Jiayi Wang

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About

Jiayi Wang is a robotics researcher specializing in motion planning and locomotion control for legged robots, with a particular focus on bridging computational efficiency and physical feasibility in complex real-world environments. Wang's work addresses fundamental challenges in enabling robots to navigate uneven and demanding terrain through intelligent, automated planning frameworks. A central contribution of Wang's research is the development of methods for automatic gait pattern selection, removing the reliance on hand-crafted heuristics and allowing legged robots to autonomously determine optimal locomotion strategies — a paper that has garnered 13 citations since its 2020 publication. Building on this foundation, Wang introduced multi-fidelity receding horizon planning (2021, 10 citations), a framework that balances computational cost against planning depth, enabling robots to anticipate future contact sequences much as humans naturally do when traversing difficult terrain. Most recently, Wang has integrated machine learning into receding horizon planning (2022, 6 citations), using learned value functions to guide robots in building momentum and making strategically informed decisions over longer horizons. Collectively, Wang's research pushes the boundary between classical optimization and data-driven methods, offering scalable, principled solutions for multi-contact locomotion — a critical step toward deploying agile legged robots in real-world settings.