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的关键驱动力，然而现有方法大多需要针对特定机器人进行训练。本文研究了跨具身人形机器人控制问题，并证明单一策略通过一次性训练即可稳健地泛化至广泛的人形机器人设计。我们提出XHugWBC——一种新颖的跨具身训练框架，通过以下机制实现通用型人形机器人控制：(1) 物理一致的形态随机化，(2) 跨异构人形机器人的语义对齐观测与动作空间，以及(3) 建模形态与动力学特性的高效策略架构。XHugWBC不绑定任何特定机器人，而是在训练过程中内化广泛的形态与动力学特征分布。通过从多样化随机化具身中学习运动先验，该策略获得了强大的结构偏置，支持对未见机器人的零样本迁移。在十二个仿真人形机器人与七个真实机器人上的实验验证了所得通用控制器卓越的泛化能力与鲁棒性。