Papers
4
Total Citations
47
H-Index
4
About
Zeyuan Huang is a robotics researcher whose work sits at the intersection of motion planning, obstacle avoidance, and intelligent control for robotic manipulators. His research focuses primarily on applying reinforcement learning and advanced planning algorithms to solve complex challenges in robot autonomy, with particular emphasis on redundant manipulators operating in dynamic and unstructured environments. Huang's most influential contribution, "Reinforcement Learning-Based Reactive Obstacle Avoidance Method for Redundant Manipulators" (2022, 22 citations), demonstrated how RL frameworks can enable manipulators to simultaneously track desired trajectories and avoid obstacles — a critical requirement in human-robot collaboration scenarios. Building on this foundation, he extended these methods to space robotics, proposing novel obstacle-avoidance motion planning strategies for on-orbit operations in his 2023 work (14 citations), addressing the unique challenges of unstructured extraterrestrial environments. His earlier contributions include a dynamic probabilistic roadmap blended potential field approach for multi-dimensional path planning (2021, 7 citations) and guided deep reinforcement learning for manipulator path planning (2021, 4 citations). Collectively, Huang's portfolio reflects a coherent research vision: making robotic systems safer, more adaptive, and operationally reliable across both terrestrial and space applications. His work is increasingly recognized within the robotics and autonomous systems community.
Research Focus
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