Object Shape and Force Estimation using Deep Learning and Optical Tactile Sensor

Abstract

Touch sensing plays a necessarily essential role in robot perception. It helps robot understanding its surrounding environment and in particular the object that it interacts with. For this reasons, robots are equipped with tactile sensor. Moreover, tactile object recognition exhibits a challenge in practical scenarios. In this paper, we proposed object shape classification and force estimation based on deep learning and optical tactile sensor with one touching. This study consists of three methods. First, tactile image is selected by optical flow technique. Image augmentation is used to increase a number of image. Second, features of each image are extracted by modified VGG-16 layer. Last, object shape and force estimation classifier are multi-layer perceptron (MLP), which is a supervised learning technique. The experimental result shows that an accuracy rate is 98.9% for classifying six object shapes and 98.68% for estimating eleven force levels. The results showed that our method outperformed the previous methods that use tactile image and one touching.