DragMesh-2：具有物理真实性的灵巧手与可关节式物体的交互
DragMesh-2: Physically Plausible Dexterous Hand-Object Interaction with Articulated Objects
摘要
与可动物体进行灵巧互动对于家庭使用、辅助操作以及人形机器人操作来说非常重要。在这种操作中，多指手可以实现不同于平行下颌式抓取的灵活接触方式。不过，与静态物体的操作不同，可动物体的目标部分无法被直接操控，其运动必须通过持续的手部与物体表面的接触来实现。因此，从以物体为中心的动态生成方式转变为以手部驱动的灵巧互动方式并非易事，因为几何轨迹的重复或开环执行方式无法模拟移动可动物体所需的接触动力学机制。此外，那些仅针对固定动力学条件下的任务完成而训练的模型容易过度拟合正常的接触载荷，尤其是在没有触觉或力反馈的情况下；当接触载荷发生变化时，这些模型的性能也会下降。为了应对这些挑战，我们提出了DragMesh-2这一基于接触的灵巧互动框架，它将可动物体的互动方式从以物体为中心的动态生成方式转变为以手部驱动的灵巧互动方式，此时可动物体的运动必须通过物理接触来实现。我们还提出了PICA这一基于物理信息的、能够感知接触状态的训练机制，该机制可以在没有触觉或力反馈的情况下将物理信号引入策略学习中，从而提升系统在变化中的鲁棒性以及任务完成率。最后，我们在多种阻尼条件和不同类型的可动物体上进行了系统评估，以研究系统在接触载荷变化下的鲁棒性，同时提供了一种纯几何形状的灵巧互动工具，以支持未来的人形机器人与物体之间的交互研究。在七个GAPartNet物体上，DragMesh-2在接触载荷变化的情况下表现出更强的鲁棒性，同时在不同阻尼条件下仍能保持较高的任务完成率。
English Abstract
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.