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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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References

Barros, P. & Wermter, S. (2016) Developing crossmodal expression recognition based on a deep neural model. Adaptive Behavior 24(5):373–96.Google Scholar
Bauer, J., Dávila-Chacón, J. & Wermter, S. (2015) Modeling development of natural multi-sensory integration using neural self-organisation and probabilistic population codes. Connection Science 27(4):358–76.CrossRefGoogle Scholar
Cangelosi, A. & Schlesinger, M. (2015) Developmental robotics: From babies to robots. MIT Press.Google Scholar
Christiansen, M. H. & Chater, N. (2016) Creating language: Integrating evolution, acquisition, and processing. MIT Press.CrossRefGoogle Scholar
Coutinho, E., Deng, J. & Schuller, B. (2014) Transfer learning emotion manifestation across music and speech. In: Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China. pp. 3592–98. IEEE.Google Scholar
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K. & Darrell, T. (2015) Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, June 7-12, 2015, pp. 2625–34. IEEE.CrossRefGoogle Scholar
Elman, J. L. (1993) Learning and development in neural networks: The importance of starting small. Cognition 48(1):7199.Google Scholar
Gallese, V. & Lakoff, G. (2005) The brain's concepts: The role of the sensory-motor system in conceptual knowledge. Cognitive Neuropsychology 22(3–4):455–79.Google Scholar
Gray, H. M., Gray, K. & Wegner, D. M. (2007) Dimensions of mind perception. Science 315(5812):619.CrossRefGoogle ScholarPubMed
Hall, E. T. (1966) The hidden dimension. Doubleday.Google Scholar
Heinrich, S. (2016) Natural language acquisition in recurrent neural architectures. Ph.D. thesis, Universität Hamburg, DE.Google Scholar
Lakoff, G. & Johnson, M. (2003) Metaphors we live by, 2nd ed. University of Chicago Press.Google Scholar
Laptev, I., Marszalek, M., Schmid, C. & Rozenfeld, B. (2008) Learning realistic human actions from movies. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, June 23–28, 2008 (CVPR 2008), pp. 18. IEEE.Google Scholar
Rohlfing, K. J. & Nomikou, I. (2014) Intermodal synchrony as a form of maternal responsiveness: Association with language development. Language, Interaction and Acquisition 5(1):117–36.Google Scholar
Ruciński, M. (2014) Modelling learning to count in humanoid robots. Ph.D. thesis, University of Plymouth, UK.Google Scholar
Wermter, S., Palm, G., Weber, C. & Elshaw, M. (2005) Towards biomimetic neural learning for intelligent robots. In: Biomimetic neural learning for intelligent robots, ed. Wermter, S., Palm, G. & Elshaw, M., pp. 118. Springer.CrossRefGoogle Scholar