Humanoids deployed in human-centered environments must handle force-interactive tasks, where external contacts introduce unexpected disturbances that disrupt locomotion accuracy and stability. Existing learning-based approaches rely on broad domain randomization, task-specific force objectives, or learning-based force estimators from motion history, each of which compromises accuracy, task transferability, or out-of-distribution (OOD) robustness. We present Analytical Disturbance-Aware Policy Training (ADAPT), a framework that equips humanoid policies with a physically grounded disturbance observer. The core of ADAPT is an analytical whole-body disturbance observer that estimates residual force/torque online with the accessible robot dynamics, without requiring force/torque sensors. Fed directly into the policy, the estimated disturbances give the humanoid an explicit, physics-derived sense of external force/torque that can generalize across diverse unseen scenes. Experiments on a Unitree G1 humanoid show that ADAPT achieves accurate disturbance prediction and stronger robustness than a proprioception-only baseline under torso perturbations, standing pushes, and asymmetric hand payloads, with improved velocity tracking even on OOD disturbances. Moreover, ADAPT enables penalizing inferred disturbances at lower-body joints to encourage lighter locomotion.

翻译：部署在人类中心环境中的人形机器人必须处理力交互任务，其中外部接触会引入意外扰动，破坏行走的精度与稳定性。现有基于学习的方法依赖于广泛的域随机化、特定任务的力目标，或基于运动历史的学习型力估计器，但每种方法在精度、任务迁移性或分布外（OOD）鲁棒性方面均存在妥协。本文提出解析扰动感知策略训练（ADAPT）框架，该框架为人形机器人策略配备了基于物理的扰动观测器。ADAPT的核心是一种解析全身扰动观测器，它利用可获得的机器人动力学在线估计残余力/力矩，无需力/力矩传感器。估计出的扰动被直接输入策略，使人形机器人获得基于物理的外部力/力矩显式感知，并能泛化至各类未见场景。在宇树G1人形机器人上的实验表明：在躯干扰动、站立推击及非对称手持载荷条件下，ADAPT实现了比仅依赖本体感知的基线方法更准确的扰动预测和更强的鲁棒性，即使在OOD扰动下仍能改善速度跟踪性能。此外，ADAPT可通过惩罚下肢关节的推断扰动来鼓励更轻快的行走步态。