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Inspiring the development of artificial intelligence for the benefit of all 

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Located in the heart of Quebec’s AI ecosystem, Mila is a community of more than 1,200 researchers specializing in machine learning and dedicated to scientific excellence and innovation.

About

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News

22 May 2025
Alfred partners with Mila to accelerate AI innovation in the food and beverage sector
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20 May 2025
Seven Sustainability Scholarships Awarded to Emerging Researchers in AI and Climate
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01 May 2025
Follow Mila researchers at AISTATS 2025
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Faculty 

Founded in 1993 by Professor Yoshua Bengio, Mila today brings together over 140 professors affiliated with Université de Montréal, McGill University, Polytechnique Montréal and HEC Montréal. Mila also welcomes professors from Université Laval, Université de Sherbrooke, École de technologie supérieure (ÉTS) and Concordia University. 

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Latest Publications

GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions
Ali Imran
Giovanni Beltrame
David St-Onge
In industrial environments, predicting human actions is essential for ensuring safe and effective collaboration between humans and robots. T… (see more)his paper introduces a perception framework that enables mobile robots to understand and share information about human actions in a decentralized way. The framework first allows each robot to build a spatial graph representing its surroundings, which it then shares with other robots. This shared spatial data is combined with temporal information to track human behavior over time. A swarm-inspired decision-making process is used to ensure all robots agree on a unified interpretation of the human's actions. Results show that adding more robots and incorporating longer time sequences improve prediction accuracy. Additionally, the consensus mechanism increases system resilience, making the multi-robot setup more reliable in dynamic industrial settings.
2025-06-01
IEEE Robotics and Automation Letters (published)
doi.org
arxiv.org
STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent
Yewon Lee
Philip Huang
Yizhou Huang
Krishna Murthy
Andrew Zou Li
Fabian Damken
Eric Heiden
Kevin A. Smith
Derek Nowrouzezahrai
Fabio Ramos
Florian Shkurti
Carnegie-mellon University
M. I. O. Technology
Technische Universitat Darmstadt
Nvidia
M. University
University of Sydney
Planning for many manipulation tasks, such as using tools or assembling parts, often requires both symbolic and geometric reasoning. Task an… (see more)d Motion Planning (TAMP) algorithms typically solve these problems by conducting a tree search over high-level task sequences while checking for kinematic and dynamic feasibility. While performant, most existing algorithms are highly inefficient as their time complexity grows exponentially with the number of possible actions and objects. Additionally, they only find a single solution to problems in which many feasible plans may exist. To address these limitations, we propose a novel algorithm called Stein Task and Motion Planning (STAMP) that leverages parallelization and differentiable simulation to efficiently search for multiple diverse plans. STAMP relaxes discrete-and-continuous TAMP problems into continuous optimization problems that can be solved using variational inference. Our algorithm builds upon Stein Variational Gradient Descent, a gradient-based variational inference algorithm, and parallelized differentiable physics simulators on the GPU to efficiently obtain gradients for inference. Further, we employ imitation learning to introduce action abstractions that reduce the inference problem to lower dimensions. We demonstrate our method on two TAMP problems and empirically show that STAMP is able to: 1) produce multiple diverse plans in parallel; and 2) search for plans more efficiently compared to existing TAMP baselines.
2025-06-01
IEEE Robotics and Automation Letters (published)
doi.org
openreview.net
Dimension-adapted Momentum Outscales SGD
Damien Ferbach
Katie Everett
Gauthier Gidel
Elliot Paquette
Courtney Paquette
2025-05-22
ArXiv (preprint)
arxiv.org
arxiv.org
Structure-Aligned Protein Language Model
Can Chen
David Heurtel-Depeiges
Robert M. Vernon
Christopher J. Langmead
Yoshua Bengio
Quentin Fournier
2025-05-22
ArXiv (preprint)
arxiv.org
arxiv.org
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AI for Humanity

Socially responsible and beneficial development of AI is a fundamental component of Mila’s mission. As a leader in the field, we wish to contribute to social dialogue and the development of applications that will benefit society.

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