David Meger

Generalizable Imitation Learning Through Pre-Trained Representations

Wei-Di Chang

Francois Hogan

Scott Fujimoto

In this paper we leverage self-supervised vision transformer models and their emergent semantic abilities to improve the generalization abil… (voir plus)ities of imitation learning policies. We introduce BC-ViT, an imitation learning algorithm that leverages rich DINO pre-trained Visual Transformer (ViT) patch-level embeddings to obtain better generalization when learning through demonstrations. Our learner sees the world by clustering appearance features into semantic concepts, forming stable keypoints that generalize across a wide range of appearance variations and object types. We show that this representation enables generalized behaviour by evaluating imitation learning across a diverse dataset of object manipulation tasks. Our method, data and evaluation approach are made available to facilitate further study of generalization in Imitation Learners.

2025-05-19

2025 IEEE International Conference on Robotics and Automation (ICRA) (publié)

doi.org

arxiv.org

Learning active tactile perception through belief-space control

Jean-François Tremblay

Johanna Hansen

David Meger

Francois Hogan

Gregory Dudek

Robot operating in an open world can encounter novel objects with unknown physical properties, such as mass, friction, or size. It is desira… (voir plus)ble to be able to sense those property through contact-rich interaction, before performing downstream tasks with the objects. We propose a method for autonomously learning active tactile perception policies, by learning a generative world model leveraging a differentiable bayesian filtering algorithm, and designing an information- gathering model predictive controller. We test the method on three simulated tasks: mass estimation, height estimation and toppling height estimation. Our method is able to discover policies which gather information about the desired property in an intuitive manner.

2025-05-19

2025 IEEE International Conference on Robotics and Automation (ICRA) (publié)

doi.org

openreview.net

Topological mapping for traversability-aware long-range navigation in off-road terrain

Jean-François Tremblay

Julie Alhosh

Louis Petit

Faraz Lotfi

Lara Landauro

David Meger

Autonomous robots navigating in off-road terrain like forests open new opportunities for automation. While off-road navigation has been stud… (voir plus)ied, existing work often relies on clearly delineated pathways. We present a method allowing for long-range planning, exploration and low-level control in unknown off-trail forest terrain, using vision and GPS only. We represent outdoor terrain with a topological map, which is a set of panoramic snapshots connected with edges containing traversability information. A novel traversability analysis method is demonstrated, predicting the existence of a safe path towards a target in an image. Navigating between nodes is done using goal-conditioned behavior cloning, leveraging the power of a pretrained vision transformer. An exploration planner is presented, efficiently covering an unknown off-road area with unknown traversability using a frontiers-based approach. The approach is successfully deployed to autonomously explore two 400 meters squared forest sites unseen during training, in difficult conditions for navigation.

2025-05-19

2025 IEEE International Conference on Robotics and Automation (ICRA) (publié)

doi.org

arxiv.org

Seeing the Unseen: How EMoE Unveils Bias in Text-to-Image Diffusion Models

Lucas Berry

Axel Brando

Wei-Di Chang

Juan Higuera

David Meger

2025-05-01

arXiv (publié)

doi.org

openreview.net

Tractable Representations for Convergent Approximation of Distributional HJB Equations

Julie Alhosh

Harley Wiltzer

David Meger

2025-03-07

ArXiv (prépublication)

arxiv.org

Tractable Representations for Convergent Approximation of Distributional HJB Equations

Julie Alhosh

Harley Wiltzer

David Meger

2025-03-01

arXiv (publié)

doi.org

arxiv.org

Fairness in Reinforcement Learning with Bisimulation Metrics

Sahand Rezaei-Shoshtari

Hanna Yurchyk

Scott Fujimoto

Doina Precup

David Meger

Ensuring long-term fairness is crucial when developing automated decision making systems, specifically in dynamic and sequential environment… (voir plus)s. By maximizing their reward without consideration of fairness, AI agents can introduce disparities in their treatment of groups or individuals. In this paper, we establish the connection between bisimulation metrics and group fairness in reinforcement learning. We propose a novel approach that leverages bisimulation metrics to learn reward functions and observation dynamics, ensuring that learners treat groups fairly while reflecting the original problem. We demonstrate the effectiveness of our method in addressing disparities in sequential decision making problems through empirical evaluation on a standard fairness benchmark consisting of lending and college admission scenarios.

2024-12-22

ArXiv (prépublication)

doi.org

arxiv.org

Fairness in Reinforcement Learning with Bisimulation Metrics

Sahand Rezaei-Shoshtari

Hanna Yurchyk

Scott Fujimoto

Doina Precup

David Meger

Ensuring long-term fairness is crucial when developing automated decision making systems, specifically in dynamic and sequential environment… (voir plus)s. By maximizing their reward without consideration of fairness, AI agents can introduce disparities in their treatment of groups or individuals. In this paper, we establish the connection between bisimulation metrics and group fairness in reinforcement learning. We propose a novel approach that leverages bisimulation metrics to learn reward functions and observation dynamics, ensuring that learners treat groups fairly while reflecting the original problem. We demonstrate the effectiveness of our method in addressing disparities in sequential decision making problems through empirical evaluation on a standard fairness benchmark consisting of lending and college admission scenarios.

2024-12-22

ArXiv (prépublication)

arxiv.org

Parseval Regularization for Continual Reinforcement Learning

Wesley Chung

Lynn Cherif

David Meger

Doina Precup

Loss of plasticity, trainability loss, and primacy bias have been identified as issues arising when training deep neural networks on sequenc… (voir plus)es of tasks -- all referring to the increased difficulty in training on new tasks. We propose to use Parseval regularization, which maintains orthogonality of weight matrices, to preserve useful optimization properties and improve training in a continual reinforcement learning setting. We show that it provides significant benefits to RL agents on a suite of gridworld, CARL and MetaWorld tasks. We conduct comprehensive ablations to identify the source of its benefits and investigate the effect of certain metrics associated to network trainability including weight matrix rank, weight norms and policy entropy.

2024-12-10

ArXiv (prépublication)

doi.org

arxiv.org

Parseval Regularization for Continual Reinforcement Learning

Wesley Chung

Lynn Cherif

David Meger

Doina Precup

Loss of plasticity, trainability loss, and primacy bias have been identified as issues arising when training deep neural networks on sequenc… (voir plus)es of tasks -- all referring to the increased difficulty in training on new tasks. We propose to use Parseval regularization, which maintains orthogonality of weight matrices, to preserve useful optimization properties and improve training in a continual reinforcement learning setting. We show that it provides significant benefits to RL agents on a suite of gridworld, CARL and MetaWorld tasks. We conduct comprehensive ablations to identify the source of its benefits and investigate the effect of certain metrics associated to network trainability including weight matrix rank, weight norms and policy entropy.

2024-12-10

ArXiv (prépublication)

arxiv.org