Learning action oriented perceptual features for robot navigation

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DC Field	Value	Language
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.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.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.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	-
Appears in Collections:	LS 08 Künstliche Intelligenz

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