Using visual model-based learning for deformable object manipulation is challenging due to difficulties in learning plannable visual representations along with complex dynamic models. In this work, we propose a new learning framework that jointly optimizes both the visual representation model and the dynamics model using contrastive estimation. Using simulation data collected by randomly perturbing deformable objects on a table, we learn latent dynamics models for these objects in an offline fashion. Then, using the learned models, we use simple model-based planning to solve challenging deformable object manipulation tasks such as spreading ropes and cloths. Experimentally, we show substantial improvements in performance over standard model-based learning techniques across our rope and cloth manipulation suite. Finally, we transfer our visual manipulation policies trained on data purely collected in simulation to a real PR2 robot through domain randomization.
@article{arxiv.2003.05436,
title = {Learning Predictive Representations for Deformable Objects Using Contrastive Estimation},
author = {Wilson Yan and Ashwin Vangipuram and Pieter Abbeel and Lerrel Pinto},
journal= {arXiv preprint arXiv:2003.05436},
year = {2020}
}