Abstract
In this paper, we present a neural architecture for a mobile robot in order to learn how to imitate a sequence of actions. We show that the use of a representation of the information in a continuous and dynamic way is necessary and the use of the neural fields can be a good solution to control the dynamic of several degrees of freedom with a single internal representation.
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© 1999 Springer-Verlag Berlin Heidelberg
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Moga, S., Gaussier, P. (1999). A Neural Structure for Learning by Imitation. In: Floreano, D., Nicoud, JD., Mondada, F. (eds) Advances in Artificial Life. ECAL 1999. Lecture Notes in Computer Science(), vol 1674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48304-7_40
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DOI: https://doi.org/10.1007/3-540-48304-7_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66452-9
Online ISBN: 978-3-540-48304-5
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