Publications

GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions

Ali Imran

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

Impact de l'antibiothérapie par Daptomycine dans le traitement des bactériémies à Enterococcus faecium en réanimation : l'étude rétrospective multicentrique ENTERODAPTO.

S. Herbel

Guillaume Dumas

L. Chantelot

J. Massol

Q. Moyon

J. Ricard

E. Azoulay

C. Hauw-Berlemont

E. Maury

T. Urbina

2025-06-01

Médecine et Maladies Infectieuses Formation (published)

doi.org

Learning What Matters: Prioritized Concept Learning via Relative Error-driven Sample Selection

Shivam Chandhok

Qian Yang

Oscar Mañas

Kanishk Jain

Leonid Sigal

Aishwarya Agrawal

Instruction tuning has been central to the success of recent vision-language models (VLMs), but it remains expensive-requiring large-scale d… (see more)atasets, high-quality annotations, and large compute budgets. We propose PRioritized cOncept learninG via Relative Error-driven Sample Selection (PROGRESS), a data- and compute-efficient framework that enables VLMs to dynamically select what to learn next based on their evolving needs during training. At each stage, the model tracks its learning progress across skills and selects the most informative samples-those it has not already mastered and that are not too difficult to learn at the current stage of training. This strategy effectively controls skill acquisition and the order in which skills are learned. Specifically, we sample from skills showing the highest learning progress, prioritizing those with the most rapid improvement. Unlike prior methods, PROGRESS requires no upfront answer annotations, queries answers only on a need basis, avoids reliance on additional supervision from auxiliary VLMs, and does not require compute-heavy gradient computations for data selection. Experiments across multiple instruction-tuning datasets of varying scales demonstrate that PROGRESS consistently outperforms state-of-the-art baselines with much less data and supervision. Additionally, we show strong cross-architecture generalization and transferability to larger models, validating PROGRESS as a scalable solution for efficient learning.

2025-06-01

ArXiv (preprint)

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

Graph Representation Learning for the Prediction of Medication Usage in the UK Biobank Based on Pharmacogenetic Variants

Bill Qi

Yannis Trakadis

2025-05-31

Bioengineering (published)

doi.org

Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn

Hongyao Tang

Johan Samir Obando Ceron

Pablo Samuel Castro

Aaron Courville

Glen Berseth

Plasticity, or the ability of an agent to adapt to new tasks, environments, or distributions, is crucial for continual learning. In this pap… (see more)er, we study the loss of plasticity in deep continual RL from the lens of churn: network output variability for out-of-batch data induced by mini-batch training. We demonstrate that (1) the loss of plasticity is accompanied by the exacerbation of churn due to the gradual rank decrease of the Neural Tangent Kernel (NTK) matrix; (2) reducing churn helps prevent rank collapse and adjusts the step size of regular RL gradients adaptively. Moreover, we introduce Continual Churn Approximated Reduction (C-CHAIN) and demonstrate it improves learning performance and outperforms baselines in a diverse range of continual learning environments on OpenAI Gym Control, ProcGen, DeepMind Control Suite, and MinAtar benchmarks.

2025-05-31

ArXiv (preprint)

arxiv.org

Understanding Behavioral Metric Learning: A Large-Scale Study on Distracting Reinforcement Learning Environments

Ziyan Luo

Tianwei Ni

Pierre-Luc Bacon

Doina Precup

Xujie Si

2025-05-31

ArXiv (preprint)

arxiv.org

Continual Learning in Vision-Language Models via Aligned Model Merging

Ghada Sokar

Gintare Karolina Dziugaite

Anurag Arnab

Ahmet Iscen

Pablo Samuel Castro

Cordelia Schmid

Continual learning is conventionally tackled through sequential fine-tuning, a process that, while enabling adaptation, inherently favors pl… (see more)asticity over the stability needed to retain prior knowledge. While existing approaches attempt to mitigate catastrophic forgetting, a bias towards recent tasks persists as they build upon this sequential nature. In this work we present a new perspective based on model merging to maintain stability while still retaining plasticity. Rather than just sequentially updating the model weights, we propose merging newly trained task parameters with previously learned ones, promoting a better balance. To maximize the effectiveness of the merging process, we propose a simple mechanism that promotes learning aligned weights with previous ones, thereby avoiding interference when merging. We evaluate this approach on large Vision-Language Models (VLMs), and demonstrate its effectiveness in reducing forgetting, increasing robustness to various task orders and similarities, and improving generalization.

2025-05-30

ArXiv (preprint)

arxiv.org

Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes

Anthony Gosselin

Ge Ya Luo

Luis Lara

Florian Golemo

Derek Nowrouzezahrai

Liam Paull

Alexia Jolicoeur-Martineau

Chris Pal

Video diffusion techniques have advanced significantly in recent years; however, they struggle to generate realistic imagery of car crashes … (see more)due to the scarcity of accident events in most driving datasets. Improving traffic safety requires realistic and controllable accident simulations. To tackle the problem, we propose Ctrl-Crash, a controllable car crash video generation model that conditions on signals such as bounding boxes, crash types, and an initial image frame. Our approach enables counterfactual scenario generation where minor variations in input can lead to dramatically different crash outcomes. To support fine-grained control at inference time, we leverage classifier-free guidance with independently tunable scales for each conditioning signal. Ctrl-Crash achieves state-of-the-art performance across quantitative video quality metrics (e.g., FVD and JEDi) and qualitative measurements based on a human-evaluation of physical realism and video quality compared to prior diffusion-based methods.

2025-05-30

ArXiv (preprint)

arxiv.org