A self-organising multiple model architecture for motor imitation

Authors:
Axel Tidemann;Pinar Ozturk
Affiliations:
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
Venue:
International Journal of Intelligent Information and Database Systems
Year:
2010

Citing 7
Cited 0

Understanding Intelligence

Understanding Intelligence
The correspondence problem

Imitation in animals and artifacts
The mirror system, imitation, and the evolution of language

Imitation in animals and artifacts
Imitation as a dual-route process featuring predictive and learning components: a biologically plausible computational model

Imitation in animals and artifacts
MOSAIC Model for Sensorimotor Learning and Control

Neural Computation
Adaptive mixtures of local experts

Neural Computation
Self-organizing multiple models for imitation: teaching a robot to dance the YMCA

IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems

Quantified Score

Hi-index	0.00

Visualization

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.