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

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/

Authors

• Mingen Li
• Changhyun Choi

Keywords

• Visualization
• Pipelines
• Reinforcement learning
• Trajectory
• Wire
• Rubber
• Reliability
• Deformable Objects
• Deformable Linear Objects
• Material Properties
• Bending Stiffness
• Policy Learning
• Properties Of Different Types
• Data Augmentation
• Multilayer Perceptron
• Reward Function
• Markov Decision Process
• Graph Neural Networks
• Observation Space
• Training Policy
• Successful Insertion
• Reinforcement Learning Policy
• Motion Primitives