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About

Zhaoqin Peng is a robotics researcher whose work spans humanoid robot motion design, biped locomotion, and wearable sensing systems. With a career rooted in the challenge of bridging human movement and robotic replication, Peng has made significant contributions to the field of motion capture adaptation for humanoid robots. His early landmark papers from 2005—accumulating nearly 90 citations combined—addressed the fundamental problem of translating captured human kinematics and dynamics into feasible humanoid motion while preserving movement similarity, a challenge central to natural and functional robot behavior. Peng's work on online walking pattern generation for humanoid platforms, including the BHR-2 robot, demonstrated practical advances in real-time locomotion control using zero-moment point (ZMP) methods. Later research expanded into human-centered sensing technologies, including pressure-sensing shoes and machine learning techniques such as KPCA and SVM for walking pattern recognition and fall detection—work with direct implications for rehabilitation robotics and exoskeleton safety. His investigations into lower-limb exoskeleton stability control further reflect a sustained commitment to assistive robotics. Across more than a decade of research, Peng has consistently worked at the intersection of biomechanics, motion intelligence, and robotic system design, contributing tools and methodologies that inform both humanoid development and human-assistive technologies.