Data-efficient Model Learning and Prediction for Contact-rich Manipulation Tasks
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by
Shahbaz Abdul Khader, Hang Yin, Pietro Falco, Danica Kragic
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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