Data-Augmented Contact Model for Rigid Body Simulation

Yifeng Jiang, Jiazheng Sun, C. Karen Liu
Proceedings of The 4th Annual Learning for Dynamics and Control Conference, PMLR 168:378-390, 2022.

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, maintaining same simulation speed as the number of contact points increases for detailed geometries. Supplemental video: https://shorturl.at/eilwX

Cite this Paper

BibTeX
@InProceedings{pmlr-v168-jiang22a, title = {Data-Augmented Contact Model for Rigid Body Simulation}, author = {Jiang, Yifeng and Sun, Jiazheng and Liu, C. Karen}, booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference}, pages = {378--390}, year = {2022}, editor = {Firoozi, Roya and Mehr, Negar and Yel, Esen and Antonova, Rika and Bohg, Jeannette and Schwager, Mac and Kochenderfer, Mykel}, volume = {168}, series = {Proceedings of Machine Learning Research}, month = {23--24 Jun}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v168/jiang22a/jiang22a.pdf}, url = {https://proceedings.mlr.press/v168/jiang22a.html}, 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, maintaining same simulation speed as the number of contact points increases for detailed geometries. Supplemental video: https://shorturl.at/eilwX} }
Endnote
%0 Conference Paper %T Data-Augmented Contact Model for Rigid Body Simulation %A Yifeng Jiang %A Jiazheng Sun %A C. Karen Liu %B Proceedings of The 4th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2022 %E Roya Firoozi %E Negar Mehr %E Esen Yel %E Rika Antonova %E Jeannette Bohg %E Mac Schwager %E Mykel Kochenderfer %F pmlr-v168-jiang22a %I PMLR %P 378--390 %U https://proceedings.mlr.press/v168/jiang22a.html %V 168 %X 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
APA
Jiang, Y., Sun, J. & Liu, C.K.. (2022). Data-Augmented Contact Model for Rigid Body Simulation. Proceedings of The 4th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 168:378-390 Available from https://proceedings.mlr.press/v168/jiang22a.html.

Related Material