Learning-based whole-body controllers have become a key driver for humanoid robots, yet most existing approaches require robot-specific training. In this paper, we study the problem of cross-embodiment humanoid control and show that a single policy can robustly generalize across a wide range of humanoid robot designs with one-time training. We introduce XHugWBC, a novel cross-embodiment training framework that enables generalist humanoid control through: (1) physics-consistent morphological randomization, (2) semantically aligned observation and action spaces across diverse humanoid robots, and (3) effective policy architectures modeling morphological and dynamical properties. XHugWBC is not tied to any specific robot. Instead, it internalizes a broad distribution of morphological and dynamical characteristics during training. By learning motion priors from diverse randomized embodiments, the policy acquires a strong structural bias that supports zero-shot transfer to previously unseen robots. Experiments on twelve simulated humanoids and seven real-world robots demonstrate the strong generalization and robustness of the resulting universal controller.

翻译：基于学习的全身控制器已成为人形机器人发展的关键驱动力，但现有方法大多需要针对特定机器人进行训练。本文研究跨本体人形机器人控制问题，证明单次训练得到的单一策略能够稳健泛化至多种人形机器人设计。我们提出XHugWBC，一种新型跨本体训练框架，通过以下方式实现通用人形机器人控制：(1) 物理一致的形态学随机化；(2) 语义对齐的多类型人形机器人的观测与动作空间；(3) 建模形态与动力学特性的高效策略架构。XHugWBC不依赖特定机器人，而是在训练过程中内化广泛的形态与动力学特性分布。通过从多样随机化本体中学习运动先验，策略获得支持零样本迁移至未见机器人的强结构偏差。在12个仿真人形机器人和7个真实机器人上的实验表明，该通用控制器具有强泛化性与鲁棒性。