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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.03068 |
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| _version_ | 1866913979688812544 |
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| author | Chen, Sirui Ye, Yufei Cao, Zi-Ang Lew, Jennifer Xu, Pei Liu, C. Karen |
| author_facet | Chen, Sirui Ye, Yufei Cao, Zi-Ang Lew, Jennifer Xu, Pei Liu, C. Karen |
| contents | We propose Hand-Eye Autonomous Delivery (HEAD), a framework that learns navigation, locomotion, and reaching skills for humanoids, directly from human motion and vision perception data. We take a modular approach where the high-level planner commands the target position and orientation of the hands and eyes of the humanoid, delivered by the low-level policy that controls the whole-body movements. Specifically, the low-level whole-body controller learns to track the three points (eyes, left hand, and right hand) from existing large-scale human motion capture data while high-level policy learns from human data collected by Aria glasses. Our modular approach decouples the ego-centric vision perception from physical actions, promoting efficient learning and scalability to novel scenes. We evaluate our method both in simulation and in the real-world, demonstrating humanoid's capabilities to navigate and reach in complex environments designed for humans. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03068 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hand-Eye Autonomous Delivery: Learning Humanoid Navigation, Locomotion and Reaching Chen, Sirui Ye, Yufei Cao, Zi-Ang Lew, Jennifer Xu, Pei Liu, C. Karen Robotics We propose Hand-Eye Autonomous Delivery (HEAD), a framework that learns navigation, locomotion, and reaching skills for humanoids, directly from human motion and vision perception data. We take a modular approach where the high-level planner commands the target position and orientation of the hands and eyes of the humanoid, delivered by the low-level policy that controls the whole-body movements. Specifically, the low-level whole-body controller learns to track the three points (eyes, left hand, and right hand) from existing large-scale human motion capture data while high-level policy learns from human data collected by Aria glasses. Our modular approach decouples the ego-centric vision perception from physical actions, promoting efficient learning and scalability to novel scenes. We evaluate our method both in simulation and in the real-world, demonstrating humanoid's capabilities to navigate and reach in complex environments designed for humans. |
| title | Hand-Eye Autonomous Delivery: Learning Humanoid Navigation, Locomotion and Reaching |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.03068 |