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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Curran Associates Inc
2020
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| Online Access: | https://hdl.handle.net/1721.1/126615 |