DragMesh-2: 与铰接物体的物理合理灵巧手-物体交互
DragMesh-2: Physically Plausible Dexterous Hand-Object Interaction with Articulated Objects
AI总结：
提出DragMesh-2框架，通过接触驱动的灵巧手-铰接物体交互，结合物理信息感知训练机制PICA，在无触觉反馈下提升变接触负载的鲁棒性。
摘要：
与铰接物体的灵巧交互对于家庭、辅助和人形操作至关重要，其中多指手可以提供超越平行爪抓取的顺应接触模式。然而，铰接物体操作不同于静态物体操作：目标部件无法直接驱动，其运动必须通过持续的物理手-手柄接触来实现。这使得从以物体为中心的铰接生成到手驱动的灵巧手-物体交互的转变变得非平凡，因为几何轨迹重放或开环执行无法模拟移动铰接部件所需的接触动力学。此外，仅在固定动力学下为任务完成训练的策略可能会过拟合标称接触负载，尤其是在没有触觉或力反馈的情况下，并且当接触负载变化时性能可能会下降。为了应对这些挑战，我们提出了DragMesh-2，一个用于与铰接物体灵巧交互的接触驱动框架，它将铰接交互从以物体为中心的生成扩展到手驱动的灵巧手-物体交互，其中铰接运动必须通过物理接触产生。我们进一步提出了PICA，一种物理信息感知的训练机制，它在没有触觉或力反馈的情况下将物理信号注入策略学习，提高了在变化接触负载下的鲁棒性和任务成功率。最后，我们在多个阻尼条件和铰接物体类别上进行了系统评估，以研究接触负载变化下的鲁棒性，并提供了一个纯几何的灵巧交互资源，以支持未来的移动操作和人形手-物体交互研究。在七个GAPartNet物体上，DragMesh-2在接触负载变化下比对比方法实现了更强的鲁棒性，同时在各种阻尼条件下保持了高任务成功率。
英文摘要：
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.
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