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About

Baoshi Cao is a robotics researcher whose work spans manipulator design, motion planning, kinematic calibration, and human-robot interaction. Based at a leading Chinese robotics institution, Cao has made significant contributions to both the theoretical foundations and practical implementation of robotic systems, with a particular focus on space and humanoid robots. Cao's most influential work addresses vibration suppression in robot manipulators through trajectory planning methods enhanced by particle swarm optimization, earning 16 citations and establishing him as a contributor to precision motion control. His research on kinematic calibration using Extended Kalman Filter-based visual measurement systems (12 citations) has advanced pose accuracy in space manipulators, a critical concern for extraterrestrial applications. Complementing this, his analysis of cycloidal drive mechanics — accounting for clearance, deformation, and friction — provides valuable design tools for next-generation actuators (11 citations). Cao has also shaped humanoid robotics development, contributing to the HIT Humanoid Robot platform and designing a two-DOF torso system optimized for dexterous operation. His more recent work applies reinforcement learning to robotic assembly tasks and neural-network-guided path planning on uneven terrain, reflecting a growing engagement with data-driven approaches. Collectively, his publications demonstrate a researcher bridging mechanical design, control theory, and machine learning to advance capable, real-world robotic systems.