  Tactile sensing is an essential modality for smart robots as it enables them
to interact flexibly with physical objects in their environment. Recent
advancements in electronic skins have led to the development of data-driven
machine learning methods that exploit this important sensory modality. However,
current datasets used to train such algorithms are limited to standard
synchronous tactile sensors. There is a dearth of neuromorphic event-based
tactile datasets, principally due to the scarcity of large-scale event-based
tactile sensors. Having such datasets is crucial for the development and
evaluation of new algorithms that process spatio-temporal event-based data. For
example, evaluating spiking neural networks on conventional frame-based
datasets is considered sub-optimal. Here, we debut a novel neuromorphic Spiking
Tactile MNIST (ST-MNIST) dataset, which comprises handwritten digits obtained
by human participants writing on a neuromorphic tactile sensor array. We also
describe an initial effort to evaluate our ST-MNIST dataset using existing
artificial and spiking neural network models. The classification accuracies
provided herein can serve as performance benchmarks for future work. We
anticipate that our ST-MNIST dataset will be of interest and useful to the
neuromorphic and robotics research communities.
