A Self-Organized Internal Models Architecture for Coding Sensory–Motor Schemes

Cognitive robotics research draws inspiration from theories and models on cognition, as conceived by neuroscience or cognitive psychology, to investigate biologically plausible computational models in artificial agents. In this field, the theoretical framework of Grounded Cognition provides epistemological and methodological grounds for the computational modeling of cognition. It has been stressed in the literature that \textit{simulation}, \textit{prediction}, and \textit{multi-modal integration} are key aspects of cognition and that computational architectures capable of putting them into play in a biologically plausible way are a necessity. Research in this direction has brought extensive empirical evidence suggesting that \textit{Internal Models} are suitable mechanisms for sensory-motor integration. However, current Internal Models architectures show several drawbacks, mainly due to the lack of a unified substrate allowing for a true sensory-motor integration space, enabling flexible and scalable ways to model cognition under the embodiment hypothesis constraints. We propose the Self-Organized Internal Models Architecture (SOIMA), a computational cognitive architecture coded by means of a network of self-organized maps, implementing coupled internal models that allow modeling multi-modal sensory-motor schemes. Our approach addresses integrally the issues of current implementations of Internal Models. We discuss the design and features of the architecture, and provide empirical results on a humanoid robot that demonstrate the benefits and potentialities of the SOIMA concept for studying cognition in artificial agents.