Pseudo-rigid body networks: learning interpretable deformable object dynamics from partial observations

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 9542–9548

Abstract

Accurately predicting deformable linear object (DLO) dynamics is challenging, especially when the task requires a model that is both human-interpretable and computationally efficient. In this work, we draw inspiration from the pseudo-rigid body method (PRB) and model a DLO as a serial chain of rigid bodies whose internal state is unrolled through time by a dynamics network. This dynamics network is trained jointly with a physics-informed encoder that maps observed motion variables to the DLO's hidden state. To encourage the state to acquire a physically meaningful representation, we leverage the forward kinematics of the PRB model as a decoder. We demonstrate in robot experiments that the proposed DLO dynamics model provides physically interpretable predictions from partial observations while being on par with black-box models regarding prediction accuracy. The project code is available at: tinyurl.com/prb-networks

Authors 4

1. Shamil Mamedov

   KU Leuven

   Affiliation as printed

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

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

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute for Data Science in Mechanical Engineering

3. Jan Swevers

   KU Leuven

   Affiliation as printed

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

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

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute for Data Science in Mechanical Engineering