End-to-end differentiable physics for learning and control
Name
7948-end-to-end-differentiable-physics-for-learning-and-control.pdf
Description
Published version
Size
794.17 KB
Format
Adobe PDF
Checksum (MD5)
cd672a4fabf3f2d079f8a38ddea183fb
Author(s)
Smith, Kevin A
Allen, Kelsey Rebecca
Tenenbaum, Joshua B
Date Issued
December 2018
Journal
32nd Conference on Neural Information Processing Systems (NeurIPS 2018)
Publisher
Curran Associates Inc
Citation
Belbute-Peres, Filipe de A. et al. “End-to-end differentiable physics for learning and control.” Paper presented at the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Dec 3-8 2018, Curran Associates Inc © 2018 The Author(s)
Version
Final published version
Abstract
© 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 observations by performing precise simulations, while achieves high sample efficiency. Specifically, in this paper we demonstrate how to perform backpropagation analytically through a physical simulator defined via a linear complementarity problem. Unlike traditional finite difference methods, such gradients can be computed analytically, which allows for greater flexibility of the engine. Through experiments in diverse domains, we highlight the system's ability to learn physical parameters from data, efficiently match and simulate observed visual behavior, and readily enable control via gradient-based planning methods. Code for the engine and experiments is included with the paper.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/7948-end-to-end-differentiable-physics-for-learning-and-control
