Data-efficient Model Learning and Prediction for Contact-rich Manipulation Tasks release_xq6xxvaapjd5hkzjkcpca25c7q

by Shahbaz Abdul Khader, Hang Yin, Pietro Falco, Danica Kragic

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2020  

Abstract

In this letter, we investigate learning forward dynamics models and multi-step prediction of state variables (long-term prediction) for contact-rich manipulation. The problems are formulated in the context of model-based reinforcement learning (MBRL). We focus on two aspects-discontinuous dynamics and data-efficiency-both of which are important in the identified scope and pose significant challenges to State-of-the-Art methods. We contribute to closing this gap by proposing a method that explicitly adopts a specific hybrid structure for the model while leveraging the uncertainty representation and data-efficiency of Gaussian process. Our experiments on an illustrative moving block task and a 7-DOF robot demonstrate a clear advantage when compared to popular baselines in low data regimes.
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Date   2020-09-27
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arXiv  1909.04915v3
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