Title: A self-organising multiple model architecture for motor imitation

Authors: Axel Tidemann, Pinar Ozturk

Addresses: Self-Organizing Systems Group, Department of Computer Science, Norwegian University of Science and Technology, Sem Sælandsvei 7-9, 7491 Norway. ' Self-Organizing Systems Group, Department of Computer Science, Norwegian University of Science and Technology, Sem Sælandsvei 7-9, 7491 Norway

Abstract: Learning by imitation allows humans to easily transfer motor knowledge between individuals. Our research is aimed towards equipping robots with imitative capabilities, so humans can simply show a robot what to do. This will greatly simplify how humans program robots. To achieve imitative behaviour, we have implemented a self-organising connectionist modular architecture on a simulated robot. Motion tracking was used to gather data of human dance movements. When imitating the dance movements, the architecture self-organises the decomposition of movements into submovements, which are controlled by different modules. The modules both collaborate and compete for control during the movement. The trajectory recorded during motion tracking was repeated, revealing recurrent neural activation patterns of the inverse models (i.e., controllers), indicating that the modules specialise on specific parts of the trajectory.

Keywords: human-robot interaction; agent architectures; neural networks; imitation learning; knowledge transfer; motor knowledge; robot programming; motion tracking; robot simulation; connectionist architecture; self-organising architecture; modular architecture; human dance movements.

DOI: 10.1504/IJIIDS.2010.031759

International Journal of Intelligent Information and Database Systems, 2010 Vol.4 No.1, pp.1 - 18

Published online: 23 Feb 2010 *

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