Hewei Gao

Papers

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About

Hewei Gao is a roboticist specializing in legged locomotion and control systems for walking robots operating in complex environments. His primary research focuses on quadruped robot gait planning and foothold selection for rough terrain navigation. Gao’s most significant contribution is a hierarchical control framework for static gait generation, which first optimizes the robot’s center-of-mass trajectory before synthesizing stable foothold sequences—a method detailed in his most-cited work (14 citations). He further advanced terrain adaptability by developing a learning-based cost function for foothold selection, integrating Denavit–Hartenberg kinematic models with Time-of-Flight camera data to enable physical quadruped robots to autonomously evaluate and choose stable footing (5 citations). Beyond legged robotics, Gao has contributed to educational robotics through the design of a six-degree-of-freedom manipulator control system using PLC programming and TCP/IP communication, demonstrating his versatility across both research and pedagogical applications. His work bridges classical control theory with machine learning approaches, addressing the fundamental challenge of enabling quadruped robots to walk reliably on uneven, unstructured terrain—a critical step toward deploying these machines in real-world search-and-rescue or exploration missions.