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.
Citation
Smith, S. C., Dharmadi, R., Imrie, C., Si, B., & Herrmann, J. M. (2020). The DIAMOND Model: Deep Recurrent Neural Networks for Self-Organizing Robot Control. Frontiers in Neurorobotics, 14, Article 62. https://doi.org/10.3389/fnbot.2020.00062
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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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
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