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For humans, our "senses of touch" have always been necessary for our abilityto precisely and efficiently manipulate objects of all shapes in anyenvironment, but until recently, not many works have been done to fullyunderstand haptic feedback. This work proposed a novel method for getting abetter shape descriptor than existing methods for classifying an object frommultiple tactile data collected from a tactile glove. It focuses on improvingprevious works on object classification using tactile data. The major problemfor object classification from multiple tactile data is to find a good way toaggregate features extracted from multiple tactile images. We propose a novelmethod, dubbed as Tactile-ViewGCN, that hierarchically aggregate tactilefeatures considering relations among different features by using GraphConvolutional Network. Our model outperforms previous methods on the STAGdataset with an accuracy of 81.82%.

Tactile-ViewGCN: Learning Shape Descriptor from Tactile Data using Graph Convolutional Network