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Gregory Dudek

Associate Academic Member
Full Professor and Research Director of Mobile Robotics Lab, McGill University, School of Computer Science
Vice President and Lab Head of AI Research, Samsung AI Center in Montréal
Google Scholar

Biography

Gregory Dudek is a full professor at McGill University’s CIM which is linked to the School of Computer Science and Research Director of Mobile Robotics Lab. He is also the Lab Director and VP of research at Samsung AI Center Montreal and an Associate academic member at Mila - Quebec Institute of Artificial Intelligence.

Dudek has authored and co-authored over 300 research publications on a wide range of subjects, including visual object description, recognition, RF localization, robotic navigation and mapping, distributed system design, 5G telecommunications and biological perception.

He co-authored the book “Computational Principles of Mobile Robotics” (Cambridge University Press) with Michael Jenkin. He has chaired and been involved in numerous national and international conferences and professional activities concerned with robotics, machine sensing and computer vision.

Dudek’s research interests include perception for mobile robotics, navigation and position estimation, environment and shape modelling, computational vision and collaborative filtering.

Current Students

Farnoosh Faraji
PhD - McGill University
Principal supervisor :
David Meger
Github
Google Scholar
Steve Wen
Master's Research - McGill University
Principal supervisor :
Doina Precup

Publications

Tactile Modality Fusion for Vision-Language-Action Models
Charlotte Morissette
Amin Abyaneh
Wei-Di Chang
Anas Houssaini
David Meger
Hsiu-Chin Lin
Jonathan Tremblay
Gregory Dudek
We propose TacFiLM, a lightweight modality-fusion approach that integrates visual-tactile signals into vision-language-action (VLA) models. … (see more)While recent advances in VLA models have introduced robot policies that are both generalizable and semantically grounded, these models mainly rely on vision-based perception. Vision alone, however, cannot capture the complex interaction dynamics that occur during contact-rich manipulation, including contact forces, surface friction, compliance, and shear. While recent attempts to integrate tactile signals into VLA models often increase complexity through token concatenation or large-scale pretraining, the heavy computational demands of behavioural models necessitate more lightweight fusion strategies. To address these challenges, TacFiLM outlines a post-training finetuning approach that conditions intermediate visual features on pretrained tactile representations using feature-wise linear modulation (FiLM). Experimental results on insertion tasks demonstrate consistent improvements in success rate, direct insertion performance, completion time, and force stability across both in-distribution and out-of-distribution tasks. Together, these results support our method as an effective approach to integrating tactile signals into VLA models, improving contact-rich manipulation behaviours.
2026-03-14
arXiv (preprint)
doi.org
arxiv.org
MANSION: Multi-floor lANguage-to-3D Scene generatIOn for loNg-horizon tasks
Lirong Che
Shuo Wen
Shan Huang
Chuang Wang
Yuzhe Yang
Gregory Dudek
Xueqian Wang
Jian Su
Real-world robotic tasks are long-horizon and often span multiple floors, demanding rich spatial reasoning. However, existing embodied bench… (see more)marks are largely confined to single-floor in-house environments, failing to reflect the complexity of real-world tasks. We introduce MANSION, the first language-driven framework for generating building-scale, multi-floor 3D environments. Being aware of vertical structural constraints, MANSION generates realistic, navigable whole-building structures with diverse, human-friendly scenes, enabling the development and evaluation of cross-floor long-horizon tasks. Building on this framework, we release MansionWorld, a dataset of over 1,000 diverse buildings ranging from hospitals to offices, alongside a Task-Semantic Scene Editing Agent that customizes these environments using open-vocabulary commands to meet specific user needs. Benchmarking reveals that state-of-the-art agents degrade sharply in our settings, establishing MANSION as a critical testbed for the next generation of spatial reasoning and planning.
2026-03-11
arXiv (preprint)
doi.org
arxiv.org
AMP2026: A Multi-Platform Marine Robotics Dataset for Tracking and Mapping
Edwin Meriaux
Shuo Wen
David Widhalm
Zhizun Wang
Junming Shi
Mariana Sosa Guzmán
Kalvik Jakkala
Bennett Carley
Elias Sokolova
Yogesh Girdhar
Monika Roznere
Jason O’Kane
Junaed Sattar
Gregory Dudek
Marine environments present significant challenges for perception and autonomy due to dynamic surfaces, limited visibility, and complex inte… (see more)ractions between aerial, surface, and submerged sensing modalities. This paper introduces the Aerial Marine Perception Dataset (AMP2026), a multi-platform marine robotics dataset collected across multiple field deployments designed to support research in two primary areas: multi-view tracking and marine environment mapping. The dataset includes synchronized data from aerial drones, boat-mounted cameras, and submerged robotic platforms, along with associated localization and telemetry information. The goal of this work is to provide a publicly available dataset enabling research in marine perception and multi-robot observation scenarios. This paper describes the data collection methodology, sensor configurations, dataset organization, and intended research tasks supported by the dataset.
2026-03-03
arXiv (preprint)
doi.org
arxiv.org
Contractive Diffusion Policies
Amin Abyaneh
Charlotte Morissette
Mohamad H. Danesh
Anas Houssaini
David Meger
Gregory Dudek
Hsiu-Chin Lin
Diffusion policies have emerged as powerful generative models for offline policy learning, whose sampling process can be rigorously characte… (see more)rized by a score function guiding a Stochastic Differential Equation (SDE). However, the same score-based SDE modeling that grants diffusion policies the flexibility to learn diverse behavior also incurs solver and score-matching errors, large data requirements, and inconsistencies in action generation. While less critical in image generation, these inaccuracies compound and lead to failure in continuous control settings. We introduce **C**ontractive **D**iffusion **P**olicies (CDPs) to induce contractive behavior in the diffusion sampling dynamics. Contraction pulls nearby flows closer to enhance robustness against solver and score-matching errors while reducing unwanted action variance. We develop an in-depth theoretical analysis along with a practical implementation recipe to incorporate CDPs into existing diffusion policy architectures with minimal modification and computational cost. We evaluate CDPs for offline learning by conducting extensive experiments in simulation and real world settings. Across benchmarks, CDPs often outperform baseline policies, with pronounced benefits under data scarcity. Project page: https://contractive-diffusion.github.io
2026-01-25
International Conference on Learning Representations (poster)
openreview.net
openreview.net
Contractive Diffusion Policies: Robust Action Diffusion via Contractive Score-Based Sampling with Differential Equations
Amin Abyaneh
Charlotte Morissette
Mohamad H. Danesh
Anas El Houssaini
David Meger
Gregory Dudek
Hsiu-Chin Lin
Diffusion policies have emerged as powerful generative models for offline policy learning, whose sampling process can be rigorously characte… (see more)rized by a score function guiding a Stochastic Differential Equation (SDE). However, the same score-based SDE modeling that grants diffusion policies the flexibility to learn diverse behavior also incurs solver and score-matching errors, large data requirements, and inconsistencies in action generation. While less critical in image generation, these inaccuracies compound and lead to failure in continuous control settings. We introduce Contractive Diffusion Policies (CDPs) to induce contractive behavior in the diffusion sampling dynamics. Contraction pulls nearby flows closer to enhance robustness against solver and score-matching errors while reducing unwanted action variance. We develop an in-depth theoretical analysis along with a practical implementation recipe to incorporate CDPs into existing diffusion policy architectures with minimal modification and computational cost. We evaluate CDPs for offline learning by conducting extensive experiments in simulation and real-world settings. Across benchmarks, CDPs often outperform baseline policies, with pronounced benefits under data scarcity.
2025-12-31
arXiv (preprint)
doi.org
openreview.net
MANSION: Multi-floor lANguage-to-3D Scene generatIOn for loNg-horizon tasks
Lirong Che
Shuo Wen
Shan Huang
Chuang Wang
Yuzhe Yang
Gregory Dudek
Xueqian Wang
Jian Su
Real-world robotic tasks are long-horizon and often span multiple floors, requiring complex spatial reasoning. Existing embodied benchmarks,… (see more) however, are largely confined to single-floor homes, failing to evaluate agents on realistic, building-scale tasks. We introduce MANSION, a language-driven framework for generating building-scale, multi-floor 3D environments for long-horizon tasks. Using this framework, we release MansionWorld, a large-scale dataset featuring over 1,000 diverse, non-residential buildings. These environments support cross-floor skills and long-horizon task generation on reusable building layouts. Experiments show that current methods degrade sharply on our multi-floor tasks, highlighting both the challenge and the value of this setting for advancing embodied AI.
2025-12-31
IEEE/CVF Conference on Computer Vision and Pattern Recognition (Accept (Poster))
doi.org
arxiv.org
The Surprising Difficulty of Search in Model-Based Reinforcement Learning
Wei-Di Chang
Mikael Henaff
Brandon Amos
Gregory Dudek
Scott Fujimoto
This paper investigates search in model-based reinforcement learning (RL). Conventional wisdom holds that long-term predictions and compound… (see more)ing errors are the primary obstacles for model-based RL. We challenge this view, showing that search is not a plug-and-play replacement for a learned policy. Surprisingly, we find that search can harm performance even when the model is highly accurate. Instead, we show that mitigating distribution shift matters more than improving model or value function accuracy. Building on this insight, we identify key techniques for enabling effective search, achieving state-of-the-art performance across multiple popular benchmark domains.
2025-12-31
arXiv (preprint)
doi.org
arxiv.org
On Mobile Ad Hoc Networks for Coverage of Partially Observable Worlds
Edwin Meriaux
Shuo Wen
Louis-Roy Langevin
Doina Precup
Antonio Lor'ia
Gregory Dudek
2025-11-30
arXiv (published)
doi.org
arxiv.org
Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning
Andrew Holliday
Ahmed El-Geneidy
Gregory Dudek
2025-10-16
Transportmetrica B: Transport Dynamics (published)
doi.org
arxiv.org
Large Pre-Trained Models for Bimanual Manipulation in 3D
Hanna Yurchyk
Wei-Di Chang
Gregory Dudek
David Meger
2025-09-29
2025 IEEE-RAS 24th International Conference on Humanoid Robots (Humanoids) (published)
doi.org
arxiv.org
Generalizable Imitation Learning Through Pre-Trained Representations
Wei-Di Chang
Francois Hogan
David Meger
Scott Fujimoto
Gregory Dudek
In this paper we leverage self-supervised vision transformer models and their emergent semantic abilities to improve the generalization abil… (see more)ities of imitation learning policies. We introduce BC-ViT, an imitation learning algorithm that leverages rich DINO pre-trained Visual Transformer (ViT) patch-level embeddings to obtain better generalization when learning through demonstrations. Our learner sees the world by clustering appearance features into semantic concepts, forming stable keypoints that generalize across a wide range of appearance variations and object types. We show that this representation enables generalized behaviour by evaluating imitation learning across a diverse dataset of object manipulation tasks. Our method, data and evaluation approach are made available to facilitate further study of generalization in Imitation Learners.
2025-05-18
2025 IEEE International Conference on Robotics and Automation (ICRA) (published)
doi.org
arxiv.org
AIoT Smart Home via Autonomous LLM Agents
Dmitriy Rivkin
Francois Hogan
Amal Feriani
Abhisek Konar
Adam Sigal
Xue Liu
Gregory Dudek
2025-01-31
IEEE Internet of Things Journal (published)
doi.org