Recognizing texture and hardness by touch

We have experimented with different neural network based architectures for bio-inspired self-organizing texture and hardness perception systems. 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.We have implemented and successfully tested both monomodal systems for texture and hardness perception and multimodal systems that merge texture and hardness data into one representation. All systems were trained and tested with multiple samples gained from the exploration of a set of 4 soft and 4 hard objects of different materials. The monomodal texture system was good at mapping individual objects in a sensible way, the hardness systems was good at mapping individual objects and in addition dividing the objects into categories of hard and soft objects. The multimodal system was successful in merging the two modalities into a representation that performed at least as good as the best recognizer of individual objects, i.e. the texture system, and at the same time categorizing the objects into hard and soft.