Learning Deformable Linear Object Dynamics From a Single Trajectory

IEEE Robotics and Automation Letters, vol. 10, pp. 7635–7642

Abstract

The dynamic manipulation of deformable objects poses a significant challenge in robotics. While model-based approaches for controlling such objects hold significant potential, their effectiveness hinges on the availability of an accurate and computationally efficient dynamics model. This work focuses on sample-efficient learning of models to capture the dynamic behavior of deformable linear objects (DLOs). Inspired by the pseudo-rigid body method, we present a physics-informed neural ODE that approximates a DLO as a serial chain of rigid bodies interconnected by passive elastic joints. However, unlike traditional uniform spatial discretization and linear springdamper joints, our approach involves learning-based discretization and nonlinear elastic joints that characterize interaction forces via a neural network. Through real-world and simulation experiments involving DLOs with markedly different physical properties, we demonstrate the model's ability to accurately predict DLO motion.

Authors 4

1. Shamil Mamedov

   KU Leuven

   Affiliation as printed

   MECO Research Team, KU Leuven and Flanders Make@KU Leuven, Heverlee, Belgium

2. A. René Geist Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Data Science in Mechanical Engineering, RWTH Aachen University, Aachen, Germany

3. Sebastian Trimpe Aachen Institute for Data Science in Mechanical Engineering

   RWTH Aachen University

   Affiliation as printed

   Institute for Data Science in Mechanical Engineering, RWTH Aachen University, Aachen, Germany

4. Jan Swevers

   KU Leuven

   Affiliation as printed

   MECO Research Team, KU Leuven and Flanders Make@KU Leuven, Heverlee, Belgium