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Glen Berseth

Appointment

Canada CIFAR AI Chair

Pan-Canadian AI Strategy

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About

Appointed Canada CIFAR AI Chair – 2021

Glen Berseth is an assistant professor at the Université de Montréal, a core academic member of the Mila – Quebec AI Institute, Canada CIFAR AI chair, and co-director of the Robotics and Embodied AI Lab (REAL). His current research focuses on machine learning and solving real-world sequential decision-making problems (planning/RL), such as robotics, scientific discovery and adaptive clean technology. The specifics of his research have covered the areas of human-robot collaboration, generalization, reinforcement learning, continual learning, meta-learning, multi-agent learning, and hierarchical learning. Berseth has published across the top venues in robotics, machine learning, and computer animation in his work. He also teaches courses on data science and robot learning at Université de Montréal and Mila, covering the most recent research on machine learning techniques for creating generalist agents. He has also created a new conference for reinforcement learning research.

Awards

  • International Conference on Learning Representations Notable Reviewer, 2023
  • IROS Best RoboCup Paper Award Finalist, 2022
  • IEEE Computer Graphics and Applications Best Paper Award - Runner Up, 2021
  • Oral presentation (top 1.8% of submissions), International Conference on Learning Representations, 2021
  • Postdoctoral Scholarships (declined), IVADO Program, 2019
  • Postgraduate Scholarships, NSERC PGSD, 2016
  • Best Short Paper, Computer Animation and Social Agents, 2015
  • 1st place for prototype Mars rover design and execution, Mars Society's University Rover Challenge, 2012

Relevant Publications

  • Trabucco, B., Phielipp, M., Berseth G. AnyMorph: Learning Transferable Polices By Inferring Agent Morphology. In International Conference on Machine Learning, 2022.
  • Berseth G, Geng D, Devin CM, Rhinehart N, Finn C, Jayaraman D, Levine S. SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments. In International Conference on Learning Representations 2021.
  • Raj Ghugare, Matthieu Geist, Benjamin Eysenbach, and Glen Berseth . Closing the gap between TD learning and supervised learning - a generalisation point of view. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=qg5JENs0N4
  • Donghyeon Kim, Glen Berseth , Mathew Schwartz, and Jaeheung Park. Torque-based deep reinforcement learning for task-and-robot agnostic learning on bipedal robots using sim-to-real transfer. IEEE Robotics and Automation Letters, pages 1–8, 2023. ISSN 2377-3766. doi: 10.1109/LRA.2023.3304561
  • Glen Berseth, Florian Golemo, and Christopher Pal. Towards learning to imitate from a single video demonstration. J. Mach. Learn. Res., 24(78):1–26, 2023. URL https://www.jmlr.org/papers/v24/ 21-1174.html
  • Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella, John Dolan, Jeff Schneider, and Glen Berseth . Reasoning with latent diffusion in offline reinforcement learning. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=tGQirjzddO

Institution

Mila

Université de Montréal

Department

Computer Science and Operations Research (DIRO)

Education

  • PhD (Computer Science), University of British Columbia
  • MSc (Computer Science), York University
  • BSc with Honours (Computer Science), York University

Country

Canada

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