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About

Kejie Li is a robotics researcher whose work spans humanoid robotics, mobile robot systems, and autonomous navigation. His most significant contributions lie in bridging human motion capture technology with humanoid robot control — a challenging problem requiring the translation of human kinematics and dynamics into physically realizable robot motion. His 2005 papers on kinematics mapping and humanoid motion design (collectively cited nearly 90 times) established foundational methods for adapting captured human movement data to robots with differing physical constraints, advancing the naturalism and stability of humanoid locomotion. Li further contributed to humanoid systems through flexible foot design, upper-body-aware walking pattern generation, and distributed real-time control architectures, demonstrating a comprehensive systems-level approach to robot development. Beyond humanoids, Li's research extends to practical autonomous platforms, including a Swedish-wheel floor cleaning robot, a wall-climbing robot with suction analysis, and indoor obstacle detection using structured light vision — reflecting broad versatility in applied robotics. His 2011 work on fuzzy equilibrium control for a coaxial wheeled robot highlights an interest in intelligent balance control strategies. With over 275 cumulative citations across diverse robotic domains, Li's body of work represents meaningful contributions to the design, control, and human-inspired motion planning of complex robotic systems.