Learning Humanoid Standing-up Control across Diverse Postures

摘要

Standing-up control is crucial for humanoid robots, with the potential for integration into current locomotion and loco-manipulation systems. Existing approaches are either limited to simulations that neglect hardware constraints or rely on predefined ground-specific motion trajectories, failing to enable standing-up across diverse postures in the real world. To bridge this gap, we present HoST (