Publication:
Online feature extraction for the incremental learning of gestures in human-swarm interaction

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dc.citedby6
dc.contributor.authorNagi J.en_US
dc.contributor.authorGiusti A.en_US
dc.contributor.authorNagi F.en_US
dc.contributor.authorGambardella L.M.en_US
dc.contributor.authorDi Caro G.A.en_US
dc.contributor.authorid25825455100en_US
dc.contributor.authorid23392613000en_US
dc.contributor.authorid56272534200en_US
dc.contributor.authorid35600356600en_US
dc.contributor.authorid6603204674en_US
dc.date.accessioned2023-05-16T02:45:47Z
dc.date.available2023-05-16T02:45:47Z
dc.date.issued2014
dc.description.abstractWe present a novel approach for the online learning of hand gestures in swarm robotic (multi-robot) systems. We address the problem of online feature learning by proposing Convolutional Max-Pooling (CMP), a simple feed-forward two-layer network derived from the deep hierarchical Max-Pooling Convolutional Neural Network (MPCNN). To learn and classify gestures in an online and incremental fashion, we employ a 2nd order online learning method, namely the Soft-Confidence Weighted (SCW) learning scheme. In order for all robots to collectively take part in the learning and recognition task and obtain a swarm-level classification, we build a distributed consensus by fusing the individual decision opinions of robots together with the individual weights generated from multiple classifiers. Accuracy, robustness, and scalability of obtained solutions have been verified through emulation experiments performed on a large data set of real data acquired by a networked swarm of robots. © 2014 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6907338
dc.identifier.doi10.1109/ICRA.2014.6907338
dc.identifier.epage3338
dc.identifier.scopus2-s2.0-84929191704
dc.identifier.spage3331
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84929191704&doi=10.1109%2fICRA.2014.6907338&partnerID=40&md5=039f82241da3af5cc500de59be7b2b94
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/21867
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleProceedings - IEEE International Conference on Robotics and Automation
dc.titleOnline feature extraction for the incremental learning of gestures in human-swarm interactionen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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