Associating SOM Representations of Haptic Submodalities

We have experimented with a bio-inspired selforganizing texture and hardness perception system which automatically learns to associate the representations of the two submodalities with each other. To this end we have developed a microphone based texture sensor and a hardness sensor that measures the compression of the material at a constant pressure. The system is based on a novel variant of the Self-Organizing Map (SOM), called Associative Self-Organizing Map (A-SOM). The A-SOM both develops a representation of its input space and learns to associate this with the activity in an external SOM or A-SOM. The system was trained and tested with multiple samples gained from the exploration of a set of 4 soft and 4 hard objects of different materials with varying textural properties. The system successfully found representations of the texture and hardness submodalities and also learned to associate these with each other.