Closed-Loop Trajectory Optimization of Deformable Linear Objects for Dynamic Motions

Abstract

Tracking dynamic endpoint trajectories of deformable linear objects (DLOs) with a robotic manipulator remains challenging due to their complex non-linear behavior. While closed-loop Model Predictive Control (MPC) can account for these non-linearities, it requires an accurate dynamic model and precise state estimation. This paper introduces a closed-loop approach for controlling a DLO's endpoint to track dynamic 2D shapes. We model the DLO as a floating-base kinematic chain and present a new perspective on learning its dynamics using a data-driven approximation of its hybrid dynamics. Based on this model, we formulate an Optimal Control Problem (OCP), which we solve within the control loop using both linear MPC and DDP. We validate our approach with simulation and hardware experiments, demonstrating its ability to track dynamic endpoint motions.