Learning a general whole-body controller for humanoid robots remains challenging due to the diversity of motion distributions, the difficulty of fast adaptation, and the need for robust balance in high-dynamic scenarios. Existing approaches often require task-specific training or suffer from performance degradation when adapting to new motions. In this paper, we present FAST, a general humanoid whole-body control framework that enables Fast Adaptation and Stable Motion Tracking. FAST introduces Parseval-Guided Residual Policy Adaptation, which learns a lightweight delta action policy under orthogonality and KL constraints, enabling efficient adaptation to out-of-distribution motions while mitigating catastrophic forgetting. To further improve physical robustness, we propose Center-of-Mass-Aware Control, which incorporates CoM-related observations and objectives to enhance balance when tracking challenging reference motions. Extensive experiments in simulation and real-world deployment demonstrate that FAST consistently outperforms state-of-the-art baselines in robustness, adaptation efficiency, and generalization.

翻译：为人形机器人学习通用全身控制器仍面临诸多挑战，包括运动分布的多样性、快速适应的困难性，以及在高动态场景下保持鲁棒平衡的需求。现有方法通常需要针对特定任务进行训练，或在适应新运动时出现性能下降。本文提出FAST，一个通用的人形机器人全身控制框架，旨在实现快速适应与稳定运动跟踪。FAST引入了Parseval引导的残差策略适应方法，该方法在正交性与KL约束下学习一个轻量级的增量动作策略，从而能够高效适应分布外运动，同时缓解灾难性遗忘。为进一步提升物理鲁棒性，我们提出了质心感知控制，该方法融合了与质心相关的观测与目标，以在跟踪具有挑战性的参考运动时增强平衡能力。大量的仿真与真实世界部署实验表明，FAST在鲁棒性、适应效率与泛化能力方面均持续优于现有最先进的基线方法。