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arXiv · 1909.04915

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

Shahbaz Abdul Khader·Hang Yin·Pietro Falco·Danica Kragic
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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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Shahbaz Abdul Khader, Hang Yin, Pietro Falco, Danica Kragic. 2019-09-11. Data-efficient Model Learning and Prediction for Contact-rich Manipulation Tasks. https://doi.org/10.1109/lra.2020.2996067

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