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About

Chuang Cheng is a robotics researcher whose work sits at the intersection of medical robotics, manipulator control, and embodied intelligence. His primary research areas include dual-arm robot trajectory planning, stability control for mobile manipulators in unstructured environments, and humanoid manipulation. Cheng’s most cited work, "Dual-Arm Robot Trajectory Planning Based on Deep Reinforcement Learning under Complex Environment" (2022, 30 citations), tackles the critical challenge of enabling robots to safely approach patients in complex clinical settings by using deep reinforcement learning to avoid collisions with human bodies and hospital beds. He has also made significant contributions to stability control, developing active disturbance rejection control methods to keep end effectors stable on uneven terrain—a vital capability for rescue robots operating in disaster zones or battlefields. His more recent work explores humanoid manipulation, including admittance control for steering wheel operation and biomimetic grasping through multimodal imitation learning. With a growing body of work that spans from foundational stability theory to cutting-edge reinforcement learning applications, Cheng is helping to bridge the gap between laboratory robotics and real-world deployment in healthcare and emergency response.