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Jean-François Tremblay

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Publications

Learning active tactile perception through belief-space control
Jean-François Tremblay
Johanna Hansen
David Meger
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… (voir plus)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.
2025-05-19
2025 IEEE International Conference on Robotics and Automation (ICRA) (publié)
doi.org
openreview.net
Topological mapping for traversability-aware long-range navigation in off-road terrain
Jean-François Tremblay
Julie Alhosh
Louis Petit
Faraz Lotfi
Lara Landauro
David Meger
Autonomous robots navigating in off-road terrain like forests open new opportunities for automation. While off-road navigation has been stud… (voir plus)ied, existing work often relies on clearly delineated pathways. We present a method allowing for long-range planning, exploration and low-level control in unknown off-trail forest terrain, using vision and GPS only. We represent outdoor terrain with a topological map, which is a set of panoramic snapshots connected with edges containing traversability information. A novel traversability analysis method is demonstrated, predicting the existence of a safe path towards a target in an image. Navigating between nodes is done using goal-conditioned behavior cloning, leveraging the power of a pretrained vision transformer. An exploration planner is presented, efficiently covering an unknown off-road area with unknown traversability using a frontiers-based approach. The approach is successfully deployed to autonomously explore two 400 meters squared forest sites unseen during training, in difficult conditions for navigation.
2025-05-19
2025 IEEE International Conference on Robotics and Automation (ICRA) (publié)
doi.org
arxiv.org
Interacting with a Visuotactile Countertop
M. Jenkin
Francois Hogan
Kaleem Siddiqi
Jean-François Tremblay
Bobak H. Baghi
Gregory Dudek
2024-01-01
International Conference on Robotics, Computer Vision and Intelligent Systems (publié)
doi.org
Multimodal dynamics modeling for off-road autonomous vehicles
Jean-François Tremblay
Travis Manderson
Aurélio Noca
Gregory Dudek
David Meger
Dynamics modeling in outdoor and unstructured environments is difficult because different elements in the environment interact with the robo… (voir plus)t in ways that can be hard to predict. Leveraging multiple sensors to perceive maximal information about the robot’s environment is thus crucial when building a model to perform predictions about the robot’s dynamics with the goal of doing motion planning. We design a model capable of long-horizon motion predictions, leveraging vision, lidar and proprioception, which is robust to arbitrarily missing modalities at test time. We demonstrate in simulation that our model is able to leverage vision to predict traction changes. We then test our model using a real-world challenging dataset of a robot navigating through a forest, performing predictions in trajectories unseen during training. We try different modality combinations at test time and show that, while our model performs best when all modalities are present, it is still able to perform better than the baseline even when receiving only raw vision input and no proprioception, as well as when only receiving proprioception. Overall, our study demonstrates the importance of leveraging multiple sensors when doing dynamics modeling in outdoor conditions.
2021-06-05
2021 IEEE International Conference on Robotics and Automation (ICRA) (publié)
doi.org
arxiv.org
Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of Obstacles
Travis Manderson
Juan Higuera
Stefan Wapnick
Jean-François Tremblay
Florian Shkurti
David Meger
Gregory Dudek
We present Nav2Goal, a data-efficient and end-to-end learning method for goal-conditioned visual navigation. Our technique is used to train … (voir plus)a navigation policy that enables a robot to navigate close to sparse geographic waypoints provided by a user without any prior map, all while avoiding obstacles and choosing paths that cover user-informed regions of interest. Our approach is based on recent advances in conditional imitation learning. General-purpose, safe and informative actions are demonstrated by a human expert. The learned policy is subsequently extended to be goal-conditioned by training with hindsight relabelling, guided by the robot's relative localization system, which requires no additional manual annotation. We deployed our method on an underwater vehicle in the open ocean to collect scientifically relevant data of coral reefs, which allowed our robot to operate safely and autonomously, even at very close proximity to the coral. Our field deployments have demonstrated over a kilometer of autonomous visual navigation, where the robot reaches on the order of 40 waypoints, while collecting scientifically relevant data. This is done while travelling within 0.5 m altitude from sensitive corals and exhibiting significant learned agility to overcome turbulent ocean conditions and to actively avoid collisions.
2020-07-12
Robotics: Science and Systems XVI (publié)
doi.org
arxiv.org