Push the Limit: Learning High-Speed Sprinting and Aggressive Cornering on a Full-Scale Humanoid

This paper presents a learning-based framework that enables a full-scale bipedal humanoid to sprint at over 7 m/s with motion-prior-guided gait and perform aggressive cornering at high speed with a pronounced trunk lean of up to 25°. The framework addresses challenges like high-frequency yaw-rate oscillations and skill-specific reward design through causal multi-head attention and skill-conditioned discriminators, achieving stable high-speed sprinting and aggressive cornering in both simulation and real-robot deployment.