A self-organising multiple model architecture for motor imitation

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 selforganizing 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-organizes 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 specialize on specific parts of the trajectory.