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About

Xiang Ma is a robotics researcher whose work focuses on the intersection of human-robot interaction, soft robotics, and intelligent motion recognition. His key research areas include developing biomimetic robotic hands, human motion recognition using machine learning, and applying reinforcement learning to musculoskeletal systems. Ma's most notable contribution is his work on an underactuated soft humanoid hand designed for hand sign language, which demonstrates how simplified mechanical designs can achieve complex human-like gestures while maintaining compliance and safety. This paper has garnered 10 citations for its innovative approach to soft robotics. He has also made significant advances in human motion recognition, employing Hidden Markov Models with hyperparameters optimized by maximum mutual information to interpret surface electromyography (sEMG) signals, enabling more intuitive human-robot collaboration. Ma's research extends to deep reinforcement learning for controlling musculoskeletal robotic arms, where he trained a pneumatic artificial muscle-driven arm to perform Tai Chi movements—a challenging task requiring precise coordination and compliance. His work addresses fundamental challenges in creating robots that can safely and effectively interact with humans, with potential applications in prosthetics, rehabilitation, and assistive technologies.