  Tactile perception is crucial for a variety of robot tasks including grasping
and in-hand manipulation. New advances in flexible, event-driven, electronic
skins may soon endow robots with touch perception capabilities similar to
humans. These electronic skins respond asynchronously to changes (e.g., in
pressure, temperature), and can be laid out irregularly on the robot's body or
end-effector. However, these unique features may render current deep learning
approaches such as convolutional feature extractors unsuitable for tactile
learning. In this paper, we propose a novel spiking graph neural network for
event-based tactile object recognition. To make use of local connectivity of
taxels, we present several methods for organizing the tactile data in a graph
structure. Based on the constructed graphs, we develop a spiking graph
convolutional network. The event-driven nature of spiking neural network makes
it arguably more suitable for processing the event-based data. Experimental
results on two tactile datasets show that the proposed method outperforms other
state-of-the-art spiking methods, achieving high accuracies of approximately
90\% when classifying a variety of different household objects.
