LMSS @ Cornell Tech: Abhishek Gupta (University of Washington)
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Cornell Tech, Bloomberg 091, New York
Learning Machines Seminar Series What: LMSS: Abhishek Gupta (University of Washington) When: Monday, November 2, 12:00 p.m. - 1:00 p.m. Where: Bloomberg 091 The series is organized by Associate Professor Yoav Artzi and sponsored by Bloomberg. Pizza will be served at 11:45 a.m. "Simulation-driven Robot Learning for the Lazy Researcher" This talk will delve into the workflow of building robot learning methods powered by large-scale physics simulation. While the promise of simulation is low-effort data generation that scales cheaply with human effort, the reality is often not as simple, with considerable human effort required to actually make simulation a useful tool for real-world robotic manipulation. In this talk, I will study how to develop methods that explicitly reduce manual human effort in enabling simulation as a data generation tool - from behavior generation, to model selection, to enabling downstream transfer to the real-world. In doing so, I will outline an effort-efficient path to leveraging simulation as a data source for robot learning - hopefully to make robotics easier for lazy researchers like me! BIO Abhishek Gupta is an assistant professor in computer science and engineering at the Paul G. Allen School of Computer Science and Engineering at the University of Washington since 2022. He leads the Washington Embodied Intelligence and Robotics Development lab focusing on robot learning and reinforcement learning. Previously, he was a postdoctoral scholar at MIT, collaborating with Russ Tedrake and Pulkit Agrawal. Prior to that he received his Ph.D. and B.S degrees from UC Berkeley, working with Sergey Levine and Pieter Abbeel. Abhishek is the recipient of IEEE RAS Early Career Award, Toyota Research Institute Young Investigator award and an Amazon Science Hub award, along with award nominations at several top conferences and workshops. His research interests lie in scalable reinforcement learning methods for robot learning, in particular methods for continual adaptation in the real world and what role simulation can play in this process.