Shape estimation with tactile sensors: a radial basis functions approach

Fine-form detection and discrimination of an object in contact with a skin-like tactile sensor is a basic feature in machine taction for perceptual, grasping and manipulation tasks. The inversion of tactile data in the form of normal and shear stress components in order to recover the contact shape and contact radius in the class of axisymmetry indenters gives rise to a nonlinear inverse problem which must be conceptually solved by using regularization techniques. Radial basis functions networks are used to solve this problem owing to their direct connection with regularization and approximate theory. Simulation results show the effectiveness of the proposed approach even with large noise added to the data.<>