Towards Spatial Perception: Learning to Locate Objects from Vision #robotDoC

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#robotdoc 2012 towards spatial perception learning to locate objects from vision S. Harding, M. Frank, A. Förster, J. Schmidhuber istituto dalle molle di studi sull’intelligenza artificiale università della svizzera italiana idsia / usi / supsi Jürgen ’Juxi’ Leitner Monday, September 10, 2012

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Transcript of Towards Spatial Perception: Learning to Locate Objects from Vision #robotDoC

Page 1: Towards Spatial Perception: Learning to Locate Objects from Vision #robotDoC

#robotdoc 2012

towardsspatial perception

learning to locate objects from vision

S. Harding, M. Frank, A. Förster, J. Schmidhuber

istituto dalle molle di studi sull’intelligenza artificialeuniversità della svizzera italiana

idsia / usi / supsi

Jürgen ’Juxi’ Leitner

Monday, September 10, 2012

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iCub

thanks to G. Metta, IIT and U.Plymouth for their pictures

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manipulation

our iCubsetup is for

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perceptionvisual

thanks to G. Metta and IIT for this picture

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three parts

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objectsdetecting

Harding et al., GPTP 2012, Leitner et al., ICDL 2012

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Image credit: NASA

on Marsdetecting rocks

Leitner et al., iSAIRAS 2012

also works for

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objectsdetecting

Harding et al., GPTP 2012, Leitner et al., ICDL 2012

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frameworkicVision

Leitner et al., BICA 2012

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three partsGPTP2012, ICDL2012(?) BICA 2012

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locateobjects

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localisation approachescurrent

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approachlearning

+

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setuplearning

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spatial perception neural network

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genetic programmingspatial perception

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genetic programmingspatial perception

x = 17.81 − 0.01906 v1 + 0.1527 v4 + 0.1378 v7 + 0.01108 v10 − 0.0296 v11 − 0.1207 v12

y = 1.124224045 + 0.1295920897 v10 + 0.1156011386 v8 + 0.01695234993 v0

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errorsrelative

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transferringspatial perception

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automate the learning (IROS 2012)different spatial representations

workfuture

eye-hand coordination

“intrinsic motivation” learningonline, continuous learning

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for listeningthanks

[email protected] http://Juxi.net/projects

Monday, September 10, 2012