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Towards Autonomous Robot Control via Self-Adapting Recurrent Networks
University of Skövde, Department of Computer Science. Neural Computing Group, Dept. of Computer Science, University of Sheffield, UK. (The Connectionist Research Group)ORCID iD: 0000-0001-6883-2450
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

Place, publisher, year, edition, pages
Skövde: University of Skövde , 1996.
Series
IDA Technical Reports ; HS-IDA-TR-96-009
Keywords [en]
autonomy, complex behaviour, recurrent connectionist networks, subsumption architecture
National Category
Information Systems
Identifiers
URN: urn:nbn:se:his:diva-1246OAI: oai:DiVA.org:his-1246DiVA, id: diva2:2380
Note

HS-IDA-TR-96-009

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

Available from: 2008-06-17 Created: 2008-06-17 Last updated: 2021-05-20Bibliographically approved

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Ziemke, Tom

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