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About

Weiyong Si is a pioneering robotics researcher whose work sits at the intersection of robot learning, human-robot interaction, and intelligent manipulation. His research portfolio spans learning from demonstration, impedance control, physical human-robot interaction, and robot-assisted medical applications — areas where he has made substantial and lasting contributions to the field. Si's most influential work includes a comprehensive review of teleoperation-based learning from demonstration (99 citations), which has become an essential reference for researchers exploring how robots acquire manipulation skills from human guidance. His investigations into impedance learning for human-guided robots (63 citations) and adaptive compliant skill learning (30 citations) address a critical challenge in robotics: enabling robots to safely and intelligently interact with unknown, dynamic environments. Complementing this, his work on physical human-robot interaction frameworks and human motion prediction advances the seamless collaboration between humans and robotic systems. Beyond manipulation, Si has contributed meaningfully to medical robotics, authoring influential surveys on autonomous robot-assisted microsurgery (36 citations) and designing teleoperation frameworks for robot-assisted sonography (29 citations). His development of stable neural energy functions for demonstration learning and composite dynamic movement primitives further reflects his commitment to theoretically grounded, practically impactful solutions. Collectively, Si's research is shaping the future of intelligent, human-collaborative robotic systems across industrial and clinical domains.