Skip to main content Accessibility help
×
Home

Crossmodal lifelong learning in hybrid neural embodied architectures

  • Stefan Wermter (a1), Sascha Griffiths (a1) and Stefan Heinrich (a1)

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.

Copyright

References

Hide All
Barros, P. & Wermter, S. (2016) Developing crossmodal expression recognition based on a deep neural model. Adaptive Behavior 24(5):373–96.
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.
Cangelosi, A. & Schlesinger, M. (2015) Developmental robotics: From babies to robots. MIT Press.
Christiansen, M. H. & Chater, N. (2016) Creating language: Integrating evolution, acquisition, and processing. MIT Press.
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.
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.
Elman, J. L. (1993) Learning and development in neural networks: The importance of starting small. Cognition 48(1):7199.
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.
Gray, H. M., Gray, K. & Wegner, D. M. (2007) Dimensions of mind perception. Science 315(5812):619.
Hall, E. T. (1966) The hidden dimension. Doubleday.
Heinrich, S. (2016) Natural language acquisition in recurrent neural architectures. Ph.D. thesis, Universität Hamburg, DE.
Lakoff, G. & Johnson, M. (2003) Metaphors we live by, 2nd ed. University of Chicago Press.
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.
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.
Ruciński, M. (2014) Modelling learning to count in humanoid robots. Ph.D. thesis, University of Plymouth, UK.
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.

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed