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Towards Autonomous Robot Control via Self-Adapting Recurrent Networks
University of Skövde, School of Humanities and Informatics.
1996 (English)Report (Other academic)
Abstract [en]

In this paper a higher-order recurrent connectionist architecture is used for learning adaptive behaviour in an autonomous robot. This architecture consists of two sub-networks in a master-slave relationship: a function network for the coupling between sensory inputs and motor outputs, and a context network, which dynamically adapts the sensory input weights in order to allow a flexible, context-dependent mapping from percepts to actions. The capabilities of this architecture are demonstrated in a number of action selection experiments with a simulated Khepera robot, and it is argued that the general approach of generically dividing the overall control task between sequentially cascaded context and function learning offers a powerful mechanism for autonomous long- and short-term adaptation of behaviour

Abstract [en]

Annotation: In the Proceedings of the First Euromicro Workshop on Advanced Mobile Robots (EUROBOT '96), IEEE Computer Society Press, Los Alamitos, CA

Place, publisher, year, edition, pages
Skövde: Institutionen för kommunikation och information , 1996.
Series
IKI Technical Reports, HS-IDA-TR-96-009
Keyword [en]
autonomy, complex behaviour, recurrent connectionist networks, subsumption architecture
National Category
Information Science
Identifiers
URN: urn:nbn:se:his:diva-1246OAI: oai:DiVA.org:his-1246DiVA: diva2:2380
Available from: 2008-06-17 Created: 2008-06-17 Last updated: 2010-03-24

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  • apa
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  • de-DE
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Output format
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