Scalable cognitive developmental network: A strategy for integrating new perception online using relation evolution SOINN

Abstract

Cognitive development can make robots more intelligent. However, current cognitive robots are still limited to learning fixed types of information. To this end, we propose a scalable cognitive developmental network (SCDN) based on self-organizing incremental neural network (SOINN), which allows new types of perception information to be added into the existing cognitive network online. SCDN has three hierarchical layers for developing concrete and abstract knowledge. Firstly, the sample layer learns the sample features of four modal information, including shape, color, name and taste. Secondly, the symbol layer generates respective symbol representations. Finally, the associative layer realizes the fusion of new perception information and native networks. Specifically, the Relation Evolution SOINN (RE-SOINN) is proposed in the associative layer to add new information into existing relationships, which contributes to the evolution of associative relationships. We evaluate the performance of SCDN on a fruits and vegetables dataset. Experimental results show that SCDN can effectively learn unknown sensory information online and form the correct link with the known information without learning from scratch.