让机械手像人一样拧瓶盖：不靠触觉也能稳
机器人抓个杯子不难，但拧开瓶盖、转动门把手这类需要持续接触的活，一直做不好——因为手和物体之间的力会变，而机器人一旦按固定套路来，力一变就滑脱。这篇研究让机械手学会靠接触本身来驱动关节运动，而不是预先规划轨迹。更关键的是，他们发明了一种训练方法PICA，让机器人即使没有触觉传感器，也能通过物理信号（比如关节力矩）感知接触力的变化，自动调整抓握力度。在7种常见铰接物体（如抽屉、剪刀）上测试，面对不同阻尼（松紧程度），成功率稳定在80%以上，而传统方法在阻尼变化时直接掉到30%。这不是你明天能用的技术，但它让机器人离“能帮老人开瓶盖”更近了一步。
📄 原文摘要(英文)
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.