Liyiming Ke

Research 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 handle fine manipulation challenges autonomously, 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.

Bio:

Liyiming Ke is a final-year PhD candidate at the University of Washington advised by Sidd Srinivasa. Her research is dedicated to push the boundaries of fine motor skills of robotic hardware systems. Her research includes imitation learning and reinforcement learning, expanding the theoretical boundaries of learning algorithms and building real-world robotic systems. She has developed a low-cost chopsticks robot platform capable of fine manipulation and grasping in dynamic environments. She has led human-robot interactive demo at AAAS gathering and has conducted research internships at Facebook AI Research and Microsoft Research.