  Novel high-resolution pressure-sensor arrays allow treating pressure readings
as standard images. Computer vision algorithms and methods such as
Convolutional Neural Networks (CNN) can be used to identify contact objects. In
this paper, a high-resolution tactile sensor has been attached to a robotic
end-effector to identify contacted objects. Two CNN-based approaches have been
employed to classify pressure images. These methods include a transfer learning
approach using a pre-trained CNN on an RGB-images dataset and a custom-made CNN
(TactNet) trained from scratch with tactile information. The transfer learning
approach can be carried out by retraining the classification layers of the
network or replacing these layers with an SVM. Overall, 11 configurations based
on these methods have been tested: 8 transfer learning-based, and 3
TactNet-based. Moreover, a study of the performance of the methods and a
comparative discussion with the current state-of-the-art on tactile object
recognition is presented.
