# DragMesh-2：物理合理的铰接物体灵巧手交互

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-13 08:00
- AIHOT 分数：37
- AIHOT 链接：https://aihot.virxact.com/items/cmqkmvyxz00hwslh6zs8o0gqa
- 原文链接：https://arxiv.org/abs/2606.15133

## AI 摘要

DragMesh-2提出接触驱动框架，将铰接物体交互从以物体为中心扩展为由手驱动的灵巧手交互，铰接运动必须通过物理接触产生。同时提出PICA——一种物理感知接触感知训练机制，无需触觉或力反馈即可注入物理信号，提升接触负载变化下的鲁棒性和任务成功率。在七个GAPartNet物体上的评估显示，DragMesh-2在多种阻尼条件下相比对比方法鲁棒性更强，同时保持高任务成功率。

## 正文

Dexterous interaction with articulated objects is important for household, assistive, and humanoid manipulation, where multi-finger hands can provide compliant contact patterns beyond parallel-jaw grasping. However, articulated-object manipulation differs from static-object manipulation: the target part cannot be directly actuated, and its motion must emerge through sustained physical hand--handle contact. This makes the transition from object-centric articulated generation to hand-driven dexterous hand--object interaction non-trivial, since geometric trajectory replay or open-loop execution does not model the contact dynamics required to move the articulated part. Moreover, policies trained only for task completion under fixed dynamics can overfit nominal contact loads, especially without tactile or force feedback, and may degrade when the contact load changes. To address these challenges, we present DragMesh-2, a contact-driven framework for dexterous interaction with articulated objects that extends articulated interaction from object-centric generation to hand-driven dexterous hand--object interaction, where articulated motion must arise through physical contact. We further propose PICA, a physically informed contact-aware training mechanism that injects physical signals into policy learning without tactile or force feedback, improving robustness and task success under changing contact loads. Finally, we conduct systematic evaluation across multiple damping conditions and articulated-object categories to study robustness under contact-load variation, and provide a pure-geometry dexterous interaction resource to support future loco-manipulation and humanoid hand--object interaction research. Across seven GAPartNet objects, DragMesh-2 achieves stronger robustness under contact-load variation than the compared methods while maintaining high task success across damping conditions.