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NeuralSim: Augmenting Differentiable Simulators with Neural Networks [article]

Eric Heiden, David Millard, Erwin Coumans, Yizhou Sheng, Gaurav S. Sukhatme
2021 arXiv   pre-print
In this work, we study the augmentation of a novel differentiable rigid-body physics engine via neural networks that is able to learn nonlinear relationships between dynamic quantities and can thus learn  ...  We observe a ten-fold speed-up when replacing the QP solver inside a model-predictive gait controller for quadruped robots with a neural network, allowing us to significantly improve control delays as  ...  Right: augmentation of differentiable simulators with our proposed neural scalar type where variable e becomes a combination of an analytical model φ (·, ·) with inputs a and b, and a neural network whose  ... 
arXiv:2011.04217v2 fatcat:awwqb4fzqzb6piwuxa3zim6xnm