# Data-Augmented Contact Model for Rigid Body Simulation

arXiv: https://arxiv.org/abs/1803.04019 · HTML: https://mlsanity.com/paper/1803.04019/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2018.

## Built on by (most cited first, 6 of 6)

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- [Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models](https://mlsanity.com/paper/2102.06794/) (2021)
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- [NeuralActuator: Neural Actuation Modeling for Robot Dynamics and External Force Perception](https://mlsanity.com/paper/2607.11734/) (2026)

## Abstract (arXiv)

Accurately modeling contact behaviors for real-world, near-rigid materials remains a grand challenge for existing rigid-body physics simulators. This paper introduces a data-augmented contact model that incorporates analytical solutions with observed data to predict the 3D contact impulse which could result in rigid bodies bouncing, sliding or spinning in all directions. Our method enhances the expressiveness of the standard Coulomb contact model by learning the contact behaviors from the observed data, while preserving the fundamental contact constraints whenever possible. For example, a classifier is trained to approximate the transitions between static and dynamic frictions, while non-penetration constraint during collision is enforced analytically. Our method computes the aggregated effect of contact for the entire rigid body, instead of predicting the contact force for each contact point individually, maintaining same simulation speed as the number of contact points increases for detailed geometries. Supplemental video: https://shorturl.at/eilwX Keywords: Physics Simulation Algorithms, Dynamics Learning, Contact Learning