This paper is devoted to a detailed presentation of the current state of the Multilevel Darwinist Brain (MDB) cognitive architecture for lifelong learning in real robots. This architecture follows the cognitive developmental robotics approach and it is based on concepts like embodiment, open-ended lifelong learning, autonomous knowledge acquisition or adaptive behaviors and motivations. In addition, this version of the MDB architecture incorporates several improvements related with more practical issues, which are the result of the experience gained through several experiments with real robots in the last few years. The MDB uses evolutionary algorithms in the knowledge acquisition process, which implies the need of paying attention to the efficiency of the computational implementation. Here, we first describe the cognitive model on which the basic operation of the architecture is based and, secondly, we detail the main aspects and working of the current version of the MDB. Finally, we have designed a very simple but illustrative real robot lifelong learning example, where we can show how to set up an experiment using the MDB. Hence, with this simple example we show the successful behavior of the MDB cognitive developmental robotics principles.
['Francisco Bellas', 'Andrés Faiña', 'Gervasio Varela', 'Richard J. Duro']
A cognitive developmental robotics architecture for lifelong learning by evolution in real robots