让机器人像人一样稳住极端平衡姿势，还能扛住大力冲击。
HuB: Learning Extreme Humanoid Balance
- 分三步优化：修正动作参考、专注平衡学习、提升仿真到现实的鲁棒性
- 在真实机器人上实现单腿站立和过头踢腿，抗干扰力强于基线方法
- 适合做高难度人形机器人平衡控制的研究者或工程师
人体具备卓越的运动能力，例如单脚站立或完成腿部抬升超过1.5米的高踢动作，均需精准的平衡控制。尽管近期人形机器人控制研究已利用强化学习追踪人类动作以习得技能，但应用于高强度平衡任务仍面临挑战。本文识别出三大障碍：参考动作误差引发的不稳定性、形态差异导致的学习困难，以及传感器噪声与未建模动态造成的仿真到现实差距。为此，我们提出HuB（Humanoid Balance）统一框架，整合参考动作优化、平衡感知策略学习与仿真到现实的鲁棒训练，每个模块针对特定问题。我们在Unitree G1人形机器人上验证了该方法，在包括“燕子平衡”和“李小龙踢腿”在内的复杂静态平衡任务中表现优异。即使遭遇强力足球撞击等物理扰动，所提策略仍保持稳定，而基线方法则持续失败。项目网站：https://hub-robot.github.io
原文摘要 · Abstract (English)
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
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