Data acquisition and modeling of 3D deformable objects using neural networks

The goal of the work presented in this paper is to develop a novel scheme for the measurement and representation of deformable objects without a priori knowledge on their shape or material. The proposed solution advantageously combines a neural gas network and feedforward neural network architectures to achieve diversified tasks as required for data collection on one side and the modeling of elastic characteristics on the other side. Data is collected for different objects using a joint sensing strategy that combines tactile probing and range imaging. The innovative object models, built as multi-resolution point-clouds associated with ¿tactile patches¿, present certain advantages over classical deformable 3D object models.