Learning Data-Efficient Rigid-Body Contact Models: Case Study of Planar Impact


Nima Fazeli, Samuel Zapolsky, Evan Drumwright, Alberto Rodriguez ;
Proceedings of the 1st Annual Conference on Robot Learning, PMLR 78:388-397, 2017.


In this paper we demonstrate the limitations of common rigid-body contact models used in the robotics community by comparing them to a collection of data-driven and data-reinforced models that exploit underlying structure inspired by the rigid contact paradigm. We evaluate and compare the analytical and data-driven contact models on an empirical planar impact data-set, and show that the learned models are able to outperform their analytical counterparts with a small training set.

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