标题： 领域随机化在全身类人机器人控制扩散策略训练中的作用 显示英文标题

标题： The Role of Domain Randomization in Training Diffusion Policies for Whole-Body Humanoid Control

摘要： 人形机器人有潜力成为为人类设计的环境中理想的具身形式。由于它们与人体结构相似，因此能够从丰富的演示数据中受益，例如通过遥操作、运动捕捉甚至使用人类执行任务的视频收集的数据。然而，从演示中提炼策略仍然是一项具有挑战性的问题。尽管扩散策略（DPs）在机器人操作方面表现出令人印象深刻的结果，但它们在步态控制和人形机器人控制中的适用性仍未得到充分探索。本文研究了数据集多样性和规模如何影响DPs在人形机器人全身控制中的性能。在模拟的IsaacGym环境中，我们通过在各种领域随机化（DR）条件下训练对抗性运动先验（AMP）代理生成合成演示，并比较适用于不同规模和多样性的数据集的DPs。我们的研究结果显示，虽然DPs可以实现稳定的行走行为，但在步态策略的成功训练需要比操作任务显著更大且更多样化的数据集，即使在简单的场景下也是如此。 显示英文摘要

摘要： Humanoids have the potential to be the ideal embodiment in environments designed for humans. Thanks to the structural similarity to the human body, they benefit from rich sources of demonstration data, e.g., collected via teleoperation, motion capture, or even using videos of humans performing tasks. However, distilling a policy from demonstrations is still a challenging problem. While Diffusion Policies (DPs) have shown impressive results in robotic manipulation, their applicability to locomotion and humanoid control remains underexplored. In this paper, we investigate how dataset diversity and size affect the performance of DPs for humanoid whole-body control. In a simulated IsaacGym environment, we generate synthetic demonstrations by training Adversarial Motion Prior (AMP) agents under various Domain Randomization (DR) conditions, and we compare DPs fitted to datasets of different size and diversity. Our findings show that, although DPs can achieve stable walking behavior, successful training of locomotion policies requires significantly larger and more diverse datasets compared to manipulation tasks, even in simple scenarios.