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.

本文介绍了一种基于高分辨率压力传感器和卷积神经网络的两种方法，对接触对象进行分类。这些方法包括利用预训练的卷积神经网络或使用自定义 CNN 进行分类，并测试了 11 个配置的性能表现，提供了与当前触觉对象识别技术的比较讨论。