DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynamic
  Manipulation of Deformable Linear Objects
Sun Zhaole Jihong Zhu Robert B. Fisher
Deformable linear object (DLO) manipulation is needed in many fields.
Previous research on deformable linear object (DLO) manipulation has primarily
involved parallel jaw gripper manipulation with fixed grasping positions.
However, the potential for dexterous manipulation of DLOs using an
anthropomorphic hand is under-explored. We present DexDLO, a model-free
framework that learns dexterous dynamic manipulation policies for deformable
linear objects with a fixed-base dexterous hand in an end-to-end way. By
abstracting several common DLO manipulation tasks into goal-conditioned tasks,
our DexDLO can perform these tasks, such as DLO grabbing, DLO pulling, DLO
end-tip position controlling, etc. Using the Mujoco physics simulator, we
demonstrate that our framework can efficiently and effectively learn five
different DLO manipulation tasks with the same framework parameters. We further
provide a thorough analysis of learned policies, reward functions, and reduced
observations for a comprehensive understanding of the framework.