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The DIAMOND Model: Deep Recurrent Neural Networks for Self-Organizing Robot Control.

Simón C Smith¹, Richard Dharmadi¹, Calum Imrie¹

¹Institute of Perception, Action and Behaviour (IPAB), School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.

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|October 12, 2020

Summary

This summary is machine-generated.

This study introduces a brain-inspired robotic control architecture using deep learning and predictive coding. Deeper networks within this architecture facilitate more complex exploratory behaviors in robots.

Keywords:

autonomous learning deep neural networks homeokinesis robot control self-organizing control

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Area of Science:

Robotics
Neuroscience
Artificial Intelligence

Background:

Robotic sensorimotor control traditionally relies on complex programming.
Brain-inspired computing offers novel approaches to autonomous systems.
Predictive coding is a framework for understanding brain function.

Purpose of the Study:

To propose a novel brain-inspired architecture for robotic sensorimotor control.
To integrate predictive coding principles with deep learning for robotics.
To investigate the role of network depth in enabling complex robotic behaviors.

Main Methods:

Developed a multi-layered recurrent neural network architecture.
Implemented a homeokinetic learning rule for spontaneous network activity.
Utilized robotic simulations to test and illustrate the network's functionality.

Main Results:

The proposed architecture successfully demonstrated robotic sensorimotor control.
Simulations showed that the homeokinetic learning rule supports self-organized behavior generation.
Evidence indicated that deeper network configurations lead to enhanced exploratory capabilities.

Conclusions:

The brain-inspired architecture offers a promising approach to robotic control.
Predictive coding and deep learning integration can yield sophisticated robotic behaviors.
Network depth is a critical factor for enabling complex, adaptive robotic exploration.