通过视觉估计柔性，让机器人学会插入各类弹性线状物。
Learning for Deformable Linear Object Insertion Leveraging Flexibility Estimation from Visual Cues
- 用视觉分析判断线状物柔性，替代繁琐的物理测量。
- 模拟与真实场景中成功率分别达85.6%和66.67%。
- 适合需处理多种材质柔性物体的机器人操作任务。
柔性线状物体（如铁丝、橡胶、丝绸、尼龙绳）在日常生活中普遍存在，其物理特性（如杨氏模量、抗弯刚度）差异大，给通用操控策略开发带来挑战。以往研究多局限于单一材料，且依赖耗时的状态数据采集。本文提出一种两阶段操控方法：先通过仿真生成真值柔性数据训练视觉估计模块，再利用强化学习训练基于估计柔性的插入策略。机器人通过交互分析视觉构型实时估算柔性，进而执行复杂插入任务。该方法在多样化场景下，模拟成功率85.6%，真实机器人实验成功率达66.67%。
原文摘要 · Abstract (English)
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/
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