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This paper describes techniques and methodologies so far developed to investigate object fine-form discrimination by means of artificial tactile sensors. Sensor arrays, selectively sensitive to stress-tensor components and based on piezoelectric polymer technology, have been realized. Sensor output data are used to solve inverse elastic contact problems, by means of neural networks suitably trained to learn regularized inverse maps. Two possible neural network designs are considered: one is based on the multi-layer perceptron trained with the standard backpropagation algorithm, and the other is based on the use of radial basis functions. In both cases, reconstruction of object shapes is demonstrated to be effective and robust with both simulated and real data.

Constructivist and ecological approaches are also observed in tactile perception studies. The question is whether identification and localization are dissociated in the tactile modality as well, and whether Norman's conception may be generalized to the field of touch. An analogue to blindsight was evidenced in passive touch, but no such dissociation was observed in active touch. A study is in progress in this domain.