SELF CONSTRUCTING NEURAL NETWORK ROBOT CONTROLLER BASED ON ON-LINE TASK PERFORMANCE FEEDBACK

Andreas Huemer, Mario Gongora, David Elizondo

2008

Abstract

A novel methodology to create a powerful controller for robots that minimises the design effort is presented. We show that using the feedback from the robot itself, the system can learn from experience. A method is presented where the interpretation of the sensory feedback is integrated in the creation of the controller, which is achieved by growing a spiking neural network system. The feedback is extracted from a performance measuring function provided at the task definition stage, which takes into consideration the robot actions without the need for external or manual analysis.

Paper Citation

in Harvard Style

Huemer A., Gongora M. and Elizondo D. (2008). SELF CONSTRUCTING NEURAL NETWORK ROBOT CONTROLLER BASED ON ON-LINE TASK PERFORMANCE FEEDBACK . In Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8111-30-2, pages 326-333. DOI: 10.5220/0001495503260333

in Bibtex Style

@conference{icinco08,
author={Andreas Huemer and Mario Gongora and David Elizondo},
title={SELF CONSTRUCTING NEURAL NETWORK ROBOT CONTROLLER BASED ON ON-LINE TASK PERFORMANCE FEEDBACK},
booktitle={Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2008},
pages={326-333},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001495503260333},
isbn={978-989-8111-30-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - SELF CONSTRUCTING NEURAL NETWORK ROBOT CONTROLLER BASED ON ON-LINE TASK PERFORMANCE FEEDBACK
SN - 978-989-8111-30-2
AU - Huemer A.
AU - Gongora M.
AU - Elizondo D.
PY - 2008
SP - 326
EP - 333
DO - 10.5220/0001495503260333