外观
论文arXiv 2609.166831 个月前
Weave：从人-物交互中学习全身灵巧移动操作
原文链接：Weave: Learning Whole-Body Dexterous Loco-Manipulation from Human-Object Interactions（arXiv 2609.16683 · 发布日期：2026-09-15）
Weave: Learning Whole-Body Dexterous Loco-Manipulation from Human-Object Interactions
Weave通过接触感知重定向、接近运动补全和几何感知策略，从捕获的人-物交互学习全身灵巧类人交互，并在九个物体上验证。
核心要点
- 类人-物体交互需协调全身平衡、移动和灵巧手部接触，迁移人类示范受形态与动力学差异影响。
- Weave将捕获的人-物交互经接触感知重定向和接近运动补全，转为可执行的机器人-物体参考。
- 核心策略感知接触与几何，跨多个物体和交互序列联合控制身体关节与驱动手指关节。
- 九个物体上训练交互成功率92.5%，未额外训练的未见序列成功率65.0%；并发布物理执行rollouts。
验证范围与局限：验证范围为九个物体上的训练交互和未见序列，并发布物理执行轨迹；摘要未说明具体机器人数量。
英文摘要 (Abstract)
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 is a contact- and geometry-aware policy that jointly commands 29 body joints and 12 actuated finger joints across multiple objects and interaction sequences. Evaluation across nine objects yields a 92.5% success rate on trained interactions and, without any additional training, 65.0% on sequences never seen during training. We additionally release ~9,000 physically executed rollouts spanning ~23 hours, providing robot-object trajectories with contact annotations for downstream interaction-policy learning and physically consistent HOI motion generation. Project website: https://xiaohu-art.github.io/Weave/
作者：Liu Cao, Xingze Wu, Jingzhi Cui, Botian Xu, Mingzhi Pei, Ruoqu Chen, Mengdi Xu
来源
本页事实对应的公开页面，以原文为准。