Manipulation of deformable Linear objects (DLOs), including iron wire, rubber, silk, and nylon rope, is ubiquitous in daily life. These objects exhibit diverse physical properties, such as Young$'$s modulus and bending stiffness.Such diversity poses challenges for developing generalized manipulation policies. However, previous research limited their scope to single-material DLOs and engaged in time-consuming data collection for the state estimation. In this paper, we propose a two-stage manipulation approach consisting of a material property (e.g., flexibility) estimation and policy learning for DLO insertion with reinforcement learning. Firstly, we design a flexibility estimation scheme that characterizes the properties of different types of DLOs. The ground truth flexibility data is collected in simulation to train our flexibility estimation module. During the manipulation, the robot interacts with the DLOs to estimate flexibility by analyzing their visual configurations. Secondly, we train a policy conditioned on the estimated flexibility to perform challenging DLO insertion tasks. Our pipeline trained with diverse insertion scenarios achieves an 85.6% success rate in simulation and 66.67% in real robot experiments. Please refer to our project page: https://lmeee.github.io/DLOInsert/

翻译：可变形线性物体（DLOs）（如铁丝、橡胶、丝绸和尼龙绳）的操纵在日常生活中无处不在。这些物体展现出多样的物理特性，例如杨氏模量和弯曲刚度。这种多样性给开发通用操纵策略带来了挑战。然而，先前的研究局限于单一材料的DLOs，并且需要耗时的数据收集进行状态估计。本文提出一种两阶段操纵方法，包含材料属性（如柔性）估计和基于强化学习的DLO插入策略学习。首先，我们设计了一种柔性估计方案，用于表征不同类型DLOs的特性。在仿真中收集真实的柔性数据以训练我们的柔性估计模块。在操纵过程中，机器人通过与DLOs交互并分析其视觉形态来估计柔性。其次，我们训练一个以估计的柔性为条件的策略来执行具有挑战性的DLO插入任务。我们的流程在多样化的插入场景中训练，在仿真中实现了85.6%的成功率，在真实机器人实验中实现了66.67%的成功率。请参阅我们的项目页面：https://lmeee.github.io/DLOInsert/