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About

Jiaqi Zhang is a pioneer in bio-inspired robotics, whose work bridges the gap between neuroscience and mechanical engineering. His primary research focuses on locomotion control for legged robots, with a particular emphasis on Central Pattern Generators (CPGs)—neural networks that produce rhythmic movements like walking and breathing. Zhang’s seminal 2009 survey on biologically inspired locomotion control (82 citations) remains a foundational reference in the field, synthesizing decades of research into a roadmap for roboticists. His most impactful contributions include modeling CPG networks using coupled Van Der Pol oscillators (30 citations) to generate stable, adaptive gaits for quadruped robots, and demonstrating how biological neural mechanisms can be translated into robust control systems (35 citations). Earlier in his career, Zhang explored evolutionary algorithms for gait optimization, using reinforcement learning to evolve efficient walking patterns for Sony’s AIBO robot (10 citations). His work has not only advanced the theoretical understanding of rhythmic motion generation but also provided practical frameworks for building more agile, animal-like robots. Zhang’s research continues to inspire new generations of roboticists seeking to unlock the secrets of natural locomotion.