Portrait de Glen Berseth

Glen Berseth

Membre académique principal

glen.berseth@mila.quebec

Chaire en IA Canada-CIFAR

Professeur agrégé, Université de Montréal, Département d'informatique et de recherche opérationnelle

Sujets de recherche

Apprentissage par renforcement

Apprentissage profond

Robotique

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

Doctorat - UdeM

Alexandre Brown

Doctorat - UdeM

Florence Cloutier

Maîtrise recherche - UdeM

Roger Creus-Castanyer

Doctorat - UdeM

Co-superviseur⋅e :

Pablo Samuel Castro

Elham Daneshmand

Doctorat - McGill

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Charlie Gauthier

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Kumaraditya Gupta

Doctorat

Superviseur⋅e principal⋅e :

Homayoun Honari

Collaborateur·rice de recherche - UdeM

Adriana Knatchbull-Hugessen

Doctorat - UdeM

Artur Kuramshin

Maîtrise recherche - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Khimya Khetarpal

Tabitha Edith Lee

Postdoctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Stagiaire de recherche - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Aaron Courville

Jesse Silverberg

Doctorat - UdeM

Co-superviseur⋅e :

Marc Gendron-Bellemare

Bhargava Uppuluri

Collaborateur·rice de recherche

Siddarth Venkatraman

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

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

L'apprentissage par renforcement en temps réel

par

Gopeshh Subbaraj

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

Publications

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)

Heterogeneous Crowd Simulation Using Parametric Reinforcement Learning.

Kaidong Hu

M. Brandon Haworth

Vladimir Pavlovic

Petros Faloutsos

Mubbasir Kapadia

Agent-based synthetic crowd simulation affords the cost-effective large-scale simulation and animation of interacting digital humans. Model-… (voir plus)based approaches have successfully generated a plethora of simulators with a variety of foundations. However, prior approaches have been based on statically defined models predicated on simplifying assumptions, limited video-based datasets, or homogeneous policies. Recent works have applied reinforcement learning to learn policies for navigation. However, these approaches may learn static homogeneous rules, are typically limited in their generalization to trained scenarios, and limited in their usability in synthetic crowd domains. In this article, we present a multi-agent reinforcement learning-based approach that learns a parametric predictive collision avoidance and steering policy. We show that training over a parameter space produces a flexible model across crowd configurations. That is, our goal-conditioned approach learns a parametric policy that affords heterogeneous synthetic crowds. We propose a model-free approach without centralization of internal agent information, control signals, or agent communication. The model is extensively evaluated. The results show policy generalization across unseen scenarios, agent parameters, and out-of-distribution parameterizations. The learned model has comparable computational performance to traditional methods. Qualitatively the model produces both expected (laminar flow, shuffling, bottleneck) and unexpected (side-stepping) emergent qualitative behaviours, and quantitatively the approach is performant across measures of movement quality.

2021-12-28

IEEE Transactions on Visualization and Computer Graphics (publié)