Publications

3D Foundation Model-Based Loop Closing for Decentralized Collaborative SLAM
Pierre-Yves Lajoie
Benjamin Ramtoula
Daniele De Martini
Giovanni Beltrame
Decentralized Collaborative Simultaneous Localization and Mapping (C-SLAM) techniques often struggle to identify map overlaps due to signifi… (see more)cant viewpoint variations among robots. Motivated by recent advancements in 3D foundation models, which can register images despite large viewpoint differences, we propose a robust loop closing approach that leverages these models to establish inter-robot measurements. In contrast to resource-intensive methods requiring full 3D reconstruction within a centralized map, our approach integrates foundation models into existing SLAM pipelines, yielding scalable and robust multi-robot mapping. Our contributions include: 1) integrating 3D foundation models to reliably estimate relative poses from monocular image pairs within decentralized C-SLAM; 2) introducing robust outlier mitigation techniques critical to the use of these relative poses and 3) developing specialized pose graph optimization formulations that efficiently resolve scale ambiguities. We evaluate our method against state-of-the-art approaches, demonstrating improvements in localization and mapping accuracy, alongside significant gains in computational and memory efficiency. These results highlight the potential of our approach for deployment in large-scale multi-robot scenarios.
2025-11-01
IEEE Robotics and Automation Letters (published)
doi.org
The role of Large Language Models in IoT security: A systematic review of advances, challenges, and opportunities
Saeid Jamshidi
Negar Shahabi
Amin Nikanjam
Kawser Wazed Nafi
Foutse Khomh
Carol Fung
2025-11-01
Internet of Things (published)
doi.org
Asymmetric Proximal Policy Optimization: mini-critics boost LLM reasoning
Jiashun Liu
Johan S. Obando-Ceron
Han Lu
Yancheng He
Weixun Wang
Wenbo Su
Bo Zheng
Pablo Samuel Castro
Aaron Courville
Ling Pan
Most recent RL for LLMs (RL4LLM) methods avoid explicit critics, replacing them with average advantage baselines. This shift is largely prag… (see more)matic: conventional value functions are computationally expensive to train at LLM scale and often fail under sparse rewards and long reasoning horizons. We revisit this bottleneck from an architectural perspective and introduce Asymmetric Proximal Policy Optimization (AsyPPO), a simple and scalable framework that restores the critics role while remaining efficient in large-model settings. AsyPPO employs a set of lightweight mini-critics, each trained on disjoint prompt shards. This design encourages diversity while preserving calibration, reducing value-estimation bias. Beyond robust estimation, AsyPPO leverages inter-critic uncertainty to refine the policy update: (i) masking advantages in states where critics agree and gradients add little learning signal, and (ii) filtering high-divergence states from entropy regularization, suppressing spurious exploration. After training on open-source data with only 5,000 samples, AsyPPO consistently improves learning stability and performance across multiple benchmarks over strong baselines, such as GRPO, achieving performance gains of more than six percent on Qwen3-4b-Base and about three percent on Qwen3-8b-Base and Qwen3-14b-Base over classic PPO, without additional tricks. These results highlight the importance of architectural innovations for scalable, efficient algorithms.
2025-10-02
ArXiv (preprint)
arxiv.org
arxiv.org
Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study
Lena Podina
Christina Humer
Alexandre AGM Duval
Victor Schmidt
Ali Ramlaoui
Shahana Chatterjee
Yoshua Bengio
Alex Hernandez-Garcia
David Rolnick
Félix Therrien
Efficient and inexpensive energy storage is essential for accelerating the adoption of renewable energy and ensuring a stable supply, despit… (see more)e fluctuations in sources such as wind and solar. Electrocatalysts play a key role in hydrogen energy storage (HES), allowing the energy to be stored as hydrogen. However, the development of affordable and high-performance catalysts for this process remains a significant challenge. We introduce Catalyst GFlowNet, a generative model that leverages machine learning-based predictors of formation and adsorption energy to design crystal surfaces that act as efficient catalysts. We demonstrate the performance of the model through a proof-of-concept application to the hydrogen evolution reaction, a key reaction in HES, for which we successfully identified platinum as the most efficient known catalyst. In future work, we aim to extend this approach to the oxygen evolution reaction, where current optimal catalysts are expensive metal oxides, and open the search space to discover new materials. This generative modeling framework offers a promising pathway for accelerating the search for novel and efficient catalysts.
2025-10-02
ArXiv (preprint)
arxiv.org
arxiv.org
Genetic contribution to asthma informs acute chest syndrome pathophysiology and risk stratification
Sara El Aouhel
Vanessa Bellegarde
Stennio Da
Silva Faria
Tristan St-Laurent
Estelle Lecluze
Anne-Laure Pham Hung d’Alexandry d’Orengiani
F. Galactéros
Pablo Bartolucci
Marc-André Legault
Guillaume Lettre
Thomas Pincez
2025-10-02
medRxiv (preprint)
doi.org
GRACE: A Language Model Framework for Explainable Inverse Reinforcement Learning
Silvia Sapora
(Rex) Devon Hjelm
Alexander T Toshev
Omar Attia
Bogdan Mazoure
Inverse Reinforcement Learning aims to recover reward models from expert demonstrations, but traditional methods yield"black-box"models that… (see more) are difficult to interpret and debug. In this work, we introduce GRACE (Generating Rewards As CodE), a method for using Large Language Models within an evolutionary search to reverse-engineer an interpretable, code-based reward function directly from expert trajectories. The resulting reward function is executable code that can be inspected and verified. We empirically validate GRACE on the BabyAI and AndroidWorld benchmarks, where it efficiently learns highly accurate rewards, even in complex, multi-task settings. Further, we demonstrate that the resulting reward leads to strong policies, compared to both competitive Imitation Learning and online RL approaches with ground-truth rewards. Finally, we show that GRACE is able to build complex reward APIs in multi-task setups.
2025-10-02
ArXiv (preprint)
arxiv.org
arxiv.org
Attention-Based Multi-Agent RL for Multi-Machine Tending Using Mobile Robots
Abdalwhab Abdalwhab
Giovanni Beltrame
Samira Ebrahimi Kahou
David St-Onge
Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborat… (see more)ive robots can tackle that can also greatly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. We introduce a multi-agent multi-machine-tending learning framework using mobile robots based on multi-agent reinforcement learning (MARL) techniques, with the design of a suitable observation and reward. Moreover, we integrate an attention-based encoding mechanism into the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine-tending scenarios. Our model (AB-MAPPO) outperforms MAPPO in this new challenging scenario in terms of task success, safety, and resource utilization. Furthermore, we provided an extensive ablation study to support our design decisions.
2025-10-01
Applied Informatics (published)
doi.org
Attention-Based Multi-Agent RL for Multi-Machine Tending Using Mobile Robots
Abdalwhab Bakheet Mohamed Abdalwhab
Giovanni Beltrame
Samira Ebrahimi Kahou
David St-Onge
2025-10-01
AI (published)
doi.org
A Blockchain Framework for Equitable and Secure Task Allocation in Robot Swarms
Hanqing Zhao
Alexandre Pacheco
Giovanni Beltrame
Xue (Steve) Liu
Marco Dorigo
Gregory Dudek
Recent studies demonstrate the potential of blockchain to enable robots in a swarm to achieve secure consensus about the environment, partic… (see more)ularly when robots are homogeneous and perform identical tasks. Typically, robots receive rewards for their contributions to consensus achievement, but no studies have yet targeted heterogeneous swarms, in which the robots have distinct physical capabilities suited to different tasks. We present a novel framework that leverages domain knowledge to decompose the swarm mission into a hierarchy of tasks within smart contracts. This allows the robots to reach a consensus about both the environment and the action plan, allocating tasks among robots with diverse capabilities to improve their performance while maintaining security against faults and malicious behaviors. We refer to this concept as equitable and secure task allocation. Validated in Simultaneous Localization and Mapping missions, our approach not only achieves equitable task allocation among robots with varying capabilities, improving mapping accuracy and efficiency, but also shows resilience against malicious attacks.
2025-10-01
IEEE Robotics and Automation Letters (published)
doi.org
Current landscape of clinical genetics knowledge and attitudes among Non-Geneticist Physicians - the McGill genetics education survey (McGES).
Sarah Abdullah-Maklan
Yannis Trakadis
2025-10-01
Journal of Community Genetics (published)
doi.org
Current landscape of clinical genetics knowledge and attitudes among Non-Geneticist Physicians - the McGill genetics education survey (McGES)
Sarah Abdullah-Maklan
Yannis Trakadis
2025-10-01
Journal of Community Genetics (published)
doi.org
Equivariant Geometric Scattering Networks via Vector Diffusion Wavelets
David R. Johnson
Rishabh Anand
Smita Krishnaswamy
Michael Perlmutter
We introduce a novel version of the geometric scattering transform for geometric graphs containing scalar and vector node features. This new… (see more) scattering transform has desirable symmetries with respect to rigid-body roto-translations (i.e.,
2025-10-01
ArXiv (preprint)
arxiv.org
arxiv.org