Title: Data-Driven Fine Manipulation
Speaker: Liyiming Ke (Allen School)
Date: Friday, March 8, 2024

Abstract: Fine manipulation, such as cutting fingernails, threading a needle, or performing delicate surgical tasks like removing clots from organs, is ubiquitous in daily life. Automating these tasks through robotic systems offers significant economic potential. Unlike existing robotic solutions that automate specific problems through dedicated systems and structures, my research aims to empower general-purpose hardware systems to automate fine manipulation challenges, without imposing additional setup complexities or requiring extensive human intervention. I have utilized data-driven approaches like imitation learning and reinforcement learning to formulate precise, robust, and adaptive policies. In scenarios where demonstrations are available, I have devised frameworks that enhance the robustness of imitation learning agents, enabling their success in fine manipulation tasks. Conversely, for scenarios where obtaining demonstrations is impractical or costly, I have introduced a training paradigm that enhances the sample efficiency of reinforcement learning agents, allowing them to develop strategies that exhibit exceptional dexterity, potentially surpassing human capabilities using 30 minutes of data. In summary, combining learning methods with structures and priors could not only reduce the human efforts during the automation process but improve the precision and robustness of the robots.

Biography: Liyiming Ke is a final-year PhD candidate at the University of Washington advised by Sidd Srinivasa. Her research is dedicated to pushing the boundaries of fine motor skills of robotic systems using data-driven approaches. She has developed theoretical frameworks for imitation learning and built a low-cost chopsticks robot platform capable of fine manipulation and grasping in dynamic environments. She has been selected as one of the Rising Stars in EECS 2023. She has conducted research internships at Facebook AI Research and Microsoft Research.

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