HoMMI：从人类示范中学习全身移动操作
HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations
AI总结：
本文提出HoMMI框架，通过人类示范直接学习全身移动操作，结合眼动感知增强数据收集能力，解决人机体感知差距问题，实现长周期移动操作任务。
摘要：
我们提出了全身移动操作接口（HoMMI），一种数据收集和策略学习框架，该框架直接从无机器人的人类示范中学习全身移动操作。我们通过加入眼动感知来增强UMI接口，以捕捉移动操作所需的全局上下文，从而实现便携、无机器人和可扩展的数据收集。然而，简单地将眼动感知纳入会引入更大的人机体感知差距，使策略转移困难。我们通过显式跨体感知手眼策略设计来弥合这一差距，包括一种不依赖体态的视觉表示；一种放松的头部动作表示；以及一个全身控制器，通过协调的全身运动在机器人特定的物理约束下实现手眼轨迹。这些共同使得长周期移动操作任务成为可能，这些任务需要双臂和全身协调、导航和主动感知。结果最好通过：https://hommi-robot.github.io查看。
英文摘要：
We present Whole-Body Mobile Manipulation Interface (HoMMI), a data collection and policy learning framework that learns whole-body mobile manipulation directly from robot-free human demonstrations. We augment UMI interfaces with egocentric sensing to capture the global context required for mobile manipulation, enabling portable, robot-free, and scalable data collection. However, naively incorporating egocentric sensing introduces a larger human-to-robot embodiment gap in both observation and action spaces, making policy transfer difficult. We explicitly bridge this gap with a cross-embodiment hand-eye policy design, including an embodiment agnostic visual representation; a relaxed head action representation; and a whole-body controller that realizes hand-eye trajectories through coordinated whole-body motion under robot-specific physical constraints. Together, these enable long-horizon mobile manipulation tasks requiring bimanual and whole-body coordination, navigation, and active perception. Results are best viewed on: https://hommi-robot.github.io
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。
