Portrait of David Meger

David Meger

Associate Academic Member
Associate Professor, McGill University, School of Computer Science
Research Topics
Computer Vision
Reinforcement Learning

Biography

David Meger is an associate professor at McGill University’s School of Computer Science.

He co-directs the Mobile Robotics Lab within the Centre for Intelligent Machines, one of Canada's largest and longest-running robotics research groups. He was the general chair of Canada’s first joint CS-CAN conference in 2023.

Meger's research contributions include visually guided robots powered by active vision and learning, deep reinforcement learning models that are widely cited and used by researchers and industry worldwide, and field robotics that allow for autonomous deployment underwater and on land.

Current Students

Master's Research - McGill University
Collaborating researcher - McGill University
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PhD - McGill University
PhD - McGill University
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PhD - McGill University
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Master's Research - McGill University
Co-supervisor :
Master's Research - McGill University
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PhD - McGill University
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Postdoctorate - McGill University
PhD - McGill University
Principal supervisor :
PhD - McGill University
Master's Research - McGill University
PhD - McGill University
Co-supervisor :
Master's Research - McGill University

Publications

Tractable Representations for Convergent Approximation of Distributional HJB Equations
Julie Alhosh
Harley Wiltzer
Fairness in Reinforcement Learning with Bisimulation Metrics
Sahand Rezaei-Shoshtari
Hanna Yurchyk
Scott Fujimoto
Ensuring long-term fairness is crucial when developing automated decision making systems, specifically in dynamic and sequential environment… (see more)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.
Fairness in Reinforcement Learning with Bisimulation Metrics
Sahand Rezaei-Shoshtari
Hanna Yurchyk
Scott Fujimoto
Ensuring long-term fairness is crucial when developing automated decision making systems, specifically in dynamic and sequential environment… (see more)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.
Parseval Regularization for Continual Reinforcement Learning
Wesley Chung
Lynn Cherif
Loss of plasticity, trainability loss, and primacy bias have been identified as issues arising when training deep neural networks on sequenc… (see more)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.
Parseval Regularization for Continual Reinforcement Learning
Wesley Chung
Lynn Cherif
Loss of plasticity, trainability loss, and primacy bias have been identified as issues arising when training deep neural networks on sequenc… (see more)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.
Topological mapping for traversability-aware long-range navigation in off-road terrain
Jean-Franccois Tremblay
Julie Alhosh
Louis Petit
Faraz Lotfi
Lara Landauro
Autonomous robots navigating in off-road terrain like forests open new opportunities for automation. While off-road navigation has been stud… (see more)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.
Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning
Harley Wiltzer
Patrick Shafto
Yash Jhaveri
Parseval Regularization for Continual Reinforcement Learning
Wesley Chung
Lynn Cherif
Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models
Lucas Berry
Axel Brando
Generative diffusion models, notable for their large parameter count (exceeding 100 million) and operation within high-dimensional image spa… (see more)ces, pose significant challenges for traditional uncertainty estimation methods due to computational demands. In this work, we introduce an innovative framework, Diffusion Ensembles for Capturing Uncertainty (DECU), designed for estimating epistemic uncertainty for diffusion models. The DECU framework introduces a novel method that efficiently trains ensembles of conditional diffusion models by incorporating a static set of pre-trained parameters, drastically reducing the computational burden and the number of parameters that require training. Additionally, DECU employs Pairwise-Distance Estimators (PaiDEs) to accurately measure epistemic uncertainty by evaluating the mutual information between model outputs and weights in high-dimensional spaces. The effectiveness of this framework is demonstrated through experiments on the ImageNet dataset, highlighting its capability to capture epistemic uncertainty, specifically in under-sampled image classes.
Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models
Lucas Berry
Axel Brando
Generative diffusion models, notable for their large parameter count (exceeding 100 million) and operation within high-dimensional image spa… (see more)ces, pose significant challenges for traditional uncertainty estimation methods due to computational demands. In this work, we introduce an innovative framework, Diffusion Ensembles for Capturing Uncertainty (DECU), designed for estimating epistemic uncertainty for diffusion models. The DECU framework introduces a novel method that efficiently trains ensembles of conditional diffusion models by incorporating a static set of pre-trained parameters, drastically reducing the computational burden and the number of parameters that require training. Additionally, DECU employs Pairwise-Distance Estimators (PaiDEs) to accurately measure epistemic uncertainty by evaluating the mutual information between model outputs and weights in high-dimensional spaces. The effectiveness of this framework is demonstrated through experiments on the ImageNet dataset, highlighting its capability to capture epistemic uncertainty, specifically in under-sampled image classes.
Imitation Learning from Observation through Optimal Transport
Wei-Di Chang
Scott Fujimoto
Constrained Robotic Navigation on Preferred Terrains Using LLMs and Speech Instruction: Exploiting the Power of Adverbs
Faraz Lotfi
Farnoosh Faraji
Nikhil Kakodkar
Travis Manderson