General Humanoid Whole-Body Control via Pretraining and Fast Adaptation

TL;DR

This paper introduces FAST, a versatile humanoid control framework that combines fast adaptation and stable motion tracking through novel residual policy adaptation and CoM-aware control, outperforming existing methods in robustness and generalization.

Contribution

The paper proposes a new control framework with Parseval-Guided Residual Policy Adaptation and CoM-aware control, enabling efficient adaptation and improved balance in humanoid robots.

Findings

FAST outperforms state-of-the-art baselines in robustness.

FAST demonstrates efficient adaptation to new motions.

FAST achieves superior generalization in diverse scenarios.

Abstract

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.