Learning Predictive Representations for Deformable Objects Using Contrastive Estimation

Wilson Yan; Ashwin Vangipuram; Pieter Abbeel; Lerrel Pinto

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arXiv · 2003.05436

Learning Predictive Representations for Deformable Objects Using Contrastive Estimation

Wilson Yan·Ashwin Vangipuram·Pieter Abbeel·Lerrel Pinto

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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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cs.LG cs.CV cs.RO stat.ML

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Wilson Yan, Ashwin Vangipuram, Pieter Abbeel, Lerrel Pinto. 2020-03-11. Learning Predictive Representations for Deformable Objects Using Contrastive Estimation. https://arxiv.org/abs/2003.05436

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