We study the problem of learning graph dynamics of deformable objects that generalizes to unknown physical properties. Our key insight is to leverage a latent representation of elastic physical properties of cloth-like deformable objects that can be extracted, for example, from a pulling interaction. In this paper we propose EDO-Net (Elastic Deformable Object - Net), a model of graph dynamics trained on a large variety of samples with different elastic properties that does not rely on ground-truth labels of the properties. EDO-Net jointly learns an adaptation module, and a forward-dynamics module. The former is responsible for extracting a latent representation of the physical properties of the object, while the latter leverages the latent representation to predict future states of cloth-like objects represented as graphs. We evaluate EDO-Net both in simulation and real world, assessing its capabilities of: 1) generalizing to unknown physical properties, 2) transferring the learned representation to new downstream tasks.
@article{arxiv.2209.08996,
title = {EDO-Net: Learning Elastic Properties of Deformable Objects from Graph Dynamics},
author = {Alberta Longhini and Marco Moletta and Alfredo Reichlin and Michael C. Welle and David Held and Zackory Erickson and Danica Kragic},
journal= {arXiv preprint arXiv:2209.08996},
year = {2024}
}