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.

翻译：新型高分辨率压力传感器阵列使得压力读数可被视为标准图像进行处理。计算机视觉算法及方法，如卷积神经网络（CNN），可用于识别接触物体。本文中，将高分辨率触觉传感器安装于机器人末端执行器以识别接触物体。采用两种基于CNN的方法对压力图像进行分类，包括利用RGB图像数据集预训练CNN的迁移学习方法，以及从零开始使用触觉信息训练的自定义CNN（TactNet）。迁移学习可通过重新训练网络分类层或将其替换为支持向量机（SVM）实现。总体而言，基于上述方法测试了11种配置：8种基于迁移学习，3种基于TactNet。此外，对方法性能进行了研究，并与当前触觉物体识别领域的最新技术进行了对比讨论。