Glen Berseth

Google Scholar

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

Maîtrise recherche - UdeM

Charlotte Cloutier Cloutier

Collaborateur·rice de recherche - Waterloo

ccloutie@uwaterloo.ca

Florence Cloutier

Maîtrise professionnelle - UdeM

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

Collaborateur·rice de recherche

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - UdeM

Adriana Knatchbull-Hugessen

Google Scholar

Doctorat - UdeM

Artur Kuramshin

Maîtrise recherche - UdeM

Daniel Lawson

Doctorat - UdeM

Co-superviseur⋅e :

Postdoctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Postdoctorat - UdeM

Co-superviseur⋅e :

Maîtrise professionnelle - UdeM

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

Google Scholar

Michael Przystupa

Stagiaire de recherche - UdeM

Esra'a Saleh

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Co-superviseur⋅e :

Billets de blogue

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

15 février 2023

par

Jędrzej Orbik

Charles Sun

Coline Devin

Glen Berseth

Lire l'article

Publications

Intelligent Switching for Reset-Free RL

Darshan Patil

Janarthanan Rajendran

Sarath Chandar

2024-01-16

ICLR.cc/2024/Conference (poster)

Intelligent Switching for 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… (voir plus)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.

2024-01-16

ICLR.cc/2024/Conference (poster)

Reasoning with Latent Diffusion in Offline Reinforcement Learning

Siddarth Venkatraman

Shivesh Khaitan

Ravi Tej Akella

John Dolan

Jeff Schneider

2024-01-16

ICLR.cc/2024/Conference (poster)

Reasoning with Latent Diffusion in Offline Reinforcement Learning

Siddarth Venkatraman

Shivesh Khaitan

Ravi Tej Akella

John Dolan

Jeff Schneider

2024-01-16

ICLR.cc/2024/Conference (poster)

Searching for High-Value Molecules Using Reinforcement Learning and Transformers

Raj Ghugare

Santiago Miret

Adriana Hugessen

Mariano Phielipp

2024-01-16

ICLR.cc/2024/Conference (poster)

Adaptive Resolution Residual Networks

Léa Demeule

Mahtab Sandhu

We introduce Adaptive Resolution Residual Networks (ARRNs), a form of neural operator that enables the creation of networks for signal-based… (voir plus) tasks that can be rediscretized to suit any signal resolution. ARRNs are composed of a chain of Laplacian residuals that each contain ordinary layers, which do not need to be rediscretizable for the whole network to be rediscretizable. ARRNs have the property of requiring a lower number of Laplacian residuals for exact evaluation on lower-resolution signals, which greatly reduces computational cost. ARRNs also implement Laplacian dropout, which encourages networks to become robust to low-bandwidth signals. ARRNs can thus be trained once at high-resolution and then be rediscretized on the fly at a suitable resolution with great robustness.

2023-10-31

NeurIPS.cc/2023/Workshop/DLDE (published)

Improving Intrinsic Exploration by Creating Stationary Objectives

Roger Creus Castanyer

Joshua Romoff

2023-10-27

ArXiv (prépublication)

Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement Learning

Adriana Hugessen

Roger Creus Castanyer

2023-10-20

NeurIPS.cc/2023/Workshop/IMOL (présentation orale)

Searching for High-Value Molecules Using Reinforcement Learning and Transformers

Raj Ghugare

Santiago Miret

Adriana Hugessen

Mariano Phielipp

Reinforcement learning (RL) over text representations can be effective for finding high-value policies that can search over graphs. However,… (voir plus) RL requires careful structuring of the search space and algorithm design to be effective in this challenge. Through extensive experiments, we explore how different design choices for text grammar and algorithmic choices for training can affect an RL policy's ability to generate molecules with desired properties. We arrive at a new RL-based molecular design algorithm (ChemRLformer) and perform a thorough analysis using 25 molecule design tasks, including computationally complex protein docking simulations. From this analysis, we discover unique insights in this problem space and show that ChemRLformer achieves state-of-the-art performance while being more straightforward than prior work by demystifying which design choices are actually helpful for text-based molecule design.

2023-10-04

ArXiv (prépublication)

Bootstrapping Adaptive Human-Machine Interfaces with Offline Reinforcement Learning

Jensen Gao

Siddharth Reddy

Anca Dragan

Sergey Levine

Adaptive interfaces can help users perform sequential decision-making tasks like robotic teleoperation given noisy, high-dimensional command… (voir plus) signals (e.g., from a brain-computer interface). Recent advances in human-in-the-loop machine learning enable such systems to improve by interacting with users, but tend to be limited by the amount of data that they can collect from individual users in practice. In this paper, we propose a reinforcement learning algorithm to address this by training an interface to map raw command signals to actions using a combination of offline pre-training and online fine-tuning. To address the challenges posed by noisy command signals and sparse rewards, we develop a novel method for representing and inferring the user's long-term intent for a given trajectory. We primarily evaluate our method's ability to assist users who can only communicate through noisy, high-dimensional input channels through a user study in which 12 participants performed a simulated navigation task by using their eye gaze to modulate a 128-dimensional command signal from their webcam. The results show that our method enables successful goal navigation more often than a baseline directional interface, by learning to denoise user commands signals and provide shared autonomy assistance. We further evaluate on a simulated Sawyer pushing task with eye gaze control, and the Lunar Lander game with simulated user commands, and find that our method improves over baseline interfaces in these domains as well. Extensive ablation experiments with simulated user commands empirically motivate each component of our method.

2023-10-01

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)

Torque-Based Deep Reinforcement Learning for Task-and-Robot Agnostic Learning on Bipedal Robots Using Sim-to-Real Transfer

Donghyeon Kim

Mathew Schwartz

Jaeheung Park

In this letter, we review the question of which action space is best suited for controlling a real biped robot in combination with Sim2Real … (voir plus)training. Position control has been popular as it has been shown to be more sample efficient and intuitive to combine with other planning algorithms. However, for position control, gain tuning is required to achieve the best possible policy performance. We show that, instead, using a torque-based action space enables task-and-robot agnostic learning with less parameter tuning and mitigates the sim-to-reality gap by taking advantage of torque control's inherent compliance. Also, we accelerate the torque-based-policy training process by pre-training the policy to remain upright by compensating for gravity. The letter showcases the first successful sim-to-real transfer of a torque-based deep reinforcement learning policy on a real human-sized biped robot.

2023-10-01

IEEE Robotics and Automation Letters (publié)

Maximum State Entropy Exploration using Predecessor and Successor Representations

Arnav Kumar Jain

Lucas Lehnert

Irina Rish

Animals have a developed ability to explore that aids them in important tasks such as locating food, exploring for shelter, and finding misp… (voir plus)laced items. These exploration skills necessarily track where they have been so that they can plan for finding items with relative efficiency. Contemporary exploration algorithms often learn a less efficient exploration strategy because they either condition only on the current state or simply rely on making random open-loop exploratory moves. In this work, we propose