Glen Berseth

alexandre.brown@mila.quebec

Biographie

Glen Berseth est professeur agrégé au Département d'informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal, membre académique principal de Mila – Institut québécois d'intelligence artificielle, détenteur d’une chaire en IA Canada-CIFAR et codirecteur du Laboratoire de robotique et d’IA intégrative de Montréal (REAL). Il a été chercheur postdoctoral à Berkeley Artificial Intelligence Research (BAIR), où il a travaillé avec Sergey Levine. Ses recherches portent sur la résolution de problèmes de prise de décision séquentielle (planification) pour les systèmes d'apprentissage autonomes du monde réel (robots). Elles ont couvert les domaines de la collaboration humain-robot, du renforcement, ainsi que de l'apprentissage continu, multiagent et hiérarchique et du méta-apprentissage. Glen Berseth a fait paraître des articles dans les meilleures publications des domaines de la robotique, de l'apprentissage automatique et de l'animation informatique. Il donne également un cours sur l'apprentissage des robots à l'Université de Montréal et à Mila, couvrant les recherches les plus récentes sur les techniques d'apprentissage automatique pour la création de robots généralistes.

Étudiants actuels

Özgür Aslan

Doctorat - UdeM

ozgur.aslan@mila.quebec

Doctorat - UdeM

Collaborateur·rice de recherche - Waterloo

ccloutie@uwaterloo.ca

Roger Creus-Castanyer

Doctorat - UdeM

Co-superviseur⋅e :

Pablo Samuel Castro

roger.creus-castanyer@mila.quebec

Doctorat - McGill

Superviseur⋅e principal⋅e :

Hsiu-Chin Lin

elham.daneshmand@mila.quebec

Léa Demeule

Doctorat - UdeM

Co-superviseur⋅e :

Chris Pal

lea.demeule@mila.quebec

kumaraditya.gupta@mila.quebec

Kumaraditya Gupta

Doctorat

Superviseur⋅e principal⋅e :

Liam Paull

Collaborateur·rice de recherche - UdeM

homayoun.honari@mila.quebec

Naser Kazemi

Collaborateur·rice de recherche - EPFL

naser.kazemi@mila.quebec

Adriana Knatchbull-Hugessen

Doctorat - UdeM

adriana.knatchbull-hugessen@mila.quebec

artur.kuramshin@mila.quebec

Artur Kuramshin

Maîtrise recherche - UdeM

daniel.lawson@mila.quebec

Daniel Lawson

Doctorat - UdeM

Co-superviseur⋅e :

Khimya Khetarpal

Postdoctorat - UdeM

tabitha-edith.lee@mila.quebec

Maîtrise recherche - UdeM

faisal.mohamed@mila.quebec

Stagiaire de recherche - UdeM

josuesmjr.mongan@gmail.com

Esra'a Saleh

Doctorat - UdeM

Co-superviseur⋅e :

Aaron Courville

esraa.saleh@mila.quebec

jesse@jessesilverberg.com

Jesse Silverberg

Doctorat - UdeM

Co-superviseur⋅e :

Marc Gendron-Bellemare

Doctorat - UdeM

Co-superviseur⋅e :

Yoshua Bengio

siddarth.venkatraman@mila.quebec

L'apprentissage par renforcement en temps réel

Albert Zhan

Doctorat - UdeM

albert.zhan@mila.quebec

Billets de blogue

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

20 juin 2025

par

Ivan Anokhin

Matthew Riemer

Rishav Rishav

Gopeshh Subbaraj

Glen Berseth

Lire l'article

Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation

15 février 2023

Apprentissage par renforcement entièrement autonome dans le monde réel avec des applications à la manipulation mobile

par

Jędrzej Orbik

Charles Sun

Coline Devin

Glen Berseth

Lire l'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… (voir plus)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) (publié)

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… (voir plus)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… (voir plus) 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) (publié)

doi.org

arxiv.org

Revisiting Successor Features for Inverse Reinforcement Learning

Sanjiban Choudhury

2019-12-31

International Conference on Machine Learning (publié)

openreview.net

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

Homayoun Honari

Roger Creus Castanyer

Pablo Samuel Castro