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About

Ho Chit Siu is a robotics researcher whose work bridges human–robot collaboration, wearable assistive devices, and AI-driven autonomy. His research spans three key areas: mobile robotic assistants for manufacturing, human joint torque estimation for rehabilitation and exoskeleton control, and LLM-enabled robot autonomy. Siu’s most influential work, “Comparative performance of human and mobile robotic assistants in collaborative fetch-and-deliver tasks” (84 citations), demonstrated how robots can augment—rather than replace—human workers in automotive assembly lines. He also led the first mobile robot system designed for moving-floor assembly lines (30 citations), a practical innovation for dynamic industrial environments. In biomechanics, Siu developed neural network methods to estimate ankle torques from electromyography and accelerometry (31 citations), enabling real-time, wearable-friendly torque prediction for prosthetic and exoskeleton control. His recent work on the CLEAR platform (13 citations) integrates large language models with computer vision for prompt-engineered robot control, pushing toward rapidly deployable, context-aware autonomy. With over 190 total citations and contributions spanning industrial deployment, clinical assessment, and AI-driven robotics, Siu’s research consistently targets real-world impact—making robots more intuitive, collaborative, and responsive to human needs.