  The existence of tactile afferents sensitive to slip-related mechanical
transients in the human hand augments the robustness of grasping through
secondary force modulation protocols. Despite this knowledge and the fact that
tactile-based slip detection has been researched for decades, robust slip
detection is still not an out-of-the-box capability for any commercially
available tactile sensor. This research seeks to bridge this gap with a
comprehensive study addressing several aspects of slip detection. Key
developments include a systematic data collection process yielding millions of
sensory data points, the generalized conversion of multivariate-to-univariate
sensor output, an insightful spectral analysis of the univariate sensor
outputs, and the application of Long Short-Term Memory (LSTM) neural networks
on the univariate signals to produce robust slip detectors from three
commercially available sensors capable of tactile sensing. The sensing elements
underlying these sensors vary in quantity, spatial arrangement, and mechanics,
leveraging principles in electro-mechanical resistance, optics, and
hydro-acoustics. Critically, slip detection performance of the tactile
technologies is quantified through a measurement methodology that unveils the
effects of data window size, sampling rate, material type, slip speed, and
sensor manufacturing variability. Results indicate that the investigated
commercial tactile sensors are inherently capable of high-quality slip
detection.
