gradslam is available as an open-source pytorch framework here
We are organizing a (virtual) seminar series on robot learning at Mila and the Robotics and Embodied AI Lab. Visit our seminar page for more.
We are organizing a (virtual) workshop at Neurips 2020 on differentiable computer vision, graphics, and physics in ML, with a star-studded speaker lineup! Visit our workshop webpage for more.
Honoured to be part of the RSS pioneers cohort of 2020 announced, with 27 other amazing grad students and postdocs. Check out a short video abstract of my near-term research.
Honoured to be announced best paper of RAL, 2019, at the awards ceremony at ICRA 2020.
Our paper on fully differentiable dense SLAM will be (virtually) presented at ICRA 2020
Our paper Monolayout, tackling amodal scene layout estimation from a single image, was presented at WACV 2020. For more details, visit our GitHub repo, or watch this video abstract.
Our paper MapLite, tackling autonomous rural intersection navigation without a detailed prior map, is accepted for publication in Robotics and Automation Letters. It will also be presented at the International Conference on Robotics and Automation (ICRA) 2020.
During a summer internship at NVIDIA, Toronto, I worked with Prof. Sanja Fidler’s group to build Kaolin: a 3D deep learning framework for PyTorch users.