Most research on deformable linear object (DLO) manipulation assumes rigid grasping. However, beyond rigid grasping and re-grasping, in-hand following is also an essential skill that humans use to dexterously manipulate DLOs, which requires continuously changing the grasp point by in-hand sliding while holding the DLO to prevent it from falling. Achieving such a skill is very challenging for robots without using specially designed but not versatile end-effectors. Previous works have attempted using generic parallel grippers, but their robustness is unsatisfactory owing to the conflict between following and holding, which is hard to balance with a one-degree-of-freedom gripper. In this work, inspired by how humans use fingers to follow DLOs, we explore the usage of a generic dexterous hand with tactile sensing to imitate human skills and achieve robust in-hand DLO following. To enable the hardware system to function in the real world, we develop a framework that includes Cartesian-space arm-hand control, tactile-based in-hand 3-D DLO pose estimation, and task-specific motion design. Experimental results demonstrate the significant superiority of our method over using parallel grippers, as well as its great robustness, generalizability, and efficiency.

翻译：大多数关于可变形线性物体（DLO）操作的研究假设采用刚性抓取。然而，除了刚性抓取和重新抓取外，手内跟随也是人类灵巧操作DLO时使用的一项基本技能，这要求通过手内滑动持续改变抓取点，同时保持对DLO的握持以防止其掉落。对于机器人而言，若不使用专门设计但非通用的末端执行器，实现这种技能极具挑战性。先前的研究尝试使用通用平行夹爪，但由于跟随与握持之间的矛盾，在单自由度夹爪中难以平衡，导致其鲁棒性不理想。本项工作受人类利用手指跟随DLO方式的启发，探索使用配备触觉感知的通用灵巧手来模仿人类技能，以实现鲁棒的DLO手内跟随。为使硬件系统在实际环境中运行，我们开发了一个框架，包括笛卡尔空间的手臂-手部控制、基于触觉的手内三维DLO姿态估计以及特定任务的动作设计。实验结果表明，我们的方法相较于使用平行夹爪具有显著优越性，同时展现出强大的鲁棒性、泛化性和高效性。