End-to-end differentiable physics for learning and control

End-to-end differentiable physics for learning and control

© 2018 Curran Associates Inc.All rights reserved. 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 observa...

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Bibliographic Details
Main Authors:	Smith, Kevin A, Allen, Kelsey Rebecca, Tenenbaum, Joshua B
Other Authors:	Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Format:	Article
Language:	English
Published:	Curran Associates Inc 2020
Online Access:	https://hdl.handle.net/1721.1/126615

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