sim2realarXiv:2007.06045
ニューラルネットワークで微分可能シミュレータを拡張しSim2Realギャップを埋める
Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap
剛体ダイナミクスの微分可能シミュレータに任意の箇所でニューラルネットを組み込み、勾配最適化とランダム探索でパラメータと重みを同定してsim2realギャップを縮小する手法を提案。
著者: Eric Heiden, David Millard, Erwin Coumans, Gaurav S. Sukhatme
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
We present a differentiable simulation architecture for articulated rigid-body dynamics that enables the augmentation of analytical models with neural networks at any point of the computation. Through gradient-based optimization, identification of the simulation parameters and network weights is performed efficiently in preliminary experiments on a real-world dataset and in sim2sim transfer applications, while poor local optima are overcome through a random search approach.
