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Gregory Dudek

Associate Academic Member
Full Professor and Research Director of Mobile Robotics Lab, McGill University, School of Computer Science
Vice President and Lab Head of AI Research, Samsung AI Center in Montréal
Google Scholar

Biography

Gregory Dudek is a full professor at McGill University’s CIM which is linked to the School of Computer Science and Research Director of Mobile Robotics Lab. He is also the Lab Director and VP of research at Samsung AI Center Montreal and an Associate academic member at Mila - Quebec Institute of Artificial Intelligence.

Dudek has authored and co-authored over 300 research publications on a wide range of subjects, including visual object description, recognition, RF localization, robotic navigation and mapping, distributed system design, 5G telecommunications and biological perception.

He co-authored the book “Computational Principles of Mobile Robotics” (Cambridge University Press) with Michael Jenkin. He has chaired and been involved in numerous national and international conferences and professional activities concerned with robotics, machine sensing and computer vision.

Dudek’s research interests include perception for mobile robotics, navigation and position estimation, environment and shape modelling, computational vision and collaborative filtering.

Current Students

Farnoosh Faraji
PhD - McGill University
Principal supervisor :
David Meger
Github
Google Scholar
Steve Wen
Master's Research - McGill University
Principal supervisor :
Doina Precup

Publications

Planning in Dynamic Environments with Conditional Autoregressive Models
Johanna Hansen
Kyle Kastner
Aaron Courville
Gregory Dudek
We demonstrate the use of conditional autoregressive generative models (van den Oord et al., 2016a) over a discrete latent space (van den Oo… (see more)rd et al., 2017b) for forward planning with MCTS. In order to test this method, we introduce a new environment featuring varying difficulty levels, along with moving goals and obstacles. The combination of high-quality frame generation and classical planning approaches nearly matches true environment performance for our task, demonstrating the usefulness of this method for model-based planning in dynamic environments.
2018-11-25
ArXiv (preprint)
arxiv.org
arxiv.org