech and Signal Processing},
 month = {April},
 pages = {197-200},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icassp95.pdf},
 publisher = {IEEE Press},
 refnumber = {3 conference paper},
 title = {Understanding Referring Expressions in a Person-Machine Spoken Dialogue},
 year = {1995}
}

@mastersthesis{rekleitis1995thesis,
 author = {I. Rekleitis},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/msc_thesis.pdf},
 school = {McGill University},
 title = {Visual Motion Estimation based on Motion Blur Interpretation},
 year = {1995}
}

@mastersthesis{torres1995thesis,
 abstract = {A method for object recognition invariant under translation, rotation and scaling is addressed. The first step of the method (preprocessing) takes into account the invariant properties of the normalized moment of inertia and a novel coding that extracts topological object characteristics. The second step (recognition) is achieved by using a Holographic Nearest Neighbor algorithm (HNN), where vectors obtained in the preprocessing step are used as inputs to it. The algorithm is tested in character recognition, using the 26 upper case letters of the alphabet. Only four different orientations and one size (for each letter) were used for training. Recognition was tested with 17 different sizes and 14 rotations. The results are encouraging since we achieved 98% correct recognition. Tolerance to manual and random noise was tested. Results for character recognition in real images of car plates are also presented. },
 author = {L. A. Torres M.},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/thesis_7.pdf},
 school = {Instituto Tecnologico y de Estudios Superiores de Monterrey, Campus-Morelos},
 title = {Translation, Rotation and Scale Invariant 2-D Object Recognition},
 year = {1995}
}

@article{584097,
 abstract = {Passively accepting measurements of the world is not enough, as the data we obtain is always incomplete, and the inferences made from it uncertain to a degree which is often unacceptable. If we are to build machines that operate autonomously they will always be faced with this dilemma, and can only be successful if they play a much more active role. This paper presents such a machine. It deliberately seeks out those parts of the world which maximize the fidelity of its internal representations, and keeps searching until those representations are acceptable. We call this paradigm autonomous exploration, and the machine an autonomous explorer.

This paper has two major contributions. The first is a theory that tells us how to explore, and which confirms the intuitive ideas we have put forward previously. The second is an implementation of that theory. In our laboratory we have constructed a working autonomous explorer and here for the first time show it in action. The system is entirely bottom-up and does not depend on any a priori knowledge of the environment. To our knowledge it is the first to have successfully closed the loop between gaze planning and the inference of complex 3D models. },
 author = {P. {Whaite} and F. P. {Ferrie}},
 journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
 number = {3},
 pages = {193-205},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/whaite-auton-exploration.pdf},
 title = {Autonomous exploration: driven by uncertainty},
 volume = {19},
 year = {1997}
}

@article{ferrie1995surface,
 abstract = {A key problem in the recovery of scene descriptions from multiple views is the fusion of information from different vantage points. The contribution of this paper is a set of algorithms for reconstructing surfaces obtained from overlapping range images in a common frame of reference. Surfaces are assumed to be piecewise-smooth but not necessarily rigid. Motion parameters, rotations and translations that describe correspondence between views, are recovered locally under the assumption that the curvature structure at a point on a surface varies slowly under transformation. The recovery problem can thus be posed as finding the set of motion parameters that preserves curvature across two views. We show that an appropriate similarity functional can be devised that is convex in the vicinity of the true motion parameters. This leads to an efficient local algorithm that recovers motion parameters in gradient descent fashion.

Fusion of information from different viewpoints is accomplished by applying local motion estimates to map data points between frames. However, because these estimates are determined locally, they are subject to the usual effects of noise and quantization error. To increase the robustness of this reconstruction procedure the additional constraint of motion consistency is introduced, that variations in the velocities of adjacent regions are also piecewise-smooth. This is cast as a second local minimization problem which seeks to find the set of motion parameters that minimizes differences in the relative positions and orientations at adjacent points. The resulting algorithm serves to smooth out local perturbations and blend adjacent surface patches. In contrast to global rigid body motion approaches, our procedure for reconstructing surfaces from different viewpoints is tolerant of local errors in correspondence and can accommodate objects that are articulated. },
 author = {Ferrie, G Soucy FP},
 note = {McGill University TR-CIM-93-17},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/soucy-motion-recovery.pdf},
 title = {Surface Recovery Using Curvature and Motion Consistency},
 year = {1995}
}

@article{608292,
 abstract = { Many strategies in computer vision assume the existence of general purpose models that can be used to characterize a scene or environment at various levels of abstraction. The usual assumptions are that a selected model is competent to describe a particular attribute, and that the parameters of this model can be estimated by interpreting the input data in an appropriate manner (e.g. location of lines and edges, segmentation into parts or regions, etc.). This paper considers the problem of determining when these assumptions break down so that an alternate model may be considered or further interpretation of data performed. Specifically, we consider how this can be accomplished with a minimum of a-priori knowledge using an approach that actively builds a description of the environment (i.e. structure and noise) over several different viewpoints. We show that a gaze planning strategy used to minimize model parameter variance can also be used to decide whether the model itself provides an adequate description of the environment. },
 author = {P. {Whaite} and F. P. {Ferrie}},
 journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
 number = {8},
 pages = {899-905},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/whaite-sequential-determ.pdf},
 title = {On the sequential determination of model misfit},
 volume = {19},
 year = {1997}
}

@inproceedings{dudek1995sirs,
 address = {Pisa, Italy},
 author = {Dudek, Gregory and Michael Jenkin and Evangelos Milios and David Wilkes},
 booktitle = {Proc. 3rd Int. Symposium on Intelligent Robotic Systems (SIRS)},
 pages = {49-56},
 title = {Multi-robot landmark-based self-location and exploration},
 year = {1995}
}

@inproceedings{dudek1995imps,
 address = {Mayaguez, PR},
 author = {Dudek, Gregory},
 booktitle = {Proc. of the CIRP/IEEE/NSF First World Congress on Intelligent Manufacturing Processes and Systems},
 title = {Mobile Robotics for Environmental Modelling},
 year = {1995}
}

@inproceedings{dudek1995graphlikeworld,
 address = {Quebec City, Quebec},
 author = {Dudek, Gregory and Michael Jenkin and Evangelos Milios and David Wilkes},
 booktitle = {Proc. Vision Interface},
 pages = {195-202},
 title = {Exploring Graph-Like World Embedded in a Metric Map},
 year = {1995}
}

@inproceedings{Alami1994,
 abstract = {We introduce an approach to the representation of curved or polyhedral 3-D objects and apply this representation to pose estimation. The representation is based on surface patches with uniform curvature properties extracted at multiple scales. These patches are computed using multiple alternative decompositions of the surface based on the signs of the mean and Gaussian curvatures. Initial coarse decompositions are subsequently refined using a curvature compatibility scheme to rectify the effect of noise and quantization errors. The extraction of simple uniform curvature features is limited by the fact that the optimal scale of processing for a single object is very difficult to determine. As a solution we propose the segmentation of range data into patches at multiple scales. A hierarchical ranking of these patches is then used to describe individual objects based on geometric information. These geometric descriptors are ranked according to several criteria expressing their estimated stability and utility. The applicability of the resulting multi-scale description is demonstrated by estimating the pose of a 3-D object. Pose estimation is cast as an optimal matching problem. The geometric pose transformation is computed by matching two representations, which amounts to finding the three-patch correspondence that produces the best global consistency. Examples of the multi-scale representation applied to both real and simulated range data are presented. Effective pose estimation is demonstrated and the algorithms behaviour in the presence of noise is validated. },
 address = {Boston, MA},
 author = {Wassim Alami and Gregory Dudek},
 booktitle = {Proceedings of SPIE - The International Society for Optical Engineering.},
 pages = {108-119},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/spie94.pdf},
 title = {Multiscale object representation using surface patches},
 volume = {2353},
 year = {1994}
}

@techreport{arbel1994informative,
 abstract = {In this paper we introduce a method for distinguishing between informative and uninformative viewpoints as they pertain to an active observer seeking to identify an object in a known environment. The method is based on a generalized inverse theory using a probabilistic framework where assertions are represented by conditional probability density functions. Consequently, the method also permits the assessment of the beliefs associated with a set of assertions based on data acquired from a particular viewpoint. The importance of this result is that it provides a basis by which an external agent can assess the quality of the information from a particular viewpoint, and make informed decisions as to what action to take using the data at hand. What is important about the method is that it provides a formal recipe for representing and combining all prior knowledge in order to obtain the required density functions (which we refer to as belief distributions).

To illustrate the theory we show how the characteristics of belief distributions can be exploited in a model-based recognition problem, where the task is to identify an unknown model from a database of known objects on the basis of parameter estimates. This leads to a sequential recognition strategy in which evidence is accumulated over successive viewpoints (at the level of the belief distribution) until a definitive assertion can be made. Experimental results are presented showing how the resulting algorithms can be used to distinguish between informative and uninformative viewpoints, rank a sequence of images on the basis of their information (e.g. to generate a set of characteristic views), and sequentially identify an unknown object. },
 author = {Arbel, Tal and Ferrie, Frank P and Whaite, Peter},
 institution = {McGill University},
 number = {TR-CIM-94-16},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/arbel-informative-views.pdf},
 title = {Informative Views and Active Recognition},
 year = {1994}
}

@inproceedings{576328,
 abstract = {This paper describes a new framework for parametric shape recognition based on a probabilistic model of inverse theory first introduced by Tarantola. The key result is a method for generating classifiers in the form of conditional probability densities for recognizing an unknown from a set of reference models. Our procedure is automatic. Off-line, it invokes an autonomous process to estimate reference model parameters and their statistics. On-line, during measurement, it combines these with apriori context-dependent information, as well as the parameters and statistics estimated for an unknown object, into a single description. That description, a conditional probability density function, represents the likelihood of correspondence between the unknown and a particular reference model. The paper also describes the implementation of this procedure in a system for automatically generating and recognizing 3-D part-oriented models. Specifically we show that recognition performance is near perfect for cases in which complete surface information is accessible to the algorithm, and that it falls off gracefully (minimal false-positive response) when only partial information is available. This leads to the possibility of an {\em active} recognition strategy in which the belief measures associated with each classification can be used as feedback for the acquisition of further evidence as required. },
 author = {T. {Arbel} and P. {Whaite} and F. P. {Ferrie}},
 booktitle = {Proceedings of 12th International Conference on Pattern Recognition},
 number = {},
 pages = {470-476 vol.1},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/arbel-uncertain-parts.pdf},
 title = {Recognizing volumetric objects in the presence of uncertainty},
 volume = {1},
 year = {1994}
}

@inproceedings{351359,
 abstract = { This paper addresses the coupled tasks of constructing a spatial representation of the environment with a mobile robot using noisy sensors (sonar) and using such a map to determine the robot's position without a prior estimate. The map is not meant to represent the actual spatial structure of the environment so much as it is meant to represent the major structural components of what the robot ``sees''. This can, in turn, be used to construct a model of the physical objects in the environment. One problem with such an approach is that maintaining an absolute coordinate system for the map is difficult without periodically calibrating the robot's position.

 We demonstrate that in a suitable environment it is possible to use sonar data to recalibrate position and orientation estimates on an ongoing basis. This is accomplished by incrementally constructing and updating a model-based description of the acquired data. Given coarse position estimates of the robot's location and orientation, these can be refined to high accuracy using the stored map and a set of sonar readings from a single position. This approach is then generalized to allow global position estimation, where position and orientation estimates are not available.

 We consider the accuracy of the method based on a single sonar reading illustrate its region of convergence using empirical data. },
 author = {P. {Mackenzie} and G. {Dudek}},
 booktitle = {Proceedings of the 1994 IEEE International Conference on Robotics and Automation},
 number = {},
 pages = {1615-1621 vol.2},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/icra94.pdf},
 title = {Precise positioning using model-based maps},
 volume = {},
 year = {1994}
}

@techreport{dudek1994techreport,
 author = {Dudek, Gregory and Paul Freedman and Souad Hadjres},
 institution = {Centre for Intelligent Machines, McGill University},
 number = {CIM-94-34},
 title = {Signatures and extended signatures for topological mapping using graphs},
 year = {1994}
}

@inbook{dudek1994rcrv,
 abstract = { Abstract We consider the problem of locating a robot in an initially-unfamiliar environment from visual input. The robot is not given a map of the environment, but it does have access to a limited set of training examples each of which specifies the video image observed when the robot is at a particular location and orientation. Such data might be acquired using dead reckoning the first time the robot entered an unfamiliar region (using some simple mechanism such as sonar to avoid collisions). In this paper, we address a specific variant of this problem for experimental and expository purposes: how to estimate a robot's orientation(pan and tilt) from sensor data. Performing the requisite scene reconstruction needed to construct a metric map of the environment using only video images is difficult. We avoid this by using an approach in which the robot learns to convert a set of image measurements into a representation of its pose (position and orientation). This provides a local metric description of the robot's relationship to a portion of a larger environment. A large-scale map might then be constructed from a collection of such local maps. In the case of our experiment, these maps express the statistical relationship between the image measurements and camera pose. The conversion from visual data to camera pose is implemented using multi-layer neural network that is trained using backpropagation. For extended environments, a separate network can be trained for each local region. The experimental data reported in this paper for orientation information (pan and tilt) suggests the accuracy of the technique is good while the on-line computational cost is very low. },
 author = { Gregory   Dudek  and  Chi   Zhang },
 booktitle = {Research in Computer and Robot Vision},
 doi = {10.1142/9789812812483_0002},
 eprint = {https://www.worldscientific.com/doi/pdf/10.1142/9789812812483_0002},
 pages = {19-35},
 title = {Pose Estimation From Image Data Without Explicit Object Models},
 url = {https://www.worldscientific.com/doi/abs/10.1142/9789812812483_0002},
 year = {1994}
}

@inproceedings{dudek1994graphworld,
 address = {Montreal, Canada},
 author = {Dudek, Gregory and Paul Freedman},
 booktitle = {Proceedings of the International Advanced Robotics Programme Workshop on Robotics in Space},
 pages = {1-20},
 title = {Mapping Unknown Graph-Like Worlds},
 year = {1994}
}

@inproceedings{dudek1994human,
 address = {Capri, Italy},
 author = {Dudek, Gregory and Arguin, Martin and Bub, Daniel},
 booktitle = {Proceedings of the International Workshop on Visual Form},
 pages = {130-138},
 title = {Human integration of shape primitives},
 year = {1994}
}

@inproceedings{jenkin1994horoptor,
 author = {Jenkin, M. and J. K. Tsotsos and G. Dudek},
 booktitle = {Proceedings of the International Conference on Pattern Recognition},
 pages = {707-710},
 title = {Horoptor and active cyclotorsion},
 volue = {1},
 year = {1994}
}

@inproceedings{arguin1994decomposition,
 address = {Sarasota, FL, USA},
 author = {Arguin, Martin and Daniel Bub and Gregory Dudek},
 booktitle = {Investigative Ophthalmology and Visual Science (The Association for Research in Vision and Opthalmology)},
 title = {Dimensional Decomposition of Human Shape Recognition},
 year = {1994}
}

@inproceedings{Dudek1993b,
 address = {Chambery, France},
 author = {Gregory Dudek and Michael Jenkin and Evangelos Milios and David Wilkes},
 booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence},
 month = {August},
 pages = {1648-1653},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/ijcai93b.pdf},
 publisher = {Internation Joint Conf. on Artificial Intelligence Inc.},
 refnumber = {3 conference paper},
 title = {Map Validation and Self-Location in a Graph-Like World},
 year = {1993}
}

@inproceedings{dudek1993ias,
 abstract = {We present a method for partitioning a set of surface estimates obtained with a laser range finding system into subsets corresponding to parts of an object. Our strategy uses two complementary representations for surfaces: one that describes local structures in terms of differential properties (e.g. edges, lines, contours) and the other that represents the surface as a collection of smooth patches at different scales. By enforcing a consistent interpretation between these two representations, it is possible to derive a partitioning algorithm that is both efficient and robust. },
 author = {G. Dudek and M. Jenkin},
 booktitle = {International Conference on Intelligent Autonomous Systems (IAS)},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/lejeune-finding-parts.pdf},
 title = {Using local information in a non-local way for mapping graph-like worlds},
 year = {1993}
}

@article{LEJEUNE1996230,
 abstract = {A key problem in the interpretation of visual form is the partitioning of a shape into components that correspond to the parts of an object. This paper presents a method for partitioning a set of surface estimates obtained with a laser range finding system into subsets corresponding to such parts. Parts are defined implicitly by means of a feature set that identifies putative part boundaries that have been computed by external means. The strategy employed makes use of two complementary representations for surfaces: one that describes local structures in terms of differential properties (e.g., edges, lines, contours) and the other that represents the surface as a collection of smooth patches at different scales. It is shown that by enforcing a consistent interpretation between these two representations, it is possible to derive a partitioning algorithm that is both efficient and robust. Examples of its performance on a set of range images are presented.},
 author = {André Lejeune and Frank P. Ferrie},
 doi = {https://doi.org/10.1006/cviu.1996.0056},
 issn = {1077-3142},
 journal = {Computer Vision and Image Understanding},
 number = {2},
 pages = {230 - 247},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/lejeune-finding-parts_1.pdf},
 title = {Finding the Parts of Objects in Range Images},
 url = {http://www.sciencedirect.com/science/article/pii/S1077314296900569},
 volume = {64},
 year = {1996}
}

@inproceedings{341183,
 abstract = {We present a method for partitioning a set of surface estimates obtained with a laser range finding system into subsets corresponding to parts of an object. Our strategy uses two complementary representations for surfaces: one that describes local structures in terms of differential properties (e.g. edges, lines, contours) and the other that represents the surface as a collection of smooth patches at different scales. By enforcing a consistent interpretation between these two representations, it is possible to derive a partitioning algorithm that is both efficient and robust. },
 author = {A. {Lejeune} and F. P. {Ferrie}},
 booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition},
 number = {},
 pages = {800-801},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/lejeune-partitioning.pdf},
 title = {Partitioning range images using curvature and scale},
 volume = {},
 year = {1993}
}

@inproceedings{arguin1993aoa,
 address = {Tucson, AZ},
 author = {Arguin, Martin and Daniel Bub and Gregory Dudek},
 booktitle = {Proceedings of the Academy of Aphasia},
 title = {What does aphasia tell us about object recognition},
 year = {1993}
}

@inproceedings{dudek1993ijcai,
 address = {Chambery, France},
 author = {Dudek, Gregory and Michael Jenkin and Evangelos Milios and David Wilkes},
 booktitle = {Proceedings of the International Joint Conference of Artificial Intelligence Workshop on Dynamically Interacting Robots (IJCAI)},
 pages = {118-123},
 title = {Robust Positioning with a Multi-Agent Robotic System},
 year = {1993}
}

@inproceedings{dudek1993visioninterface,
 address = {Toronto, Ontario},
 author = {Dudek, Gregory and Michael Jenkin and Evangelos Milios and David Wilkes},
 booktitle = {Proceedings of Vision Interface},
 pages = {91-96},
 title = {Organizational Characteristics for Multi-Agent Robotic Systems},
 year = {1993}
}

@inproceedings{dudek1993ijcai-utility,
 address = {Chambery, France},
 author = {Dudek, Gregory and Michael Jenkin and Evangelos Milios and David Wilkes},
 booktitle = {Proceedings of the International Joint Conference of Artificial Intelligence Workshop on Dynamically Interacting Robots (IJCAI)},
 pages = {101-108},
 title = {On the Utility of Multi-Agent Autonomous Robot Systems},
 year = {1993}
}

@inproceedings{292176,
 author = {D. {Wilkes} and G. {Dudek} and M. {Jenkin} and E. {Milios}},
 booktitle = {[1993] Proceedings IEEE International Conference on Robotics and Automation},
 number = {},
 pages = {392-397 vol.2},
 title = {Multi-transducer sonar interpretation},
 volume = {},
 year = {1993}
}

@inproceedings{dudek1993visioninterface,
 address = {Toronto, Ontario},
 author = {Dudek, Gregory and Paul MacKenzie},
 booktitle = {Proceedings of Vision Interface},
 pages = {97-102},
 title = {Model Based Map Construction for Mobile Robot Localization},
 year = {1993}
}

@inproceedings{dudek1993using,
 author = {Dudek, Gregory and Freedman, Paul and Hadjres, Souad},
 booktitle = {IJCAI},
 pages = {1639--1647},
 title = {Using local information in a non-local way for mapping graph-like worlds},
 year = {1993}
}

@inproceedings{583135,
 author = {G. {Dudek} and M. {Jenkin} and E. {Milios} and D. {Wilkes}},
 booktitle = {Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93)},
 number = {},
 pages = {441-447 vol.1},
 title = {A taxonomy for swarm robots},
 volume = {1},
 year = {1993}
}

@inproceedings{dudek1993ias,
 address = {Pittsburgh, PA},
 author = {Dudek, Gregory and Michael Jenkin},
 booktitle = {Proceedings of the International Conference on Intelligent Autonomous Systems (IAS)},
 pages = {542-550},
 title = {A Multi-Layer Distributed Environment for Mobile Robots},
 year = {1993}
}

@inbook{dudekShapeTexture1992,
 abstract = {This paper deals with a method for the classification of objects described by planar curves in an image. It is suggested that an important aspect of shape classification methods is the nature and degree of the invariance to deformation of the shape. One extreme of this spectrum is the statistical description of shape. Such a method is presented here. Using scale space to describe shape statistically allows for an unusual form of object classification. The scale space used is one based on curvature measurements extracted by a modified ``regularization'' method. It allows a curve to be represented as a set of descriptors at various scales. The spatial correlation of these descriptors is used to produce a statistical description of a contour which has similarities to a large-scale texture measure. The ``texture'' being measured is, in fact, the combination of substructures that define the object's shape. },
 author = {Gregory Dudek},
 booktitle = {Shape in Picture: Mathematical Description of Shape in Grey-level Images},
 editor = {Ying-Lie O, Alexander Toet, David Foster, Henk J.A.M. Heijmans, Peter Meer},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/shape-texture.pdf},
 publisher = {Springer},
 title = {Shape Description and Classification using the Interrelationship of Structures at Multiple Scales},
 year = {1992}
}

@techreport{Wilkes1992b,
 author = {David Wilkes and Gregory Dudek and Michael Jenkin and Evangelos Milios},
 institution = {McGill Centre for Intelligent Machines},
 number = {McGill CIM TR-92-9},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/cimtr92.pdf},
 publisher = {McGill Centre for Intelligent Machines},
 refnumber = {3 report},
 title = {Reflections on Sonar Range Sensing},
 year = {1992}
}

@inproceedings{dudek1992aprv,
 abstract = {This paper describes a new symbolic representation for planar curves. This representation is based on a segmentation of the curve based on regions of uniform curvature. Rather than smooth noisy data before doing the decomposition, the technique defines a family of functions that extract the segments of the curve as part of the smoothing process. The representation decomposes the curve at multiple scales and the parts produced appear to correspond to a natural decomposition of the curve. It also allows for multiple descriptions of some parts of the curve. The final representation can be rendered compact, avoids several common disadvantages in noisy curve description, and should be useful for recognition.},
 address = {Berlin, Heidelberg},
 author = {Dudek, Gregory and Tsotsos, John},
 booktitle = {Active Perception and Robot Vision},
 editor = {Sood, Arun K. and Wechsler, Harry},
 isbn = {978-3-642-77225-2},
 pages = {527--537},
 publisher = {Springer Berlin Heidelberg},
 title = {Using Curvature Information in the Decomposition and Representation of Planar Curves},
 year = {1992}
}

@inproceedings{dudek1992shapeinpicture,
 address = {Driebergen, Holland},
 author = {Dudek, Gregory},
 booktitle = {Proceedings of the Workshop on Shape in Picture},
 title = {Shape Description and Classification using Scale-Space Measurement},
 year = {1992}
}

@inproceedings{dudek1992visualscience,
 address = {Sarasota, FL},
 author = {Dudek, Gregory},
 booktitle = {Investigative Ophthalmology and Visual Science (The Association for Research in Vision and Opthalmology)},
 title = {Shape Classification and Scale-Space Texture},
 year = {1992}
}

@inproceedings{dudek1992sbmr,
 address = {Nice, France},
 author = {Dudek, Gregory},
 booktitle = {Proceedings of the Workshop on Sensor-Based Mobile Robotics},
 title = {Robot Map-Making Using Weak Sensory Feedback},
 year = {1992}
}

@inproceedings{wilkes1992amv,
 address = {Petriu, Singapore},
 author = {Wilkes, David and Gregory Dudek and Michael Jenkin and Evangelos Milios},
 booktitle = {Advances in Machine Vision: Strategies and Applications},
 editor = {Archibald and Petriu},
 pages = {361-370},
 title = {Modelling Sonar Range Sensors},
 year = {1992}
}

@inproceedings{dudek1992activeexploration,
 address = {Boston, MA},
 author = {Dudek, Gregory and Souad Hadjres and Paul Freedman},
 booktitle = {Proceedings of the International Society for Optical Engineering Symposium on Advances in Intelligent Robotics Systems (Conference on Mobile Robotics VII)},
 title = {Algorithms for Active Exploration of Unknown Environments: Using Uncertain Sensing Data to Create Reliable Map},
 year = {1992}
}

@mastersthesis{baird1992thesis,
 abstract = { Complex robotic tasks such as autonomous exploration and grasping demand the co-operation of sensors and actuators. In order to integrate sensor measurements and actuator control schemes we must determine the rigid body transformations that relate the native co-ordinate frames of these devices. Equivalently, we need to estimate the relative pose of sensors and actuators in the system.

 We examine the problem of determining the pose of a robot-mounted range-finding camera, and present a class of solutions motivated by the idea that mobile camera calibration is best addressed by an ongoing dynamic estimation process. We use range measurements and known robot kinematics to provide the estimate of camera pose which is maximally consistent with the available data. Our scheme uses scene features that are often present in typical workcell scenes and that are easily and reliably extracted. We develop several formulations of the principle, and present experimental results for both simulated and real data sets. },
 author = {Duncan L. Baird},
 pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/baird-robot-eye-thesis.pdf},
 school = {McGill University},
 title = {Dynamically Estimating Mobile Range Camera Pose From Invariant Feature Measurements},
 year = {1991}
}

@inproceedings{wilkes1991visioninterface,
 address = {Calgary, Alberta},
 author = {Wilkes, David and Gregory Dudek and Michael Jenkin and Evangelos Milios},
 booktitle = {Proceedings of Vision Interface},
 pages = {213-217},
 title = {The Simulation of Sonar Mapping in Complex Environments Using Multiple Reflecting Surfaces},
 year = {1991}
}

@inproceedings{tsotsos1991irisprecarn,
 address = {Vancouver, BC},
 author = {Tsotsos, John K. and Evangelos Milios and Gregory Dudek},
 booktitle = {Proceedings of the First Annual IRIS-Precarn Conference},
 title = {The Integration and Action and Perception in Robotic Systems},
 year = {1991}
}

@inproceedings{dudek1991mra,
 abstract = {The combined problems of sensor error modelling and the exploration of unknown environments are fundamental aspects of autonomous robotics. We consider these problems in the context of mapping an unknown closed environment with a sonar sensor. This leads to three issues at different levels of abstraction: modelling the characteristics of the sensor, dealing with the unavoidable anomalies in the data obtained, and constructing a long-term representation of the environment. An important aspect of this partitioning of the problem is that it takes us from local quantitative descriptions to large-scale symbolic ones. This paper presents algorithms for extracting obstacle contours from sonar data, that accounts for the properties of sonar modelled in our previous work. This work is then related to work on the high-level problem of constructing a map of an unknown environment. Our model of sonar range sensing for robot navigation accounts for multiple reflections of the sonar signal between transmission and reception. This gives more realistic results than previous models. This controlled data-acquisition methodology can be subsequently used to compare techniques for the interpretation of the acquired data.

From this model of sonar sensing itself, a new model for inferring coherent reflecting surfaces is developed, based on an assumption of coherent smooth surfaces in the world. This is accomplished by using an ``energy based minimization'' algorithm to reject sonar measurements that are not consistent with an {\it a priori} world model.

Finally, we briefly discuss a representation for large scale structure of the environment that is based only on the topological interrelation between places of interest and the paths connecting them. },
 address = {Medicine Hat, Alberta},
 author = {Dudek, Gregory and Michael Jenkin and Evangelos Milios and David Wilkes},
 booktitle = {Proceedings of the Third Conference on Military Robotic Applications (Defense Research Establishment Suffield Special Publication 149)},
 pages = {198-205},
 title = {Sonar Sensing and Obstacle Modelling},
 year = {1991}
}

@article{dudek1991gmcm,
 author = {Dudek, Gregory},
 editor = {B. Vemuri},
 journal = {Geometric Methods in Computer Vision},
 title = {Shape Metrics from Curvature-Tuned Smoothing},
 year = {1991}
}

@inproceedings{dudek1991isop,
 abstract = {This research deals with the decomposition and description of curved objects. In ongoing work, a new part description for curves and surfaces using a set of curvature-based minimization operators has been developed. The decomposition operation simultaneously performs data interpolation, data smoothing, and segmentation. The unification of these three stages results in a smoothing operation that is tightly coupled with the primitives to be used in subsequent object description. Each of the minimization operators, in addition to having a curvature tuning, has a different spatial sensitivity function. As a result, different possible descriptions of an object are produced and these capture information at multiple spatial scales. Each object is described by a small number of tokens based on differential geometric properties. The set of descriptors produced for a given object can be organized into an unusual form of nonlinear scale-space. The utility of such a scale-based description by way of two methods for the characterization (i.e., recognition) of two-dimensional objects via their multi- scale signature in terms of curvature-scale-space features is demonstrated. One method is based on graph matching by dynamic programming and the other based on statistical properties of scale space ('shape texture').},
 address = {San Diego, CA},
 author = {Gregory Dudek},
 booktitle = {Geometric Methods in Computer Vision},
 doi = {10.1117/12.49976},
 editor = {Baba C. Vemuri},
 organization = {International Society for Optics and Photonics},
 pages = {75 -- 85},
 publisher = {SPIE},
 title = {{Shape metrics from curvature-scale space and curvature-tuned smoothing}},
 url = {https://doi.org/10.1117/12.49976},
 volume = {1570},
 year = {1991}
}

@inproceedings{dudek1991cvpr,
 address = {Maui, HA},
 author = {Dudek, Gregory and John K, Tsotsos},
 booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
 pages = {35-41},
 title = {Robustly recognizing curves using curvature-tuned smoothing},
 year = {1991}
}

@article{dudek1991tra,
 author = {Dudek, Gregory and Michael Jenkin and Evangelos Milios and David Wilkes},
 journal = {IEEE Transactions on Robotics and Automation},
 number = {6},
 pages = {859-865},
 title = {Robotic Exploration as Graph Construction},
 volume = {7},
 year = {1991}
}

@inproceedings{cohen1991irisprecarn,
 address = {Vancouver, BC},
 author = {Cohen P. and G. Dudek and M. D. Levine and F. Ferrie and S. W. Zucker},
 booktitle = {Proceedings of the First Annual IRIS-Precarn Conference},
 title = {A Framework for Active Vision},
 year = {1991}
}

@inproceedings{koreitem2020one,
  title={One-Shot Informed Robotic Visual Search in the Wild},
  author={Koreitem, Karim and Shkurti, Florian and Manderson, Travis and Chang, Wei-Di and Higuera, Juan Camilo Gamboa and Dudek, Gregory},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2020},
  organization={IEEE},
  url={https://arxiv.org/abs/2003.10010},
  pdf={https://arxiv.org/pdf/2003.10010.pdf}
}


@inproceedings{Manderson2020rss,
    title={Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of Obstacles}, 
    author={Travis Manderson and Juan Camilo Gamboa Higuera and Stefan Wapnick and Jean-François Tremblay and Dave Meger and Gregory Dudek},
    year={2020},  
    booktitle = {Proceedings of Robotics: Science and Systems}, 
    year      = {2020}, 
    month     = {July},
    pdf       = {https://arxiv.org/pdf/2006.16235.pdf},
    projectpage = {http://www.cim.mcgill.ca/~mrl/nav2goal/}
}

@INPROCEEDINGS{9108679,
  author={Andrew Holliday and Gregory Dudek},
  booktitle={Proceedings of the Conference on Computer and Robot Vision (CRV)},
  title={Pre-trained CNNs as Visual Feature Extractors: A Broad Evaluation},
  year={2020},
  volume={},
  number={},
  abstract={In this work, we perform a wide-ranging evaluation of \acp{CNN} as feature extractors for matching visual features under large changes in appearance, perspective, and visual scale. Our evaluation covers 82 different layers from twelve different \ac{CNN} architectures belonging to four families: AlexNets, VGG Nets, ResNets, and DenseNets.  To our knowledge, this is the most comprehensive analysis of its kind in the literature.  We find that the intermediate layers of DenseNets serve as the best feature extractors overall, providing the best overall trade-off of robustness to feature size.  Moreover, we find that for each network, the later-intermediate layers provide the best performance, regardless of the total number of layers in the network.},
  pdf={https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/aholliday_crv_2020.pdf},
  media={https://www.cim.mcgill.ca/~mrl/content/publications/other/aholliday_crv_2020.mp4},
  pages={78-84},}

@article{holliday2017speedup,
  title={Speedup of deep learning ensembles for semantic segmentation using a model compression technique},
  author={Holliday, Andrew and Barekatain, Mohammadamin and Laurmaa, Johannes and Kandaswamy, Chetak and Prendinger, Helmut},
  journal={Computer Vision and Image Understanding},
  volume={164},
  pages={16--26},
  year={2017},
  publisher={Elsevier}
}

@inproceedings{gamboa_nn_deep_pilco_17,
    address = {Long Beach, California, United States},
    author = {Gamboa Higuera, Juan Camilo and Meger, David and Dudek, Gregory},
    booktitle = {2nd Bayesian Deep Learning Workshop at NIPS 2017 (BDL 2017)},
    title = {{Synthesizing Neural Network Controllers with Probabilistic Model-Based Reinforcement Learning}},
    pdf = {http://www.cim.mcgill.ca/~mrl/content/publications/pdfs/bdl_2017_synthesizing_NN_policies_with_deep_pilco_final.pdf},
    year = {2017}
}

@inproceedings{thakur2019uncertainty,
  title={Uncertainty aware learning from demonstrations in multiple contexts using bayesian neural networks},
  author={Thakur, Sanjay and van Hoof, Herke and Higuera, Juan Camilo Gamboa and Precup, Doina and Meger, David},
  booktitle={2019 International Conference on Robotics and Automation (ICRA)},
  pages={768--774},
  year={2019},
  organization={IEEE},
  url={https://arxiv.org/abs/1903.05697},
  pdf={https://arxiv.org/pdf/1903.05697.pdf}
}

@inproceedings{mozifian2019learning,
  title={Learning Domain Randomization Distributions for Training Robust Locomotion Policies},
  author={Mozifian, Melissa and Higuera, Juan Camilo Gamboa and Meger, David and Dudek, Gregory},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2020},
  organization={IEEE},
  url = {https://arxiv.org/abs/1906.00410},
  pdf = {https://arxiv.org/pdf/1906.00410.pdf}
}


@mastersthesis{juan_masters,
    author = {Gamboa Higuera, Juan Camilo},
    title = {Distributing work among heterogeneous robots, An approach based on fair division theory},
    institution = {School of Computer Science, Centre for Intelligent Machines, {McGill} University, Montreal, Canada},
    note = {MSc. Thesis},
    year = {2012},
    pdf = {https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/juan_msc_thesis.pdf}
}

@article{Abeysirigoonawardena2019GeneratingAD,
  title={Generating Adversarial Driving Scenarios in High-Fidelity Simulators},
  author={Yasasa Abeysirigoonawardena and Florian Shkurti and Gregory Dudek},
  journal={International Conference on Robotics and Automation (ICRA)},
  year={2019},
  pages={8271-8277},
  abstract={In recent years self-driving vehicles have become more common place on public roads, with the promise of bringing safety and efficiency to modern transportation systems. Increasing the reliability of these vehicles on the road requires an extensive suite of software tests, ideally performed on high-fidelity  simulators, where multiple vehicles and pedestrians interact with the self-driving  vehicle. It is therefore of critical importance to ensure that self-driving software is assessed against a wide range of challenging simulated driving scenarios. The state of the art in driving scenario generation, as adopted by some of the front-runners  of the self-driving car industry, still relies on human input [1]. In this paper we  propose to automate the process using Bayesian Optimization to generate adversarial self-driving scenarios that expose poorly-engineered or poorly-trained self-driving policies, and increase the risk of collision with simulated pedestrians and vehicles. We show that by incorporating the generated scenarios into the training set of the self-driving policy, and by fine-tuning the policy using vision-based imitation learning we obtain safer self-driving behavior.},
  pdf={https://www.cim.mcgill.ca/~mrl/content/publications/pdfs/yasasa2019icra.pdf},
  media={https://www.cim.mcgill.ca/~mrl/content/publications/other/yasasa2019icra.mp4}
}