Material classification by tactile sensing using surface textures

In this paper we describe an application of machine learning to distinguish between seven different materials, based on their surface texture. Applications of such a system includes quality assurance and estimating surface friction during manipulation tasks. A naive Bayes classifier is used to distinguish textures sensed by a bio-inspired artificial finger. The finger has randomly distributed strain gauges and Polyvinylidene Fluoride (PVDF) films embedded in silicone. Different textures induce different intensity of vibrations in the silicone. Textures can be distinguished by the presence of different frequencies in the signal. The data from the finger is pre-processed and the Fourier coefficients of the sensor outputs are used to learn a classifier for different textures. The performance of the classifier is evaluated against a naive time domain based learner. Preliminary results show that our classifier performs better.