Learning-in-the-loop optimization: End-to-end control and co-design of soft robots through learned deep latent representations
Name
NeurIPS-2019-learning-in-the-loop-optimization-end-to-end-control-and-co-design-of-soft-robots-through-learned-deep-latent-representations-Paper.pdf
Description
Published version
Size
10.14 MB
Format
Adobe PDF
Checksum (MD5)
96be1edf5fc1d81f3a9b59a4b7259a55
Author(s)
Spielberg, Andrew
Zhao, Allan
Du, Tao
Hu, Yuanming
Rus, Daniela L
Matusik, Wojciech
Date Issued
2019
Journal
Advances in Neural Information Processing Systems
Citation
Spielberg, Andrew et al. "Learning-in-the-loop optimization: End-to-end control and co-design of soft robots through learned deep latent representations." Advances in Neural Information Processing Systems 32 (2019). © 2019 Neural information processing systems foundation. All rights reserved.
Version
Final published version
Abstract
Soft robots have continuum solid bodies that can deform in an infinite number of ways. Controlling soft robots is very challenging as there are no closed form solutions. We present a learning-in-the-loop co-optimization algorithm in which a latent state representation is learned as the robot figures out how to solve the task. Our solution marries hybrid particle-grid-based simulation with deep, variational convolutional autoencoder architectures that can capture salient features of robot dynamics with high efficacy. We demonstrate our dynamics-aware feature learning algorithm on both 2D and 3D soft robots, and show that it is more robust and faster converging than the dynamics-oblivious baseline. We validate the behavior of our algorithm with visualizations of the learned representation.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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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/2019/hash/438124b4c06f3a5caffab2c07863b617-Abstract.html
