HuB: Learning Extreme Humanoid Balance

Tong Zhang, Boyuan Zheng, Ruiqian Nai, Yingdong Hu, Yen-Jen Wang, Geng Chen, Fanqi Lin, Jiongye Li, Chuye Hong, Koushil Sreenath, Yang Gao

Keywords: Humanoid Whole-body Control, Balance Control, Reinforcement Learning

Abstract: The human body demonstrates exceptional motor capabilities—such as standing steadily on one foot or performing a high kick with the leg raised over 1.5 meters—both requiring precise balance control. While recent research on humanoid control has leveraged reinforcement learning to track human motions for skill acquisition, applying this paradigm to balance-intensive tasks remains challenging. In this work, we identify three key obstacles: instability from reference motion errors, learning difficulties due to morphological mismatch, and the sim-to-real gap caused by sensor noise and unmodeled dynamics. To address these challenges, we propose $\textbf{HuB}$ ($\textbf{Hu}$manoid $\textbf{B}$alance), a unified framework that integrates $\textit{reference motion refinement}$, $\textit{balance-aware policy learning}$, and $\textit{sim-to-real robustness training}$, with each component targeting a specific challenge. We validate our approach on the Unitree G1 humanoid robot across challenging quasi-static balance tasks, including extreme single-legged poses such as $\texttt{Swallow Balance}$ and $\texttt{Bruce Lee’s Kick}$. Our policy remains stable even under strong physical disturbances—such as a forceful soccer strike—while baseline methods consistently fail to complete these tasks.

Submission Number: 470