End-to-End Differentiable Physics for Learning and Control

NeurIPS 2018 Filipe De Avila Belbute-PeresKevin SmithKelsey AllenJosh TenenbaumJ. Zico Kolter

We present a differentiable physics engine that can be integrated as a module in deep neural networks for end-to-end learning. As a result, structured physics knowledge can be embedded into larger systems, allowing them, for example, to match observations by performing precise simulations, while achieves high sample efficiency... (read more)

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