Weave: Learning Whole-Body Dexterous Loco-Manipulation from Human-Object Interactions
Learning humanoid-object interaction requires coordinating whole-body balance, locomotion, and dexterous hand contact to control both robot and object motion. Human demonstrations provide examples of coordinated interaction, but transferring these behaviors to humanoid robots requires learning how to establish and maintain effective contacts under different embodiments and dynamics. We present Weave, a unified framework for learning whole-body dexterous humanoid-object interaction from captured human demonstrations. Weave first converts captured human-object interactions into executable robot-object references through contact-aware retargeting and approach-motion completion. At its core i...
| Authors | Liu Cao, Xingze Wu, Jingzhi Cui et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
Models and datasets referencing this paper
- appolyn/WeaveDataset
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