  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.
