Learning dance movements by imitation: A multiple model approach

Abstract

Imitation learning is an intuitive and easy way of programming robots. Instead of specifying motor commands, you simply show the robot what to do. This paper presents a modular connectionist architecture that enables imitation learning in a simulated robot. The robot imitates human dance movements, and the architecture self-organizes the decomposition of movements into submovements, which are controlled by different modules. Modules both dominate and collaborate during control of the robot. Low-level examination of the inverse models (i.e. motor controllers) reveals a recurring pattern of neural activity during repetition of movements, indicating that the modules successfully capture specific parts of the trajectory to be imitated. © 2008 Springer-Verlag Berlin Heidelberg.