Chasing Stability: Humanoid Running via Control Lyapunov Function Guided Reinforcement Learning
Overview
This paper presents CLF-RL, a method that integrates nonlinear control theory with reinforcement learning to achieve stable running on humanoid robots. By embedding Control Lyapunov Functions into the reward structure and using full-order dynamics for trajectory optimization, the approach eliminates the need for hand-crafted heuristic rewards. The resulting policy enables a Unitree G1 robot to perform reliable full-flight running in tight corridors and outdoor settings while remaining robust to disturbances and utilizing only on-board sensors for global reference tracking.
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Reported quantity · position tracking improvement: 1.7 other · Basis not reported
“wards. We further develop a trajectory optimization routine that can handle the hybrid structure of the system to produce dynamically feasible, multi-domain, trackable references; using full-order dynamics rather than a kinematic approximation yields up to a 1.7x improvement in position tracking. The resulting policy runs on a Unitree G1 humanoid with a full flight phase, operating reliably in tight corridors such as”
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- arXiv Robotics — research abstractsSignalChasing Stability: Humanoid Running via Control Lyapunov Function Guided Reinforcement Learning
This paper presents CLF-RL, a method that integrates nonlinear control theory with reinforcement learning to achieve stable running on humanoid robots. By embedding Control Lyapunov Functions into the reward structure and using full-order dynamics for trajectory optimization, the approach eliminates the need for hand-crafted heuristic rewards. The resulting policy enables a Unitree G1 robot to perform reliable full-flight running in tight corridors and outdoor settings while remaining robust to disturbances and utilizing only on-board sensors for global reference tracking.
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