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Spring 2022 GRASP SFI: Youngwoon Lee, University of Southern California, “Scaling Robot Learning with Skills: Towards Furniture Assembly and Beyond”
April 27, 2022 at 3:00 PM - 4:00 PM
Despite the recent progress in robot learning, robotics research and benchmarks today are typically confined to simple short-horizon tasks. However, tasks in our daily lives are much more complicated — consisting of multiple sub-tasks and requiring high dexterity skills — and the typical “learning from scratch” scheme is hardly scale to such complex long-horizon tasks.
In this talk, I propose to extend the range of tasks that robots can learn by acquiring a useful skillset and efficiently harnessing these skills. As a first step, I will introduce a novel benchmark for complex long-horizon manipulation tasks, IKEA furniture assembly environment. Then, I will present skill chaining approaches that enable sequential skill composition to perform long-horizon tasks. Finally, I will talk about how to learn a long-horizon task efficiently using skills and skill priors extracted from diverse data.
University of Southern California
Youngwoon Lee is a fifth-year Ph.D. student at USC, advised by Joseph J. Lim. He received his B.S. and M.S. degrees in Computer Science at KAIST. His research interests are in deep reinforcement learning and imitation learning for robotics. Particularly, his research focuses on solving complex long-horizon tasks, such as furniture assembly, which requires many aspects of intelligent robots from structural reasoning to long-term planning to sophisticated control.