Robust Manipulation of Deformable Linear Objects

Abstract

Robotic manipulation is a fundamental challenge in the pursuit of automating household tasks and advancing robotic manufacturing. Manipulation planning for deformable objects is particularly challenging due to the associated large action spaces and complex dynamics. For deformable objects, some actions are sensitive to noise in the model, which can significantly degrade the accuracy of predicted trajectories. In this letter, we present a motion planning algorithm for arranging deformable linear objects (DLOs) into user-provided goal configurations. Using a novel problem formulation as well as a combination of sampling and gradient based optimization, the algorithm finds sequences of grasps and control inputs that are robust. We demonstrate the effectiveness of the new algorithm using numerical experiments and manipulation examples both in simulation and in real world environments.