人-人形机器人的跨本体行为-技能迁移：基于分解的对抗演示学习
Human-Humanoid Robots Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning from Demonstration
AI总结：
针对人形机器人高自由度导致的数据收集困难，提出跨本体技能迁移框架，利用数字人模型和对抗模仿学习行为原语，将复杂结构分解为功能组件独立训练并动态协调，通过人-物交互图实现任务泛化，在五台机器人上验证有效降低数据需求并提升迁移效率。
摘要：
人形机器人被设想为能够执行广泛人类水平移动操作任务的具身智能体，特别是在需要繁重和重复劳动的场景中。然而，由于人形机器人具有高自由度，学习这些技能极具挑战性，且为其收集充足训练数据是一个费力的过程。鉴于新型人形平台不断涌现，允许泛化技能迁移的跨本体框架正变得日益关键。为此，我们提出一种可迁移框架，通过使用统一的数字人模型作为通用原型，绕过对每个新机器人平台重新训练的需求，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原语，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。通过人-物交互图实现任务泛化，并通过特定本体的运动学运动重定向和动力学微调将技能迁移到不同机器人。我们的框架在五台具有不同配置的人形机器人上进行验证，展示了稳定的移动操作，突显了其在减少数据需求和提升跨平台技能迁移效率方面的有效性。
英文摘要：
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.
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。
