使用带有触觉感知的灵巧手指进行可变形线性物体的手内跟随
In-Hand Following of Deformable Linear Objects Using Dexterous Fingers with Tactile Sensing
AI总结：
针对可变形线性物体操作，本研究提出一种基于触觉感知的通用灵巧手框架，通过臂手控制、3-D位姿估计与运动设计实现鲁棒的手内跟随，显著优于平行夹爪。
摘要：
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之外，手内跟随也是人类灵巧操作DLO的一项基本技能，该技能要求在握住DLO以防掉落的同时，通过手内滑动连续改变抓取点。如果不使用特殊设计但缺乏通用性的末端执行器，机器人要实现这种技能极具挑战性。以往研究尝试使用通用平行夹爪，但由于跟随与握持之间的冲突难以用单自由度夹爪平衡，其鲁棒性不尽如人意。在本研究中，受人类使用手指跟随DLO的启发，我们探索使用带有触觉感知的通用灵巧手来模仿人类技能，实现鲁棒的手内DLO跟随。为使硬件系统能在真实世界中运行，我们开发了一个框架，包含笛卡尔空间臂手控制、基于触觉的手内3-D DLO位姿估计以及特定任务的运动设计。实验结果证明了我们的方法相比使用平行夹爪具有显著优越性，并展现出极大的鲁棒性、泛化能力和效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
