Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap

• URL: http://arxiv.org/abs/2007.06045v1
• Date: Sun, 12 Jul 2020 17:27:11 GMT
• Title: Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap
• Authors: Eric Heiden, David Millard, Erwin Coumans, Gaurav S. Sukhatme
• Abstract summary: 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.
• Score: 15.1962264049463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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