Glen Berseth

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

Doctorat - UdeM

Maîtrise recherche - UdeM

Site web

Roger Creus-Castanyer

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - McGill

Superviseur⋅e principal⋅e :

Hsiu-Chin Lin

Léa Demeule

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Liam Paull

Kumaraditya Gupta

Doctorat

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - UdeM

Adriana Knatchbull-Hugessen

Doctorat - UdeM

Artur Kuramshin

Maîtrise recherche - UdeM

Site web

Daniel Lawson

Doctorat - UdeM

Co-superviseur⋅e :

Postdoctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Marc Gendron-Bellemare

Collaborateur·rice de recherche

L'apprentissage par renforcement en temps réel

Siddarth Venkatraman

Doctorat - UdeM

Co-superviseur⋅e :

Albert Zhan

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

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

Toward Hardware-Agnostic Quadrupedal World Models via Morphology Conditioning

Chenhao Li

Marco Hutter

World models promise a paradigm shift in robotics, where an agent learns the underlying physics of its environment once to enable efficient … (voir plus)planning and behavior learning. However, current world models are often hardware-locked specialists: a model trained on a Boston Dynamics Spot robot fails catastrophically on a Unitree Go1 due to the mismatch in kinematic and dynamic properties, as the model overfits to specific embodiment constraints rather than capturing the universal locomotion dynamics. Consequently, a slight change in actuator dynamics or limb length necessitates training a new model from scratch. In this work, we take a step towards a framework for training a generalizable Quadrupedal World Model (QWM) that disentangles environmental dynamics from robot morphology. We address the limitations of implicit system identification, where treating static physical properties (like mass or limb length) as latent variables to be inferred from motion history creates an adaptation lag that can compromise zero-shot safety and efficiency. Instead, we explicitly condition the generative dynamics on the robot's engineering specifications. By integrating a physical morphology encoder and a reward normalizer, we enable the model to serve as a neural simulator capable of generalizing across morphologies. This capability unlocks zero-shot control across a range of embodiments. We introduce, for the first time, a world model that enables zero-shot generalization to new morphologies for locomotion. While we carefully study the limitations of our method, QWM operates as a distribution-bounded interpolator within the quadrupedal morphology family rather than a universal physics engine, this work represents a significant step toward morphology-conditioned world models for legged locomotion.

2026-04-08

arXiv (prépublication)

SLowRL: Safe Low-Rank Adaptation Reinforcement Learning for Locomotion

Elham Daneshmand

Shafeef Omar

Majid Khadiv

Hsiu-Chin Lin

Sim-to-real transfer of locomotion policies often leads to performance degradation due to the inevitable sim-to-real gap. Naively fine-tunin… (voir plus)g these policies directly on hardware is problematic, as it poses risks of mechanical failure and suffers from high sample inefficiency. In this paper, we address the challenge of safely and efficiently fine-tuning reinforcement learning (RL) policies for dynamic locomotion tasks. Specifically, we focus on fine-tuning policies learned in simulation directly on hardware, while explicitly enforcing safety constraints. In doing so, we introduce SLowRL, a framework that combines Low-Rank Adaptation (LoRA) with training-time safety enforcement via a recovery policy. We evaluate our method both in simulation and on a real Unitree Go2 quadruped robot for jump and trot tasks. Experimental results show that our method achieves a

2026-03-16

arXiv (prépublication)

Toward Self-Driven Microscopy Exploration for the Characterization of Functional Materials

Claudia M. Bazán

Kirsty Ellis

Ramzi Zidani

Maxime Goulet

Jean-Nicolas Deraspe

Jeanine Looman

Delphine Bouilly

Audrey Laventure

2026-03-14

AI4X @ Neural Information Processing Systems (poster)

Generalization in Online Reinforcement Learning for Mobile Agents

Li Gu

Zihuan Jiang

Zhixiang Chi

Huan Liu

Ziqiang Wang

Yuanhao Yu

Yang Wang

Graphical user interface (GUI)-based mobile agents automate digital tasks on mobile devices by interpreting natural-language instructions an… (voir plus)d interacting with the screen. While recent methods apply reinforcement learning (RL) to train vision-language-model(VLM) agents in interactive environments with a primary focus on performance, generalization remains underexplored due to the lack of standardized benchmarks and open-source RL systems. In this work, we formalize the problem as a Contextual Markov Decision Process (CMDP) and introduce \textbf{AndroidWorld-Generalization}, a benchmark with three increasingly challenging regimes for evaluating zero-shot generalization to unseen task instances, templates, and applications. We further propose an RL training system that integrates Group Relative Policy Optimization (GRPO) with a scalable rollout collection system, consisting of containerized infrastructure and asynchronous execution % , and error recovery to support reliable and efficient training. Experiments on AndroidWorld-Generalization show that RL enables a 7B-parameter VLM agent to surpass supervised fine-tuning baselines, yielding a 26.1\% improvement on unseen instances but only limited gains on unseen templates (15.7\%) and apps (8.3\%), underscoring the challenges of generalization. As a preliminary step, we demonstrate that few-shot adaptation at test-time improves performance on unseen apps, motivating future research in this direction. To support reproducibility and fair comparison, we open-source the full RL training system, including the environment, task suite, models, prompt configurations, and the underlying infrastructure \footnote{https://github.com/zihuanjiang/AndroidWorld-Generalization}.

2026-03-07

arXiv (prépublication)

Align and Filter: Improving Performance in Asynchronous On-Policy RL

Homayoun Honari

Roger Creus Castanyer

Distributed training and increasing the gradient update frequency are practical strategies to accelerate learning and improve performance, b… (voir plus)ut both exacerbate a central challenge: \textit{policy lag}, which is the mismatch between the behavior policy generating data and the learning policy being updated. Policy lag can hinder the scaling of on-policy learning algorithms to larger problems. In this paper, we identify the sources of policy lag caused by distributed learning and high update frequency. We use the findings to propose \textit{total Variation-based Advantage aligned Constrained policy Optimization (\methodacronym)} as a practical approach to mitigate policy lag. We empirically validate our method and show that it offers better robustness to policy lag in classic RL tasks and a modern RL for LLM math reasoning task.

2026-03-01

arXiv (prépublication)

Temporal Representations for Exploration: Learning Complex Exploratory Behavior without Extrinsic Rewards

Catherine Ji

Benjamin Eysenbach

Effective exploration in reinforcement learning requires not only tracking where an agent has been, but also understanding how the agent per… (voir plus)ceives and represents the world. To learn powerful representations, an agent should actively explore states that contribute to its knowledge of the environment. Temporal representations can capture the information necessary to solve a wide range of potential tasks while avoiding the computational cost associated with full state reconstruction. In this paper, we propose an exploration method that leverages temporal contrastive representations to guide exploration, prioritizing states with unpredictable future outcomes. We demonstrate that such representations can enable the learning of complex exploratory x in locomotion, manipulation, and embodied-AI tasks, revealing capabilities and behaviors that traditionally require extrinsic rewards. Unlike approaches that rely on explicit distance learning or episodic memory mechanisms (e.g., quasimetric-based methods), our method builds directly on temporal similarities, yielding a simpler yet effective strategy for exploration.

2026-03-01

arXiv (prépublication)

ARM-FM: Automated Reward Machines via Foundation Models for Compositional Reinforcement Learning

Roger Creus Castanyer

Pablo Samuel Castro

Cyrus Neary

Reinforcement learning (RL) algorithms are highly sensitive to reward function specification, which remains a central challenge limiting the… (voir plus)ir broad applicability. We present ARM-FM: Automated Reward Machines via Foundation Models, a framework for automated, compositional reward design in RL that leverages the high-level reasoning capabilities of foundation models (FMs). Reward machines (RMs) - an automata-based formalism for reward specification - are used as the mechanism for RL objective specification, and are automatically constructed via the use of FMs. The structured formalism of RMs yields effective task decompositions, while the use of FMs enables objective specifications in natural language. Concretely, we (i) use FMs to automatically generate RMs from natural language specifications; (ii) associate language embeddings with each RM automata-state to enable generalization across tasks; and (iii) provide empirical evidence of ARM-FM's effectiveness in a diverse suite of challenging environments, including evidence of zero-shot generalization.

2025-12-31

International Conference on Learning Representations (Accept (Poster))

Discovering Diverse Behaviors via Temporal Contrastive Learning

Catherine Ji

Benjamin Eysenbach

Effective exploration in reinforcement learning requires not only tracking where an agent has been, but also understanding how the agent per… (voir plus)ceives and represents the world. To learn powerful representations, an agent should actively explore states that contribute to its knowledge of the environment. Temporal representations can capture the information necessary to solve a wide range of potential tasks while avoiding the computational cost associated with full state reconstruction. In this paper, we propose an exploration method that leverages temporal contrastive representations to guide exploration, prioritizing states with unpredictable future outcomes. We demonstrate that such representations can enable the learning of complex exploratory behaviors in locomotion, manipulation, and embodied-AI tasks, revealing capabilities and behaviors that traditionally require extrinsic rewards. Unlike approaches that rely on explicit distance learning or episodic memory mechanisms (e.g., quasimetric-based methods), our method builds directly on temporal similarities, yielding a simpler yet effective strategy for exploration.

2025-12-31

International Conference on Learning Representations (Accept (Poster))

A Comedy of Estimators: On KL Regularization in RL Training of LLMs

Vedant Shah

Johan Obando-Ceron

Vineet Jain

Brian Bartoldson

Bhavya Kailkhura

The reasoning performance of large language models (LLMs) can be substantially improved by training them with reinforcement learning (RL). T… (voir plus)he RL objective for LLM training involves a regularization term, which is the reverse Kullback-Leibler (KL) divergence between the trained policy and the reference policy. Since computing the KL divergence exactly is intractable, various estimators are used in practice to estimate it from on-policy samples. Despite its wide adoption, including in several open-source libraries, there is no systematic study analyzing the numerous ways of incorporating KL estimators in the objective and their effect on the downstream performance of RL-trained models. Recent works show that prevailing practices for incorporating KL regularization do not provide correct gradients for stated objectives, creating a discrepancy between the objective and its implementation. In this paper, we further analyze these practices and study the gradients of several estimators configurations, revealing how design choices shape gradient bias. We substantiate these findings with empirical observations by RL fine-tuning \texttt{Qwen2.5-7B}, \texttt{Llama-3.1-8B-Instruct} and \texttt{Qwen3-4B-Instruct-2507} with different configurations and evaluating their performance on both in- and out-of-distribution tasks. Through our analysis, we observe that, in on-policy settings: (1) estimator configurations with biased gradients can result in training instabilities; and (2) using estimator configurations resulting in unbiased gradients leads to better performance on in-domain as well as out-of-domain tasks. We also investigate the performance resulting from different KL configurations in off-policy settings and observe that KL regularization can help stabilize off-policy RL training resulting from asynchronous setups.

2025-12-25

ArXiv (prépublication)

RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies

Pranav Atreya

Karl Pertsch

Tony Lee

Moo Jin Kim

Arhan Jain

Artur Kuramshin

Cyrus Neary

Edward S. Hu

Kanav Arora

Kirsty Ellis

Luca Macesanu

Matthew Leonard

Meedeum Cho

Özgür Aslan

Shivin Dass

Tony Wang

Xingfang Yuan

Abhishek Gupta

Dinesh Jayaraman

Glen Berseth … (voir 6 de plus)

Kostas Daniilidis

Roberto Martín-Martín

Youngwoon Lee

Percy Liang

Chelsea Finn

Sergey Levine

2025-10-06

Proceedings of The 8th Conference on Robot Learning (publié)

proceedings.mlr.press

Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models

Johan Samir Obando Ceron

Yoshua Bengio

Brian R. Bartoldson

Bhavya Kailkhura

Guillaume Lajoie

Nikolay Malkin

Moksh J. Jain

Test-time scaling methods improve the capabilities of large language models (LLMs) by increasing the amount of compute used during inference… (voir plus) to make a prediction. Inference-time compute can be scaled in parallel by choosing among multiple independent solutions or sequentially through self-refinement. We propose Recursive Self-Aggregation (RSA), a test-time scaling method inspired by evolutionary methods that combines the benefits of both parallel and sequential scaling. Each step of RSA refines a population of candidate reasoning chains through aggregation of subsets to yield a population of improved solutions, which are then used as the candidate pool for the next iteration. RSA exploits the rich information embedded in the reasoning chains -- not just the final answers -- and enables bootstrapping from partially correct intermediate steps within different chains of thought. Empirically, RSA delivers substantial performance gains with increasing compute budgets across diverse tasks, model families and sizes. Notably, RSA enables Qwen3-4B-Instruct-2507 to achieve competitive performance with larger reasoning models, including DeepSeek-R1 and o3-mini (high), while outperforming purely parallel and sequential scaling strategies across AIME-25, HMMT-25, Reasoning Gym, LiveCodeBench-v6, and SuperGPQA. We further demonstrate that training the model to combine solutions via a novel aggregation-aware reinforcement learning approach yields significant performance gains. Code available at https://github.com/HyperPotatoNeo/RSA.

2025-09-29

ArXiv (prépublication)

Concept-based Steering of Large Language Models for Conditional Molecular Generation

Yang Zhang

Modern LLMs, with their internet-scale pretraining and advanced human-level capabilities across specialized tasks, have demonstrated promisi… (voir plus)ng performance in molecular discovery using existing text-based molecular representations, such as SMILES and SELFIES. However, generating valid, unique, and high-fidelity molecules while precisely controlling for multiple properties simultaneously remains challenging. While prior works demonstrated success by fine-tuning language models on a novel corpus of molecules with property-conditioned tags, real-world applications require generating molecules from diverse property distributions, previously unseen in the training data. To this end, we present Concept-based Activation STeering (CAST), the first approach to apply activation steering to directly edit a model's internal representation for conditional molecular generation. CAST offers a lightweight, flexible alternative to fine-tuning by computing property-conditioned steering vectors via a concept network that does not require retraining the LLM. Through extensive experiments on datasets such as Therapeutics Data Commons, we show that CAST consistently outperforms existing methods on both in-distribution and out-of-distribution conditional generation tasks. We also conduct comprehensive ablation studies to highlight the extent of control our concept-guided steering provides on the molecules generated by the LLM.

2025-09-19

NeurIPS.cc/2025/Workshop/AI4Mat (poster)