STARS Lab

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Space and Terrestrial Autonomous Robotic Systems Lab at the University of Toronto

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Welcome to the STARS Laboratory!

We envision a future in which robotic systems are pervasive, persistent, and perceptive. We carry out research at the nexus of sensing, planning, and control, with a focus on the study of fundamental problems related to perception, representation, and understanding of the world. Our goal is to build reliable, power-on-and-go machines that can operate safely over long periods of time and in challenging, dynamic environments, for example, in homes and offices, on road networks, underground, underwater, in space, and on remote planetary surfaces. Read more about our research, follow our Twitter feed and LinkedIn feed for updates about the lab, or consider joining us!
 

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STARS News

  • Olivier Lamarre’s latest preprint featured on Phys.org front page!

    Thanks to Phys.org for running a feature all about the recent preprint article by Olivier Lamarre (STARS) and Shantanu Malhotra (NASA JPL)! The article describes how to define recovery policies to ensure that a solar-powered rover remains safe (i.e., limits the risk of complete battery depletion) while travelling through permanently shadowed regions (at, e.g., the lunar south pole). The full article is under review; read the preprint on arXiv here: https://arxiv.org/abs/2307.16786

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  • Alumnus Filip Marić recognized as U of T Engineering “Grad to Watch”!

    The University of Toronto Faculty of Applied Science and Engineering has released their annual list of Grads to Watch (2023), and we’re delighted that STARS Laboratory alumnus Flip Marić is part of the cohort! Filip’s PhD work focused on geometric methods for robotic manipulation; he was a Joint Educational Placement (JEP) student, co-supervised by Ivan Petrović in the LAMoR group at the University of Zagreb. Filip is now a research scientist at the Samsung AI Centre in Montreal. The full story is available here. Congratulations Filip!

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  • CoRL’22 Pre-Training Workshop Dyson Best Paper Award for semantic segmentation work!

    Our paper by lead author Andrej Janda on self-supervised pre-training for 3D semantic segmentation won the Dyson Best Paper Award at the CoRL’22 Pre-Training Robot Learning Workshop! The paper describes an approach for pre-training 3D segmentation networks using 2D image data; our contrastive learning approach enables self-supervision and 2D-to-3D feature transfer. Better segmentation with far less (or no) manual labelling! Congratulations to Andrej, Brandon, and Edwin! The short paper is available on arXiv: https://arxiv.org/abs/2211.11801. A big thank you to the workshop organizers and to Dyson for sponsoring the workshop award!

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  • Olivier Lamarre talks rovers, Mars, and research as U of T Groundbreaker

    Congratulations to our own Olivier Lamarre for being featured in Season 2, Episode 4 of U of T’s Groundbreakers series on future innovators! Oliver talks about planetary rover navigation and prospects for discovering life beyond Earth, as well as his time at NASA JPL. The full video is available here.

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  • Multimodal Learning paper – IAS’17 Best Paper Finalist

    Our paper entitled “Learning Sequential Latent Variable Models from Multimodal Time Series Data” was a Best Paper Finalist at the 17th International Conference on Intelligent Autonomous Systems (IAS’17), held in Zagreb, Croatia, in June! The paper studies how to best combine multimodal data (vision, haptic, and proprioceptive) to improve the performance of manipulation tasks; we learn the latent dynamics of a specific task in an fully self-supervised manner. Congratulations to Oliver Limoyo and Trevor Ablett! The preprint is available on arXiv: https://arxiv.org/abs/2204.10419.

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STARS Laboratory at the University of Toronto.