Coming of Age of Robot Learning

MIT-IBM Watson AI Lab

Date(s) : 06/05/2022   iCal
14 h 30 min - 15 h 30 min

The questions of dexterity, agility, and learning from a few demonstrations have intrigued robotics researchers. In this talk, I will explore answers and solutions to these questions via the following case studies:

(i) a dexterous manipulation system capable of re-orienting novel objects.

(ii) a quadruped robot that is substantially more agile than its counterparts (runs, spins) on challenging natural terrains.

(iii) framework for learning task-sensitive perceptual representations for planning and out-of-distribution generalization.

(time permitting) While a lot of recent progress in robotics is driven by perception, we show that learned controllers can help address problems that were previously thought to be hard. I will discuss our findings, the insights we gained, and the road ahead.

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 has 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. He received his Ph.D. from UC Berkeley, Bachelors’s degree from IIT Kanpur, where he was awarded the Directors Gold Medal and co-founded SafelyYou Inc.



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