AI Seminar sponsored by Fortive

Animesh Garg -- Generalizable Autonomy in Robotics

Abstract: Data-driven methods in Robotics circumvent hand-tuned feature engineering, albeit lack guarantees and often incur a massive computational expense. My research aims to bridge this gap and enable generalizable imitation for robot autonomy. We need to build systems that can capture semantic task structures that promote sample efficiency and can generalize to new task instances across visual, dynamical or semantic variations. And this involves designing algorithms that unify learning with perception, control and planning. In this talk, I will show how inductive biases and priors help with Generalizable Autonomy. First I will talk about choice of action representations in RL and imitation from ensembles of suboptimal supervisors. Then I will talk about latent variable models in self-supervised learning. Finally I will talk about meta-learning for multi-task learning and data gathering in robotics.

Bio: Animesh Garg is a CIFAR AI Chair Assistant Professor at University of Toronto and Vector Institute. He is also a Senior Research Scientist at Nvidia. His research interests focus on intersection of Learning and Perception in Robot Manipulation. He works on efficient generalization in large scale imitation learning. Animesh works on the applications of robot manipulation in surgery and manufacturing as well as personal robotics. Previously, Animesh received his Ph.D. from the University of California, Berkeley and a postdoc at Stanford AI Labs. His work has won multiple best paper awards and nominations including ICRA 2019, ICRA 2015 and IROS 2019, among others and has also featured in press outlets such as New York Times, BBC, and Wired.