Humanoid-DART: 使用扩散引导的增强通过重新标记和跟踪实现人形机器人全身操控
Humanoid-DART: Humanoid Loco-Manipulation using Diffusion-guided Augmentation through Relabeling and Tracking
AI总结：
提出一种自监督框架，从稀疏演示中引导并逐步扩展行为库，结合扩散轨迹生成与强化学习，实现目标条件策略学习，减少专家监督。
摘要：
模仿人类演示已成为学习人形机器人全身操控策略的主流范式。然而，由于收集多样化演示的高成本以及需要持续的人工干预来纠正策略失败，这些方法的扩展仍然具有挑战性。在本文中，我们提出了一种自监督框架，该框架从稀疏演示中引导并逐步扩展其行为库，从而能够学习一个目标条件策略，该策略以最少的专家监督自动探索目标空间。我们的方法将基于扩散的轨迹生成与强化学习相结合，其中后者用于跟踪扩散模型为一系列全身操控技能生成的目标条件轨迹。通过广泛的消融研究和与最先进方法的比较，我们展示了我们的框架在多种人形机器人全身操控技能上的有效性。
英文摘要：
Imitating human demonstrations has emerged as a dominant paradigm for learning humanoid loco-manipulation policies. However, scaling these approaches remains challenging due to the high cost of collecting diverse demonstrations and the need for continual human intervention to correct policy failures. In this paper, we present a self-supervised framework that bootstraps from sparse demonstrations and progressively expands its behavioral repertoire, enabling the learning of a goal-conditioned policy that automatically explores the goal space with minimal expert supervision. Our approach combines diffusion-based trajectory generation with reinforcement learning, where the latter is used to track goal-conditioned trajectories produced by the diffusion model for a range of loco-manipulation skills. Through extensive ablation studies and comparisons with state-of-the-art methods, we demonstrate the effectiveness of our framework on multiple humanoid loco-manipulation skills.
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。
