Learning Data-Efficient Rigid-Body Contact Models: Case Study of Planar Impact
This work addresses the problem of improving contact modeling accuracy for robotics applications, though it is incremental as it builds on existing rigid contact paradigms.
The paper demonstrates that data-driven rigid-body contact models outperform common analytical models in planar impact tasks, achieving better performance with a small training set.
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.