可扩展且通用的全身控制：跨人形机器人运动
Scalable and General Whole-Body Control for Cross-Humanoid Locomotion
AI总结：
提出XHugWBC框架，通过形态随机化、语义对齐观测动作空间和有效策略架构，实现单次训练后跨多种人形机器人的零样本泛化控制。
摘要：
基于学习的全身控制器已成为人形机器人的关键驱动力，但现有方法大多需要针对特定机器人进行训练。本文研究了跨实体人形控制问题，并表明单一策略通过一次性训练即可稳健地泛化到各种人形机器人设计。我们提出了XHugWBC，一种新颖的跨实体训练框架，通过以下方式实现通用人形控制：(1) 物理一致的形态随机化，(2) 跨不同人形机器人的语义对齐观测和动作空间，以及(3) 建模形态和动力学特性的有效策略架构。XHugWBC不依赖于任何特定机器人，而是在训练过程中内化广泛的形态和动力学特性分布。通过从多样化的随机实体中学习运动先验，策略获得了强大的结构偏差，支持对未见过的机器人进行零样本迁移。在12个模拟人形机器人和7个真实世界机器人上的实验证明了所得通用控制器的强泛化性和鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
