DexDLO：学习用于可变形线性物体动态操作的目标条件灵巧策略
DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynamic Manipulation of Deformable Linear Objects
AI总结：
DexDLO提出一种无模型端到端框架，利用固定底座灵巧手学习可变形线性物体的目标条件动态操作策略，在Mujoco中统一参数高效完成五种操作任务，并深入分析策略与奖励设计。
摘要：
可变形线性物体（DLO）的操作在许多领域都是必需的。以往关于可变形线性物体（DLO）操作的研究主要涉及使用固定抓取位置的平行夹爪操作。然而，使用拟人手对DLO进行灵巧操作的潜力尚未得到充分探索。我们提出了DexDLO，这是一个无模型框架，以端到端的方式学习使用固定底座灵巧手对可变形线性物体进行灵巧动态操作策略。通过将几个常见的DLO操作任务抽象为目标条件任务，我们的DexDLO可以执行这些任务，例如DLO抓取、DLO拉拽、DLO末端位置控制等。使用Mujoco物理模拟器，我们证明了我们的框架可以在使用相同框架参数的情况下，高效且有效地学习五个不同的DLO操作任务。我们进一步对学习到的策略、奖励函数和缩减观测进行了深入分析，以全面理解该框架。
英文摘要：
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.
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。
