  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.
