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.

翻译：灵巧地与可活动物体进行交互对于家庭、辅助及类人操作任务至关重要，多指手可提供超越平行夹爪抓取方式的柔性接触模式。然而，可活动物体的操作与静态物体存在本质差异：目标部件无法直接驱动，其运动必须通过持续的手柄物理接触才能实现。这使得从以物体为中心的可活动生成到以手驱动的灵巧手-物体交互的转变充满挑战，因为几何轨迹回放或开环执行无法建模驱动可活动部件所需的接触动力学。此外，仅针对固定动力学条件下任务完成训练的策略会过度拟合标称接触载荷，尤其在缺乏触觉或力反馈时，当接触载荷发生变化时性能可能下降。为解决这些挑战，我们提出DragMesh-2——一个面向可活动物体的接触驱动灵巧交互框架，它将可活动交互从以物体为中心的生成扩展到以手驱动的灵巧交互场景，使得可活动运动必须通过物理接触产生。我们进一步提出PICA——一种物理信息注入的接触感知训练机制，在不依赖触觉或力反馈的条件下将物理信号融入策略学习，从而提升接触载荷变化时的鲁棒性与任务成功率。最后，我们在多种阻尼条件和可活动物体类别上进行系统评估以研究接触载荷变化下的鲁棒性，并提供纯几何灵巧交互资源以支持未来移动操作与类人手-物体交互研究。在七个GAPartNet物体上，DragMesh-2在接触载荷变化下展现出强于对比方法的鲁棒性，同时在各类阻尼条件下保持高任务成功率。