Fun with Robots and Machine Learning
2023-02-08 00:00:00 +0000 9:00 am - 10:00 am EST
Pulkit Agrawal
Massachusetts Institute of Technology
Abstract: Robots are getting smarter at converting complex natural language commands describing household tasks into step-wise instructions. Yet, they fail to actually perform such tasks! A prominent explanation for these failures is the fragility and inability of the low-level skills (e.g., locomotion, grasping, pushing, object re-orientation, etc.) to generalize to unseen scenarios. In this talk, I will discuss a framework for learning low-level skills that surpasses limitations of current systems at tackling contact-rich tasks and is real-world-ready generalizes, runs in real-time with onboard computing, and uses commodity sensors. I will describe the framework using the following case studies. (i) a dexterous manipulation system capable of re-orienting novel objects. (ii) a quadruped robot capable of fast locomotion and manipulation on diverse natural terrains. (iii) learning from a few task demonstrations of an object manipulation task to generalize to new object instances in out-of-distribution configurations.
Bio: Pulkit is the Steven and Renee Finn Chair Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT, where he directs the Improbable AI Lab. His research interests span robotics, deep learning, computer vision, and reinforcement learning. His work received the Best Paper Award at Conference on Robot Learning 2021 and Best Student Paper Award at Conference on Computer Supported Collaborative Learning 2011. He is a recipient of the Sony Faculty Research Award, Salesforce Research Award, Amazon Research Award, a Fulbright fellowship, etc. Before joining MIT, he co-founded SafelyYou Inc., received his Ph.D. from UC Berkeley, and Bachelor's degree from IIT Kanpur, where he was awarded the Directors Gold Medal.