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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PhD - McGill University
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Master's Research - McGill University
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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

Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling
Lucas Berry
Learning active tactile perception through belief-space control
Jean-François Tremblay
Johanna Hansen
Francois Hogan
Robot operating in an open world can encounter novel objects with unknown physical properties, such as mass, friction, or size. It is desira… (see more)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.