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About

Chuanzheng Li is a leading researcher in legged robotics, specializing in model predictive control (MPC), trajectory optimization, and dynamic motion planning for quadrupedal and jumping robots. His major contributions center on developing novel control frameworks that enable robots to perform agile, contact-rich maneuvers in complex environments. Li’s most influential work, the representation-free MPC (RF-MPC) framework, eliminates the need for simplified rotational representations by directly using rotation matrices, achieving robust dynamic motions in 3D space—a paper that has garnered 173 citations. He further advanced the field with a hybrid iLQR MPC approach for contact-implicit stabilization, addressing the challenge of hybrid dynamics in legged systems (41 citations). Li also pioneered mixed-integer convex optimization for single-leg dynamic motion planning, overcoming nonlinear torque constraints (24 citations), and introduced centroidal-momentum-based trajectory generation for legged locomotion. His hybrid sampling/optimization planning method enables agile jumping robots to traverse challenging terrains. Through these works, Li has significantly pushed the boundaries of real-time, optimization-driven control for legged robots, making him a key figure in the pursuit of highly dynamic and autonomous robotic locomotion.