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About

Xiaojun Zhao is a robotics researcher whose work has made meaningful contributions to the field of humanoid robot motion design and control, with a particular focus on bridging the gap between human movement and robotic locomotion. Working primarily in the mid-2000s, Zhao pioneered techniques for adapting human motion capture data to humanoid robot kinematics, recognizing that the fundamental differences in physical structure and dynamics between humans and robots demand sophisticated mapping and similarity evaluation methods. His most influential work, "Kinematics Mapping and Similarity Evaluation of Humanoid Motion Based on Human Motion Capture" (2005), has garnered 43 citations and remains a foundational reference for researchers tackling motion retargeting challenges. Zhao also advanced practical solutions for real-time robot control, developing online trajectory generation methods that utilize key locomotion parameters — including hip positioning, step length, and walking cycle — to enable adaptive bipedal walking. His construction and analysis of human motion capture systems further supported the translation of natural human movement into viable humanoid motion design. Collectively, Zhao's contributions helped establish core methodological frameworks for humanoid locomotion that continue to inform modern robotics research.