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Crossmodal lifelong learning in hybrid neural embodied architectures

Published online by Cambridge University Press:  10 November 2017

Stefan Wermter
Affiliation:
Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany. wermter@informatik.uni-hamburg.degriffiths@informatik.uni-hamburg.deheinrich@informatik.uni-hamburg.dehttps://www.informatik.uni-hamburg.de/~wermter/https://www.informatik.uni-hamburg.de/~griffiths/https://www.informatik.uni-hamburg.de/~heinrich/
Sascha Griffiths
Affiliation:
Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany. wermter@informatik.uni-hamburg.degriffiths@informatik.uni-hamburg.deheinrich@informatik.uni-hamburg.dehttps://www.informatik.uni-hamburg.de/~wermter/https://www.informatik.uni-hamburg.de/~griffiths/https://www.informatik.uni-hamburg.de/~heinrich/
Stefan Heinrich
Affiliation:
Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany. wermter@informatik.uni-hamburg.degriffiths@informatik.uni-hamburg.deheinrich@informatik.uni-hamburg.dehttps://www.informatik.uni-hamburg.de/~wermter/https://www.informatik.uni-hamburg.de/~griffiths/https://www.informatik.uni-hamburg.de/~heinrich/

Abstract

Lake et al. point out that grounding learning in general principles of embodied perception and social cognition is the next step in advancing artificial intelligent machines. We suggest it is necessary to go further and consider lifelong learning, which includes developmental learning, focused on embodiment as applied in developmental robotics and neurorobotics, and crossmodal learning that facilitates integrating multiple senses.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2017 

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