Learning for Deformable Linear Object Insertion Leveraging Flexibility Estimation from Visual Cues

TL;DR

This paper introduces a two-stage approach for deformable linear object insertion that estimates material flexibility from visual cues and learns insertion policies, achieving high success rates in simulation and real-world tests.

Contribution

It presents a novel flexibility estimation method from visual data and integrates it into reinforcement learning for generalized DLO insertion tasks.

Findings

85.6% success rate in simulation

66.67% success rate in real robot experiments

Effective generalization across diverse DLO materials

Abstract

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/