School of Computer Science Colloquium Announcement by Dr. Animesh Garg: “Building Blocks of Generalizable Autonomy”

 

Date: Friday February 4, 2022 

Time: 11:00am-12:00pm 

Meeting URL:    https://us06web.zoom.us/j/85226540929?from=addon 

Passcode: If interested in attending this event, contact the Graduate Secretary at csgradinfo@uwindsor.ca with sufficient notice before the event to obtain the passcode.

                                                                    

My approach to Generalizable Autonomy posits that interactive learning across families of tasks is essential for discovering efficient representation and inference mechanisms. Arguably, a cognitive concept or a dexterous skill should be reusable across task instances to avoid constant relearning. It is insufficient to learn to “open a door”, and then have to re-learn it for a new door, or even windows & cupboards. Thus, I focus on three key questions:  (1) Representational biases for embodied reasoning, (2) Causal Inference in abstract sequential domains,  and (3) Interactive Policy Learning under uncertainty. In this talk, I will first through example lay bare the need for structured biases in modern RL algorithms in the context of robotics. This will span state, actions, learning mechanisms, and network architectures. Secondly, we will talk about the discovery of latent causal structure in dynamics for planning. Finally, I will demonstrate how large-scale data generation combined with insights from structure learning can enable sample efficient algorithms for practical systems. In this talk, I will focus mainly on manipulation, but my work has been applied to surgical robotics and legged locomotion as well. 

Animesh Garg is a CIFAR Chair Assistant Professor of Computer Science and Mechanical Engineering (courtesy) at the University of Toronto, a Faculty Member at the Vector Institute and UofT Robotics Institute, where he leads the Toronto People, AI, and Robotics (PAIR) research group. Animesh is also a Senior Researcher at Nvidia Research. His research focuses on machine learning algorithms for perception and control in robotics. His work aims to build Generalizable Autonomy in robotics which involves a confluence of representations and algorithms for reinforcement learning, control, and perception. His work has received AAAI New Faculty Highlight and Best Paper Recognitions at top tier venues in Machine Learning and Robotics such as ICRA, IROS, RSS, Hamlyn Symposium, Workshops at NeurIPS, ICML, and has been widely covered in the press New York Times, Nature, Wired, IEEE Spectrum.