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Spring 2022 GRASP SFI: Ted Xiao, Robotics at Google, “A Panorama of End-to-end Robot Learning”
March 2, 2022 at 3:00 PM - 4:00 PM
In recent years, a variety of approaches to robotic control have gained popularity as robots play an increasingly larger role in our everyday lives. In this talk I will give an introduction to modern-day robot learning, covering engineering and research challenges. After establishing the foundations of popular methods, I will present why I believe that end-to-end Machine Learning is the scalable approach for solving robotics problems. Finally, I will talk about some interesting open problems and how data-driven methods can learn efficiently in the real world on a large set of tasks with less supervision.
Ted Xiao
University of California Berkeley
Ted (he/him) is a research engineer at Robotics at Google where he focuses on deep reinforcement learning. Before working at Google, Ted received his BS and MS in EECS at UC Berkeley, where he worked in the Hybrid Systems Lab with Claire Tomlin and Sergey Levine. Ted’s main research interests are reinforcement learning, multitask learning, and representation learning.