Glen Berseth

alexandre.brown@mila.quebec

Biography

Glen Berseth is an assistant professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal and a core academic member of Mila – Quebec Artificial Intelligence Institute.

He is a Canada CIFAR AI Chair and co-directs the Robotics and Embodied AI Lab (REAL). He was formerly a postdoctoral researcher at Berkeley Artificial Intelligence Research (BAIR), working with Sergey Levine.

Berseth’s previous and current research has focused on solving sequential decision-making problems (planning) for real-world autonomous learning systems (robots). More specifically, his research has focused on human-robot collaboration, reinforcement, and continual-, meta-, multi-agent and hierarchical learning.

He has published in the top venues in robotics, machine learning and computer animation. He teaches a course on robot learning at Université de Montréal and at Mila, in which he covers the most recent research on machine learning techniques for creating generalist robots.

Current Students

Özgür Aslan

PhD - Université de Montréal

ozgur.aslan@mila.quebec

PhD - Université de Montréal

Collaborating researcher - Waterloo

ccloutie@uwaterloo.ca

Roger Creus-Castanyer

PhD - Université de Montréal

Co-supervisor :

Pablo Samuel Castro

roger.creus-castanyer@mila.quebec

PhD - McGill University

Principal supervisor :

Hsiu-Chin Lin

elham.daneshmand@mila.quebec

Léa Demeule

PhD - Université de Montréal

Co-supervisor :

Chris Pal

lea.demeule@mila.quebec

kumaraditya.gupta@mila.quebec

Kumaraditya Gupta

PhD

Principal supervisor :

Liam Paull

Collaborating researcher - Université de Montréal

homayoun.honari@mila.quebec

Naser Kazemi

Collaborating researcher - EPFL

naser.kazemi@mila.quebec

Adriana Knatchbull-Hugessen

PhD - Université de Montréal

adriana.knatchbull-hugessen@mila.quebec

artur.kuramshin@mila.quebec

Artur Kuramshin

Master's Research - Université de Montréal

daniel.lawson@mila.quebec

Daniel Lawson

PhD - Université de Montréal

Co-supervisor :

Khimya Khetarpal

Postdoctorate - Université de Montréal

tabitha-edith.lee@mila.quebec

Master's Research - Université de Montréal

faisal.mohamed@mila.quebec

Research Intern - Université de Montréal

josuesmjr.mongan@gmail.com

Esra'a Saleh

PhD - Université de Montréal

Co-supervisor :

Aaron Courville

esraa.saleh@mila.quebec

jesse@jessesilverberg.com

Jesse Silverberg

PhD - Université de Montréal

Co-supervisor :

Marc Gendron-Bellemare

PhD - Université de Montréal

Co-supervisor :

Yoshua Bengio

siddarth.venkatraman@mila.quebec

Real-time Reinforcement Learning

Albert Zhan

PhD - Université de Montréal

albert.zhan@mila.quebec

Blog Posts

Deux robots dans une cuisine, en train de préparer le dîner. L'un coupe les légumes et l'autre fait une omelette.

June 20, 2025

Ivan Anokhin

Matthew Riemer

Rishav Rishav

Gopeshh Subbaraj

Glen Berseth

Read the article

February 15, 2022

Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation Primary tabs View Edit(active tab) Delete Revisions

Jędrzej Orbik

Charles Sun

Coline Devin

Glen Berseth

Read the article

Publications

Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot

Yandong Ji

Zhongyu Li

Yinan Sun

Xue Bin Peng

Sergey Levine

Koushil Sreenath

We address the problem of enabling quadrupedal robots to perform precise shooting skills in the real world using reinforcement learning. Dev… (see more)eloping algorithms to enable a legged robot to shoot a soccer ball to a given target is a challenging problem that combines robot motion control and planning into one task. To solve this problem, we need to consider the dynamics limitation and motion stability during the control of a dynamic legged robot. Moreover, we need to consider motion planning to shoot the hard-to-model deformable ball rolling on the ground with uncertain friction to a desired location. In this paper, we propose a hierarchical framework that leverages deep reinforcement learning to train (a) a robust motion control policy that can track arbitrary motions and (b) a planning policy to decide the desired kicking motion to shoot a soccer ball to a target. We deploy the proposed framework on an A1 quadrupedal robot and enable it to accurately shoot the ball to random targets in the real world.

2022-10-22

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (published)

doi.org

arxiv.org

AnyMorph: Learning Transferable Polices By Inferring Agent Morphology

Brandon Trabucco

Mariano Phielipp

The prototypical approach to reinforcement learning involves training policies tailored to a particular agent from scratch for every new mor… (see more)phology. Recent work aims to eliminate the re-training of policies by investigating whether a morphology-agnostic policy, trained on a diverse set of agents with similar task objectives, can be transferred to new agents with unseen morphologies without re-training. This is a challenging problem that required previous approaches to use hand-designed descriptions of the new agent's morphology. Instead of hand-designing this description, we propose a data-driven method that learns a representation of morphology directly from the reinforcement learning objective. Ours is the first reinforcement learning algorithm that can train a policy to generalize to new agent morphologies without requiring a description of the agent's morphology in advance. We evaluate our approach on the standard benchmark for agent-agnostic control, and improve over the current state of the art in zero-shot generalization to new agents. Importantly, our method attains good performance without an explicit description of morphology.

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

doi.org

proceedings.mlr.press

ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning

Sean Chen

Jensen Gao

Siddharth Reddy

Anca Dragan

Sergey Levine

Building assistive interfaces for controlling robots through arbitrary, high-dimensional, noisy inputs (e.g., webcam images of eye gaze) can… (see more) be challenging, especially when it involves inferring the user's desired action in the absence of a natural ‘default’ interface. Reinforcement learning from online user feedback on the system's performance presents a natural solution to this problem, and enables the interface to adapt to individual users. However, this approach tends to require a large amount of human-in-the-loop training data, especially when feedback is sparse. We propose a hierarchical solution that learns efficiently from sparse user feedback: we use offline pre-training to acquire a latent embedding space of useful, high-level robot behaviors, which, in turn, enables the system to focus on using online user feedback to learn a mapping from user inputs to desired high-level behaviors. The key insight is that access to a pre-trained policy enables the system to learn more from sparse rewards than a naïve RL algorithm: using the pre-trained policy, the system can make use of successful task executions to relabel, in hindsight, what the user actually meant to do during unsuccessful executions. We evaluate our method primarily through a user study with 12 participants who perform tasks in three simulated robotic manipulation domains using a webcam and their eye gaze: flipping light switches, opening a shelf door to reach objects inside, and rotating a valve. The results show that our method successfully learns to map 128-dimensional gaze features to 7-dimensional joint torques from sparse rewards in under 10 minutes of online training, and seamlessly helps users who employ different gaze strategies, while adapting to distributional shift in webcam inputs, tasks, and environments

2022-05-22

2022 International Conference on Robotics and Automation (ICRA) (published)

doi.org

arxiv.org

Revisiting Successor Features for Inverse Reinforcement Learning

Sanjiban Choudhury

2019-12-31

International Conference on Machine Learning (published)

openreview.net

Training PPO-Clip with Parallelized Data Generation: A Case of Fixed-Point Convergence

Homayoun Honari

Roger Creus Castanyer

Pablo Samuel Castro