Glen Berseth

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

PhD - Université de Montréal

Master's Research - Université de Montréal

Roger Creus-Castanyer

PhD - Université de Montréal

Co-supervisor :

PhD - McGill University

Principal supervisor :

Hsiu-Chin Lin

Léa Demeule

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Principal supervisor :

Liam Paull

Kumaraditya Gupta

PhD

Principal supervisor :

Collaborating researcher - Université de Montréal

Adriana Knatchbull-Hugessen

PhD - Université de Montréal

Artur Kuramshin

Master's Research - Université de Montréal

Daniel Lawson

PhD - Université de Montréal

Co-supervisor :

Postdoctorate - Université de Montréal

Co-supervisor :

Master's Research - Université de Montréal

Research Intern - Université de Montréal

Esra'a Saleh

PhD - Université de Montréal

Co-supervisor :

PhD - Université de Montréal

Co-supervisor :

Marc Gendron-Bellemare

Collaborating researcher

Real-time Reinforcement Learning

Siddarth Venkatraman

PhD - Université de Montréal

Co-supervisor :

Albert Zhan

PhD - Université de Montréal

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

Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn

Hongyao Tang

Deep neural networks provide Reinforcement Learning (RL) powerful function approximators to address large-scale decision-making problems. Ho… (see more)wever, these approximators introduce challenges due to the non-stationary nature of RL training. One source of the challenges in RL is that output predictions can churn, leading to uncontrolled changes after each batch update for states not included in the batch. Although such a churn phenomenon exists in each step of network training, how churn occurs and impacts RL remains under-explored. In this work, we start by characterizing churn in a view of Generalized Policy Iteration with function approximation, and we discover a chain effect of churn that leads to a cycle where the churns in value estimation and policy improvement compound and bias the learning dynamics throughout the iteration. Further, we concretize the study and focus on the learning issues caused by the chain effect in different settings, including greedy action deviation in value-based methods, trust region violation in proximal policy optimization, and dual bias of policy value in actor-critic methods. We then propose a method to reduce the chain effect across different settings, called Churn Approximated ReductIoN (CHAIN), which can be easily plugged into most existing DRL algorithms. Our experiments demonstrate the effectiveness of our method in both reducing churn and improving learning performance across online and offline, value-based and policy-based RL settings, as well as a scaling setting.

2024-09-24

NeurIPS.cc/2024/Conference (poster)

Simplifying Constraint Inference with Inverse Reinforcement Learning

Adriana Hugessen

Harley Wiltzer

Learning safe policies has presented a longstanding challenge for the reinforcement learning (RL) community. Various formulations of safe RL… (see more) have been proposed; However, fundamentally, tabula rasa RL must learn safety constraints through experience, which is problematic for real-world applications. Imitation learning is often preferred in real-world settings because the experts’ safety preferences are embedded in the data the agent imitates. However, imitation learning is limited in its extensibility to new tasks, which can only be learned by providing the agent with expert trajectories. For safety-critical applications with sub-optimal or inexact expert data, it would be preferable to learn only the safety aspects of the policy through imitation, while still allowing for task learning with RL. The field of inverse constrained RL, which seeks to infer constraints from expert data, is a promising step in this direction. However, prior work in this area has relied on complex tri-level optimizations in order to infer safe behavior (constraints). This challenging optimization landscape leads to sub-optimal performance on several benchmark tasks. In this work, we present a simplified version of constraint inference that performs as well or better than prior work across a collection of continuous-control benchmarks. Moreover, besides improving performance, this simplified framework is easier to implement, tune, and more readily lends itself to various extensions, such as offline constraint inference. Our code is made available at https://github.com/ahugs/simple-icrl.

2024-09-24

NeurIPS.cc/2024/Conference (poster)

Enhancing Agent Learning through World Dynamics Modeling

Xingdi Yuan

Large language models (LLMs) have been increasingly applied to tasks in language understanding and interactive decision-making, with their i… (see more)mpressive performance largely attributed to the extensive domain knowledge embedded within them. However, the depth and breadth of this knowledge can vary across domains. Many existing approaches assume that LLMs possess a comprehensive understanding of their environment, often overlooking potential gaps in their grasp of actual world dynamics. To address this, we introduce Discover, Verify, and Evolve (DiVE), a framework that discovers world dynamics from a small number of demonstrations, verifies the accuracy of these dynamics, and evolves new, advanced dynamics tailored to the current situation. Through extensive evaluations, we assess the impact of each component on performance and compare the dynamics generated by DiVE to human-annotated dynamics. Our results show that LLMs guided by DiVE make more informed decisions, achieving rewards comparable to human players in the Crafter environment and surpassing methods that require prior task-specific training in the MiniHack environment.

2024-07-24

arXiv (Cornell University) (preprint)

arxiv.org

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Alexander Khazatsky

Karl Pertsch

Suraj Nair

Ashwin Balakrishna

Sudeep Dasari

Siddharth Karamcheti

Soroush Nasiriany

Mohan Kumar Srirama

Lawrence Yunliang Chen

Kirsty Ellis

Peter David Fagan

Joey Hejna

Masha Itkina

Marion Lepert

Yecheng Jason Ma

Ye Ma

Patrick Tree Miller

Jimmy Wu

Suneel Belkhale

Shivin Dass … (see 82 more)

Huy Ha

Arhan Jain

Abraham Lee

Youngwoon Lee

Marius Memmel

Sungjae Park

Ilija Radosavovic

Kaiyuan Wang

Albert Zhan

Kevin Black

Cheng Chi

Kyle Beltran Hatch

Shan Lin

Jingpei Lu

Jean Mercat

Abdul Rehman

Pannag R Sanketi

Archit Sharma

Cody Simpson

Quan Vuong

Homer Rich Walke

Blake Wulfe

Ted Xiao

Jonathan Heewon Yang

Arefeh Yavary

Tony Z. Zhao

Christopher Agia

Rohan Baijal

Mateo Guaman Castro

Daphne Chen

Qiuyu Chen

Trinity Chung

Jaimyn Drake

Ethan Paul Foster

Jensen Gao

David Antonio Herrera

Minho Heo

Kyle Hsu

Jiaheng Hu

Muhammad Zubair Irshad

Donovon Jackson

Charlotte Le

Xinyu Lin

Yunshuang Li

K. Lin

Roy Lin

Zehan Ma

Abhiram Maddukuri

Suvir Mirchandani

Daniel Morton

Tony Khuong Nguyen

Abigail O'Neill

Rosario Scalise

Derick Seale

Victor Son

Stephen Tian

Emi Tran

Andrew E. Wang

Yilin Wu

Annie Xie

Jingyun Yang

Patrick Yin

Yunchu Zhang

Osbert Bastani

Jeannette Bohg

Ken Goldberg

Abhinav Gupta

Abhishek Gupta

Dinesh Jayaraman

Joseph J Lim

Jitendra Malik

Roberto Martín-Martín

Subramanian Ramamoorthy

Dorsa Sadigh

Shuran Song

Jiajun Wu

Michael C. Yip

Yuke Zhu

Thomas Kollar

Sergey Levine

Chelsea Finn

The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and … (see more)robust robotic manipulation policies. However, creating such datasets is challenging: collecting robot manipulation data in diverse environments poses logistical and safety challenges and requires substantial investments in hardware and human labour. As a result, even the most general robot manipulation policies today are mostly trained on data collected in a small number of environments with limited scene and task diversity. In this work, we introduce DROID (Distributed Robot Interaction Dataset), a diverse robot manipulation dataset with 76k demonstration trajectories or 350 hours of interaction data, collected across 564 scenes and 84 tasks by 50 data collectors in North America, Asia, and Europe over the course of 12 months. We demonstrate that training with DROID leads to policies with higher performance and improved generalization ability. We open source the full dataset, policy learning code, and a detailed guide for reproducing our robot hardware setup.

2024-07-14

Robotics: Science and Systems XX (published)

Realtime Reinforcement Learning: Towards Rapid Asynchronous Deployment of Large Models

Matthew D Riemer

Gopeshh Subbaraj

Irina Rish

Realtime environments change even as agents perform action inference and learning, thus requiring high interaction frequencies to effectivel… (see more)y minimize long-term regret. However, recent advances in machine learning involve larger neural networks with longer inference times, raising questions about their applicability in realtime systems where reaction time is crucial. We present an analysis of lower bounds on regret in realtime environments to show that minimizing long-term regret is generally impossible within the typical sequential interaction and learning paradigm, but often becomes possible when sufficient asynchronous compute is available. We propose novel algorithms for staggering asynchronous inference processes to ensure that actions are taken at consistent time intervals, and demonstrate that use of models with high action inference times is only constrained by the environment's effective stochasticity over the inference horizon, and not by action frequency. Our analysis shows that the number of inference processes needed scales linearly with increasing inference times while enabling use of models that are multiple orders of magnitude larger than existing approaches when learning from a realtime simulation of Game Boy games such as Pokemon and Tetris.

2024-06-18

ICML.cc/2024/Workshop/ARLET (poster)

Revisiting Successor Features for Inverse Reinforcement Learning

Sanjiban Choudhury

2024-06-16

ICML.cc/2024/Workshop/MFHAIA (poster)

Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement Learning

Adriana Hugessen

Roger Creus Castanyer

Faisal Mohamed

Both entropy-minimizing and entropy-maximizing (curiosity) objectives for unsupervised reinforcement learning (RL) have been shown to be eff… (see more)ective in different environments, depending on the environment's level of natural entropy. However, neither method alone results in an agent that will consistently learn intelligent behavior across environments. In an effort to find a single entropy-based method that will encourage emergent behaviors in any environment, we propose an agent that can adapt its objective online, depending on the entropy conditions by framing the choice as a multi-armed bandit problem. We devise a novel intrinsic feedback signal for the bandit, which captures the agent's ability to control the entropy in its environment. We demonstrate that such agents can learn to control entropy and exhibit emergent behaviors in both high- and low-entropy regimes and can learn skillful behaviors in benchmark tasks. Videos of the trained agents and summarized findings can be found on our project page https://sites.google.com/view/surprise-adaptive-agents

2024-05-13

rl-conference.cc/RLC/2024/Conference (published)

Closing the Gap between TD Learning and Supervised Learning - A Generalisation Point of View

Raj Ghugare

Matthieu Geist

Benjamin Eysenbach

Some reinforcement learning (RL) algorithms can stitch pieces of experience to solve a task never seen before during training. This oft-soug… (see more)ht property is one of the few ways in which RL methods based on dynamic-programming differ from RL methods based on supervised-learning (SL). Yet, certain RL methods based on off-the-shelf SL algorithms achieve excellent results without an explicit mechanism for stitching; it remains unclear whether those methods forgo this important stitching property. This paper studies this question for the problems of achieving a target goal state and achieving a target return value. Our main result is to show that the stitching property corresponds to a form of combinatorial generalization: after training on a distribution of (state, goal) pairs, one would like to evaluate on (state, goal) pairs not seen together in the training data. Our analysis shows that this sort of generalization is different from i.i.d. generalization. This connection between stitching and generalisation reveals why we should not expect SL-based RL methods to perform stitching, even in the limit of large datasets and models. Based on this analysis, we construct new datasets to explicitly test for this property, revealing that SL-based methods lack this stitching property and hence fail to perform combinatorial generalization. Nonetheless, the connection between stitching and combinatorial generalisation also suggests a simple remedy for improving generalisation in SL: data augmentation. We propose a temporal data augmentation and demonstrate that adding it to SL-based methods enables them to successfully complete tasks not seen together during training. On a high level, this connection illustrates the importance of combinatorial generalization for data efficiency in time-series data beyond tasks beyond RL, like audio, video, or text.

2024-01-15

ICLR.cc/2024/Conference (poster)

Improving Intrinsic Exploration by Creating Stationary Objectives

Roger Creus Castanyer

Joshua Romoff

2024-01-15

ICLR.cc/2024/Conference (poster)

Intelligent Switching in Reset-Free RL

Darshan Patil

Janarthanan Rajendran

Sarath Chandar

In the real world, the strong episode resetting mechanisms that are needed to train agents in simulation are unavailable. The \textit{resett… (see more)ing} assumption limits the potential of reinforcement learning in the real world, as providing resets to an agent usually requires the creation of additional handcrafted mechanisms or human interventions. Recent work aims to train agents (\textit{forward}) with learned resets by constructing a second (\textit{backward}) agent that returns the forward agent to the initial state. We find that the termination and timing of the transitions between these two agents are crucial for algorithm success. With this in mind, we create a new algorithm, Reset Free RL with Intelligently Switching Controller (RISC) which intelligently switches between the two agents based on the agent's confidence in achieving its current goal. Our new method achieves state-of-the-art performance on several challenging environments for reset-free RL.

2024-01-15

ICLR.cc/2024/Conference (poster)

Reasoning with Latent Diffusion in Offline Reinforcement Learning

Siddarth Venkatraman

Shivesh Khaitan

Ravi Tej Akella

John Dolan

Jeff Schneider

Offline reinforcement learning (RL) holds promise as a means to learn high-reward policies from a static dataset, without the need for furth… (see more)er environment interactions. However, a key challenge in offline RL lies in effectively stitching portions of suboptimal trajectories from the static dataset while avoiding extrapolation errors arising due to a lack of support in the dataset. Existing approaches use conservative methods that are tricky to tune and struggle with multi-modal data (as we show) or rely on noisy Monte Carlo return-to-go samples for reward conditioning. In this work, we propose a novel approach that leverages the expressiveness of latent diffusion to model in-support trajectory sequences as compressed latent skills. This facilitates learning a Q-function while avoiding extrapolation error via batch-constraining. The latent space is also expressive and gracefully copes with multi-modal data. We show that the learned temporally-abstract latent space encodes richer task-specific information for offline RL tasks as compared to raw state-actions. This improves credit assignment and facilitates faster reward propagation during Q-learning. Our method demonstrates state-of-the-art performance on the D4RL benchmarks, particularly excelling in long-horizon, sparse-reward tasks.

2024-01-15

ICLR.cc/2024/Conference (poster)

Searching for High-Value Molecules Using Reinforcement Learning and Transformers

Raj Ghugare

Santiago Miret

Adriana Hugessen

Mariano Phielipp