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arXiv · 2007.06045

Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap

Eric Heiden·David Millard·Erwin Coumans·Gaurav S. Sukhatme
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Abstract

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.

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Eric Heiden, David Millard, Erwin Coumans, Gaurav S. Sukhatme. 2020-07-12. Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap. https://arxiv.org/abs/2007.06045

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