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About

Hsuan-Ming Feng is a robotics and intelligent systems researcher whose work spans motion planning, autonomous navigation, computer vision, and fuzzy control systems. Over more than a decade of prolific research, Feng has made significant contributions to the development of intelligent robotic platforms capable of operating in complex, real-world environments. Feng's most impactful contribution is a reinforcement learning-based motion planning framework for dual-arm robots using the Soft Actor-Critic algorithm, which has garnered 62 citations and addresses critical challenges such as self-collision avoidance, joint limit management, and singularity handling in 7-DOF robotic arms. Earlier foundational work on omni-directional vision-based mobile robots with fuzzy control systems (31 citations) established Feng's expertise in integrating perception and intelligent decision-making. A recurring theme throughout Feng's research is the Robot Operating System (ROS), which he has employed across humanoid pose control, autonomous map generation, path navigation, and soccer robot behavior design. His broader portfolio further demonstrates versatility in applying evolutionary algorithms like Particle Swarm Optimization and Ant Colony Optimization to robotic manipulation and embedded systems. Feng's cumulative body of work reflects a sustained commitment to bridging theoretical AI methods with practical robotic implementation.