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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.03243 |
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| _version_ | 1866914568446410752 |
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| author | Xu, Xiaomeng Park, Jisang Zhang, Han Cousineau, Eric Bhat, Aditya Barreiros, Jose Wang, Dian Bohg, Jeannette Song, Shuran |
| author_facet | Xu, Xiaomeng Park, Jisang Zhang, Han Cousineau, Eric Bhat, Aditya Barreiros, Jose Wang, Dian Bohg, Jeannette Song, Shuran |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03243 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations Xu, Xiaomeng Park, Jisang Zhang, Han Cousineau, Eric Bhat, Aditya Barreiros, Jose Wang, Dian Bohg, Jeannette Song, Shuran Robotics 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 |
| title | HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.03243 |