Task segmentation in a mobile robot by mnSOM and hierarchical clustering

Our previous studies assigned labels to mnSOM modules based on the assumption that winner modules corresponding to subsequences in the same class share the same label. We propose segmentation using hierarchical clustering based on the resulting mnSOM. Since it does not need the above unrealistic assumption, it gains practical importance at the sacrifice of the deterioration of the segmentation performance by 1.2%. We compare the performance of task segmentation for two kinds of module architecture in mnSOM. The result is that module architecture with sensory-motor signals as target outputs has superior performance to that with only sensory signals as target outputs.