Learning action oriented perceptual features for robot navigation

dc.contributor.author	Morik, Katharina	de
dc.contributor.author	Rieger, Anke	de
dc.date.accessioned	2004-12-06T12:53:29Z
dc.date.available	2004-12-06T12:53:29Z
dc.date.created	1993	de
dc.date.issued	1999-10-26	de
dc.description.abstract	Machine learning can offer an increase in the flexibility and applicability of robotics at several levels of control. In this paper, we characterize two symbolic learning tasks in the field of robotics. We outline an approach for learning features from sensory data and for using these features to learn more complex ones. We illustrate our approach with first experiments in the field of navigation. The paper is written in English.	en
dc.format.extent	182493 bytes
dc.format.extent	200115 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.issn	0943-4135	de
dc.identifier.uri	http://hdl.handle.net/2003/2578
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-14889
dc.language.iso	en	de
dc.publisher	Universität Dortmund	de
dc.relation.ispartofseries	Forschungsberichte des Lehrstuhls VIII, Fachbereich Informatik der Universität Dortmund ; 3	de
dc.subject.ddc	004	de
dc.title	Learning action oriented perceptual features for robot navigation	en
dc.type	Text	de
dc.type.publicationtype	report
dcterms.accessRights	open access

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