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.

翻译：可变形线性物体（DLO）的操作在众多领域具有重要需求。现有研究主要采用平行夹爪在固定抓取位置进行可变形线性物体操作，但利用仿人手对可变形线性物体进行灵巧操作的潜力尚未得到充分探索。我们提出DexDLO——一种无模型框架，通过端到端方式学习基于固定基座灵巧手的可变形线性物体动态操作策略。通过将多种常见可变形线性物体操作任务抽象为目标条件化任务，DexDLO可执行可变形线性物体抓取、牵引、末端位置控制等任务。利用Mujoco物理仿真器，我们证明了该框架在统一参数设置下能够高效完成五种不同可变形线性物体操作任务。为进一步全面理解该框架，我们提供了对学习策略、奖励函数及简化观测值的深入分析。