実データで強化した剛体接触モデル
Data-Augmented Contact Model for Rigid Body Simulation
解析的な接触制約を保ちつつ観測データから接触挙動を学習し、剛体の跳ね・滑り・回転を予測する接触インパルスモデルを提案した論文。
著者: Yifeng Jiang, Jiazheng Sun, C. Karen Liu
分類: cs.RO, cs.LG
原文アブストラクト
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
