从人与人示范中学习全身体态的人与人机交互
Learning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations
AI总结：
本文提出PAIR和D-STAR方法，从人与人示范中学习高质量的全身体态交互，通过解耦时空推理实现同步协作。
摘要：
使人形机器人能够与人进行物理交互是关键前沿，但进展受高质量人机交互（HHoI）数据稀缺的阻碍。虽然利用丰富的人际交互（HHI）数据提供了一种可扩展的替代方案，但我们首先证明标准的重定向失败，因为打破了必要的接触。我们通过PAIR（物理感知交互重定向），一种以接触为中心的两阶段流程，来解决这个问题，该流程在形态差异下保持接触语义以生成物理一致的HHoI数据。然而，高质量的数据却暴露了第二个失败：传统模仿学习策略仅模仿轨迹，缺乏交互理解。因此，我们引入D-STAR（解耦时空动作推理器），一种分层策略，将何时行动与在哪里行动分离。在D-STAR中，相关注（何时）和多尺度空间模块（哪里）通过扩散头融合，产生超越模仿的同步全身行为。通过解耦这些推理流，我们的模型学习了稳健的时间阶段，而不受空间噪声的干扰，导致响应、同步的合作。我们通过广泛且严谨的模拟验证了我们的框架，证明了相对于基线方法有显著的性能提升，并展示了一个完整的、有效的从HHI数据中学习复杂全身交互的管道。
英文摘要：
Enabling humanoid robots to physically interact with humans is a critical frontier, but progress is hindered by the scarcity of high-quality Human-Humanoid Interaction (HHoI) data. While leveraging abundant Human-Human Interaction (HHI) data presents a scalable alternative, we first demonstrate that standard retargeting fails by breaking the essential contacts. We address this with PAIR (Physics-Aware Interaction Retargeting), a contact-centric, two-stage pipeline that preserves contact semantics across morphology differences to generate physically consistent HHoI data. This high-quality data, however, exposes a second failure: conventional imitation learning policies merely mimic trajectories and lack interactive understanding. We therefore introduce D-STAR (Decoupled Spatio-Temporal Action Reasoner), a hierarchical policy that disentangles when to act from where to act. In D-STAR, Phase Attention (when) and a Multi-Scale Spatial module (where) are fused by the diffusion head to produce synchronized whole-body behaviors beyond mimicry. By decoupling these reasoning streams, our model learns robust temporal phases without being distracted by spatial noise, leading to responsive, synchronized collaboration. We validate our framework through extensive and rigorous simulations, demonstrating significant performance gains over baseline approaches and a complete, effective pipeline for learning complex whole-body interactions from HHI data.
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。
