This is an extended repo for the paper Data-efficient model learning and prediction for contact-rich manipulation tasks, Khader, S. A., Yin, H., Falco, P., & Kragic, D. (2020), IEEE Robotics and Automation Letters. [IEEE] [arXiv]

See here for the main repo.

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.

This repo implements the baseline methods in the paper: standard Gaussian process, manifold Gaussian process and ensemble-based uncertainty-aware neural network.