Learning Predictive Representations for Deformable Objects Using Contrastive Estimation

• URL: http://arxiv.org/abs/2003.05436v1
• Date: Wed, 11 Mar 2020 17:55:15 GMT
• Title: Learning Predictive Representations for Deformable Objects Using Contrastive Estimation
• Authors: Wilson Yan, Ashwin Vangipuram, Pieter Abbeel, Lerrel Pinto
• Abstract summary: We propose a new learning framework that jointly optimize both the visual representation model and the dynamics model. We show substantial improvements over standard model-based learning techniques across our rope and cloth manipulation suite.
• Score: 83.16948429592621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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