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About

Zhicheng He is a robotics researcher whose work centers on legged locomotion, motion planning, and control systems for humanoid and multi-legged robots. His research has made meaningful contributions to advancing the stability, efficiency, and adaptability of biped and hexapod robotic platforms across a range of challenging environments. He is perhaps best known for his application of the inverted pendulum model to biped turn-planning (2020, 14 citations), a foundational contribution that has influenced subsequent work in walking trajectory generation. His broader portfolio spans quadratic programming-based dynamic control frameworks, multi-objective parameter optimization for motion systems, and centroidal momentum allocation for uneven terrain traversal — all addressing the persistent challenge of making bipedal robots walk reliably and naturally. Notably, He has embraced modern learning-based approaches, integrating reinforcement learning with Fourier series gait planning and graph-powered motion matching to produce more generalizable, human-like robot behaviors. His earlier work on the MiniRHex hexapod and high-performance computing for dynamic running robots demonstrates a versatile foundation in mechanical design and real-time control. With accumulating citations across hardware and learning-driven methodologies, Zhicheng He represents an emerging voice in the quest to bring robust, adaptive locomotion to real-world humanoid robotics.