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Tactile perception is crucial for a variety of robot tasks including graspingand in-hand manipulation. New advances in flexible, event-driven, electronicskins may soon endow robots with touch perception capabilities similar tohumans. These electronic skins respond asynchronously to changes (e.g., inpressure, temperature), and can be laid out irregularly on the robot's body orend-effector. However, these unique features may render current deep learningapproaches such as convolutional feature extractors unsuitable for tactilelearning. In this paper, we propose a novel spiking graph neural network forevent-based tactile object recognition. To make use of local connectivity oftaxels, we present several methods for organizing the tactile data in a graphstructure. Based on the constructed graphs, we develop a spiking graphconvolutional network. The event-driven nature of spiking neural network makesit arguably more suitable for processing the event-based data. Experimentalresults on two tactile datasets show that the proposed method outperforms otherstate-of-the-art spiking methods, achieving high accuracies of approximately90\% when classifying a variety of different household objects.

TactileSGNet: A Spiking Graph Neural Network for Event-based Tactile Object Recognition