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We present a differentiable simulation architecture for articulatedrigid-body dynamics that enables the augmentation of analytical models withneural networks at any point of the computation. Through gradient-basedoptimization, identification of the simulation parameters and network weightsis performed efficiently in preliminary experiments on a real-world dataset andin sim2sim transfer applications, while poor local optima are overcome througha random search approach.

Augmenting Differentiable Simulators with Neural Networks to Close the Sim2Real Gap