Title: Data-Augmented Contact Model for Rigid Body Simulation,
Abstract: 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,
removing the exponential decline in accuracy as the number of contact points
increases.