In this study, we propose an extension of the MOSAIC architecture to control real humanoid robots. MOSAIC was originally proposed by neuroscientists to understand the human ability of adaptive control. The modular architecture of the MOSAIC model can be useful for solving nonlinear and non-stationary control problems. Both humans and humanoid robots have nonlinear body dynamics and many degrees of freedom. Since they can interact with environments (e.g., carrying objects), control strategies need to deal with non-stationary dynamics. Therefore, MOSAIC has strong potential as a human motor-control model and a control framework for humanoid robots. Yet application of the MOSAIC model has been limited to simple simulated dynamics since it is susceptive to observation noise and also cannot be applied to partially observable systems. Our approach introduces state estimators into MOSAIC architecture to cope with real environments. By using an extended MOSAIC model, we are able to successfully generate squatting and object-carrying behaviors on a real humanoid robot. © 2012 Elsevier Ltd.
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