David Meger

William Bonilla

PhD - McGill University

Valliappan Chidambaram Adaikkappan

PhD - McGill University

Wesley Chung

PhD - McGill University

Co-supervisor :

Doina Precup

Farnoosh Faraji

PhD - McGill University

Co-supervisor :

PhD - McGill University

Principal supervisor :

Doina Precup

Anas Houssaini

Master's Research - McGill University

Co-supervisor :

Hsiu-Chin Lin

Zina Kamel

Master's Research - McGill University

Co-supervisor :

Hsiu-Chin Lin

Melissa Mozifian

PhD - McGill University

Principal supervisor :

Joelle Pineau

Louis Petit

Postdoctorate - McGill University

Website

Sahand Rezaei-Shoshtari

PhD - McGill University

Principal supervisor :

PhD - McGill University

Jean-François Tremblay

PhD - McGill University

Steven Wang

Master's Research - McGill University

Harley Wiltzer

PhD - McGill University

Co-supervisor :

Marc Gendron-Bellemare

Master's Research - McGill University

Publications

Hypernetworks for Zero-shot Transfer in Reinforcement Learning

Sahand Rezaei-Shoshtari

Charlotte Morissette

Francois Hogan

Gregory Dudek

In this paper, hypernetworks are trained to generate behaviors across a range of unseen task conditions, via a novel TD-based training objec… (see more)tive and data from a set of near-optimal RL solutions for training tasks. This work relates to meta RL, contextual RL, and transfer learning, with a particular focus on zero-shot performance at test time, enabled by knowledge of the task parameters (also known as context). Our technical approach is based upon viewing each RL algorithm as a mapping from the MDP specifics to the near-optimal value function and policy and seek to approximate it with a hypernetwork that can generate near-optimal value functions and policies, given the parameters of the MDP. We show that, under certain conditions, this mapping can be considered as a supervised learning problem. We empirically evaluate the effectiveness of our method for zero-shot transfer to new reward and transition dynamics on a series of continuous control tasks from DeepMind Control Suite. Our method demonstrates significant improvements over baselines from multitask and meta RL approaches.

2023-06-26

Proceedings of the AAAI Conference on Artificial Intelligence (published)

arxiv.org

ANSEL Photobot: A Robot Event Photographer with Semantic Intelligence

Dmitriy Rivkin

Gregory Dudek

Nikhil Kakodkar

Oliver Limoyo

Xue (Steve) Liu

Francois Hogan

Our work examines the way in which large language models can be used for robotic planning and sampling in the context of automated photograp… (see more)hic documentation. Specifically, we illustrate how to produce a photo-taking robot with an exceptional level of semantic awareness by leveraging recent advances in general purpose language (LM) and vision-language (VLM) models. Given a high-level description of an event we use an LM to generate a natural-language list of photo descriptions that one would expect a photographer to capture at the event. We then use a VLM to identify the best matches to these descriptions in the robot's video stream. The photo portfolios generated by our method are consistently rated as more appropriate to the event by human evaluators than those generated by existing methods.

2023-06-02

2023 IEEE International Conference on Robotics and Automation (ICRA) (published)

arxiv.org

Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling

Lucas Berry

2023-02-02

ArXiv (preprint)

arxiv.org

Learning active tactile perception through belief-space control

Jean-François Tremblay

Johanna Hansen

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… (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.

2022-05-12

ICRA.org/2022/Workshop/Contact-Rich (poster)