Learning Graph Dynamics With External Contact for Deformable Linear Objects Shape Control

This letter focuses on the shape control manipulation of deformable linear objects (DLO) with a dual-arm robotic system. One significant challenge of DLO shape control is the underactuated control system, which means that finite robotic manipulators can not fully control DLO's shape due to the lack of sufficient constraints on DLO. We propose a novel DLO shape control framework aiming to stably control DLO's shape in a wide deformation range, which innovatively provides additional constraints on DLO via external contact. The proposed framework consists of a learning module named Learning Graph Dynamics with External Contact for deformable linear objects (LG-DEC) and an action generation module. The LG-DEC uses a graph convolution network to approximate the dynamics between DLO and contactable objects based on keypoint-based representation. In the action generation module, the learned graph dynamics is used to predict whether the randomly sampled actions can drive DLO closer to the desired shape and output the best action for DLO shape control. In simulation, the proposed framework accomplishes the DLO shape control task with external contact in six scenarios. Furthermore, the proposed framework can be well applied to a physical robotic system for the shape control on different types of DLO.

Automated summary

This letter focuses on the shape control manipulation of deformable linear objects (DLO) with a dual-arm robotic system. One significant challenge of DLO shape control is the underactuated control system, which means that finite robotic manipulators can not fully control DLO's shape due to the lack of sufficient constraints on DLO. We propose a novel DLO shape control framework aiming to stably control DLO's shape in a wide deformation range, which innovatively provides additional constraints on DLO via external contact.