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dc.contributor.authorAmato, Giuseppe
dc.contributor.authorBacciu, D.
dc.contributor.authorBroxvall, M.
dc.contributor.authorChessa, S.
dc.contributor.authorColeman, S.
dc.contributor.authorDi Rocco, M.
dc.contributor.authorDragone, Mauro
dc.contributor.authorGallicchio, Claudio
dc.contributor.authorGennaro, Claudio
dc.contributor.authorLozano, Hector
dc.contributor.authorMcGinnity, T.M.
dc.contributor.authorMicheli, Alessio
dc.contributor.authorRay, A.K.
dc.contributor.authorRenteria, Arantxa
dc.contributor.authorSaffiotti, A.
dc.contributor.authorSwords, D.
dc.contributor.authorVairo, Claudio
dc.contributor.authorVance, P.
dc.date.accessioned2016-04-05T10:28:05Z
dc.date.available2016-04-05T10:28:05Z
dc.date.issued2015-12
dc.identifier.citationJOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, Vol. 80, pp. S57-S81, suplement 1, special issue, 2015en
dc.identifier.issn1573-0409en
dc.identifier.urihttp://hdl.handle.net/11556/177
dc.description.abstractRobotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent-based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a proof of concept smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feedback received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future worken
dc.description.sponsorshipEuropean Comission, FP7 Programmeen
dc.language.isoengen
dc.publisherSPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDSen
dc.titleRobotic Ubiquitous Cognitive Ecology for Smart Homesen
dc.typearticleen
dc.identifier.doi10.1007/s10846-015-0178-2en
dc.isiYesen
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7/269914/EU/Robotics UBIquitous COgnitive Network/RUBICONen
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsAmbient assisted livingen
dc.subject.keywordsRobotic ecologyen
dc.subject.keywordsNetworked roboticsen
dc.subject.keywordsCognitive roboticsen
dc.subject.keywordsWireless sensor and actuator networksen
dc.subject.keywordsHome automationen
dc.subject.keywordsActivity recognitionen
dc.subject.keywordsActivity discoveryen


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