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About

Wei Lang is a rising force in legged and amphibious robotics, whose work is carving a practical path toward agile, economical locomotion. His primary research focuses on quadrupedal gait control, reinforcement learning for multi-gait systems, and the control of hybrid amphibious robots. Lang’s major contribution lies in bridging the gap between computationally expensive model predictive control (MPC) and the real-world constraints of embedded hardware. His 2024 paper on “Gait-Heuristic Reinforcement Learning” (5 citations) introduces a method that learns economical, multi-gait locomotion without requiring high-frequency full-body MPC, directly addressing the computing power bottleneck that plagues legged robots. This builds on his 2023 hierarchical RL framework (5 citations) for learning multiple gaits. Lang also demonstrated practical impact with a bounding gait controller for a parallel quadruped (2 citations) and extended his expertise to complex environments with a CPG-MPC controller for a wheel-fin-flipper amphibious robot (1 citation). Though early in his career, Lang’s work is notable for its clear engineering focus: he doesn’t just push algorithmic boundaries but solves the hardware-limited, real-world problems that define modern robotics. His trajectory suggests he will be a key contributor to making legged robots truly practical outside the lab.