HuB: Learning Extreme Humanoid Balance
Journal:
arXiv
Published Date:
May 12, 2025
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 HuB (Humanoid Balance), a
unified framework that integrates reference motion refinement, balance-aware
policy learning, and 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 Swallow Balance and 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.
Project website: https://hub-robot.github.io
