Humanoid robots are envisioned as embodied intelligent agents capable of performing a wide range of human-level loco-manipulation tasks, particularly in scenarios requiring strenuous and repetitive labor. However, learning these skills is challenging due to the high degrees of freedom of humanoid robots, and collecting sufficient training data for humanoid is a laborious process. Given the rapid introduction of new humanoid platforms, a cross-embodiment framework that allows generalizable skill transfer is becoming increasingly critical. To address this, we propose a transferable framework that reduces the data bottleneck by using a unified digital human model as a common prototype and bypassing the need for re-training on every new robot platform. The model learns behavior primitives from human demonstrations through adversarial imitation, and the complex robot structures are decomposed into functional components, each trained independently and dynamically coordinated. Task generalization is achieved through a human-object interaction graph, and skills are transferred to different robots via embodiment-specific kinematic motion retargeting and dynamic fine-tuning. Our framework is validated on five humanoid robots with diverse configurations, demonstrating stable loco-manipulation and highlighting its effectiveness in reducing data requirements and increasing the efficiency of skill transfer across platforms.

翻译：人形机器人被设想为具身智能体，能够执行广泛的人类级移动操作任务，特别是在需要繁重重复劳动的场合。然而，由于人形机器人具有高自由度，学习这些技能具有挑战性，且为人形机器人收集充足的训练数据是一个费力的过程。鉴于新型人形机器人平台不断涌现，一个允许可泛化技能迁移的跨具身框架正变得日益关键。为此，我们提出了一种可迁移框架，通过使用统一的数字人体模型作为通用原型，并避免在每个新机器人平台上重新训练，从而缓解数据瓶颈。该模型通过对抗模仿从人类演示中学习行为基元，并将复杂的机器人结构分解为功能组件，每个组件独立训练并动态协调。任务泛化通过人-物交互图实现，技能则通过特定具身的运动学运动重定向和动力学微调迁移至不同的机器人。我们的框架在五种不同构型的人形机器人上进行了验证，展示了稳定的移动操作能力，并突显了其在减少数据需求、提升跨平台技能迁移效率方面的有效性。