Glen Berseth

I am an assistant professor at the University de Montreal and Mila. My research explores how to use deep learning and reinforcement learning to develop generalist robots.


I am an assistant professor at the Université de Montréal, a core academic member of the Mila - Quebec AI Institute, CIFAR AI chair, and co-director of the Robotics and Embodied AI Lab (REAL). I was a Postdoctoral Researcher with Berkeley Artificial Intelligence Research (BAIR), working with Sergey Levine. His previous and current research has focused on solving sequential decision-making problems for real-world autonomous learning systems (robots). The specific of his research has covered the areas of reinforcement-, continual-, meta-, hierarchical learning, and human-robot collaboration. In his work, Dr. Berseth has published at top venues across the disciplines of robotics, machine learning, and computer animation. Currently, he is teaching a course on robot learning at Université de Montréal and Mila that covers the most recent research on machine learning techniques for creating generalist robots.

To see a more formal biography, click here.

Interested in joining the lab?

Are you interested in the practical and theoretical challenges of creating generalist problem-solving robots? Please see this page to apply. I may not respond to emails.

News

Jędrzej Orbik Articles


Representative Publications

  • Autonomous real-world Reinforcement learning

    We study how robots can autonomously learn skills that require a combination of navigation and grasping. While reinforcement learning in principle provides for automated robotic skill learning, in practice reinforcement learning in the real world is challenging and often requires extensive instrumentation and supervision. Our aim is to devise a robotic reinforcement learning system for learning navigation and manipulation together, in an autonomous way without human intervention, enabling continual learning under realistic assumptions. Our proposed system, ReLMM, can learn continuously on a real-world platform without any environment instrumentation, without human intervention, and without access to privileged information, such as maps, objects positions, or a global view of the environment. Our method employs a modularized policy with components for manipulation and navigation, where manipulation policy uncertainty drives exploration for the navigation controller, and the manipulation module provides rewards for navigation. We evaluate our method on a room cleanup task, where the robot must navigate to and pick up items scattered on the floor. After a grasp curriculum training phase, ReLMM can learn navigation and grasping together fully automatically, in around 40 hours of autonomous real-world training.