Using multiple models to imitate the YMCA

Authors:
Axel Tidemann
Affiliations:
IDI, Norwegian University of Science and Technology
Venue:
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
Year:
2008

Citing 7
Cited 0

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

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Sensory-motor primitives as a basis for imitation: linking perception to action and biology to robotics

Imitation in animals and artifacts
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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

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Abstract

Learning by imitation enables people to program robots simply by showing them what to do, instead of having to specify the motor commands of the robot. To achieve imitative behaviour in a simulated robot, a modular connectionist architecture for motor learning and control was implemented. The architecture was used to imitate human dance movements. The architecture self-organizes the decomposition of the movement to be imitated across different modules. The results show that the decomposition of the movement tends to be both competitive (i.e. one module dominates the others for a part of the movement) and collaborative (i.e. modules cooperate in controlling the robot).