Simón C. Smith
The DIAMOND Model: Deep Recurrent Neural Networks for Self-Organizing Robot Control
Smith, Simón C.; Dharmadi, Richard; Imrie, Calum; Si, Bailu; Herrmann, J. Michael
Authors
Richard Dharmadi
Calum Imrie
Bailu Si
J. Michael Herrmann
Abstract
The proposed architecture applies the principle of predictive coding and deep learning in a brain-inspired approach to robotic sensorimotor control. It is composed of many layers each of which is a recurrent network. The component networks can be spontaneously active due to the homeokinetic learning rule, a principle that has been studied previously for the purpose of self-organized generation of behavior. We present robotic simulations that illustrate the function of the network and show evidence that deeper networks enable more complex exploratory behavior.
Journal Article Type | Article |
---|---|
Acceptance Date | Aug 3, 2020 |
Online Publication Date | Sep 15, 2020 |
Publication Date | 2020-09 |
Deposit Date | Jul 11, 2023 |
Publicly Available Date | Jul 13, 2023 |
Journal | Frontiers in Neurorobotics |
Publisher | Frontiers Media |
Peer Reviewed | Peer Reviewed |
Volume | 14 |
Article Number | 62 |
DOI | https://doi.org/10.3389/fnbot.2020.00062 |
Keywords | deep neural networks, autonomous learning, homeokinesis, self-organizing control, robot control |
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The DIAMOND Model: Deep Recurrent Neural Networks for Self-Organizing Robot Control
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http://creativecommons.org/licenses/by/4.0/
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