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

arXiv: https://arxiv.org/abs/1710.05947 · HTML: https://mlsanity.com/paper/1710.05947/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2017.

## Built on by (most cited first, 2 of 2)

- [Residual Model Learning for Microrobot Control](https://mlsanity.com/paper/2104.00631/) (2021)
- [Learning Reactive and Predictive Differentiable Controllers for Switching Linear Dynamical Models](https://mlsanity.com/paper/2103.14256/) (2021)

## Abstract (arXiv)

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.