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The existence of tactile afferents sensitive to slip-related mechanicaltransients in the human hand augments the robustness of grasping throughsecondary force modulation protocols. Despite this knowledge and the fact thattactile-based slip detection has been researched for decades, robust slipdetection is still not an out-of-the-box capability for any commerciallyavailable tactile sensor. This research seeks to bridge this gap with acomprehensive study addressing several aspects of slip detection. Keydevelopments include a systematic data collection process yielding millions ofsensory data points, the generalized conversion of multivariate-to-univariatesensor output, an insightful spectral analysis of the univariate sensoroutputs, and the application of Long Short-Term Memory (LSTM) neural networkson the univariate signals to produce robust slip detectors from threecommercially available sensors capable of tactile sensing. The sensing elementsunderlying these sensors vary in quantity, spatial arrangement, and mechanics,leveraging principles in electro-mechanical resistance, optics, andhydro-acoustics. Critically, slip detection performance of the tactiletechnologies is quantified through a measurement methodology that unveils theeffects of data window size, sampling rate, material type, slip speed, andsensor manufacturing variability. Results indicate that the investigatedcommercial tactile sensors are inherently capable of high-quality slipdetection.

Slip Detection: Analysis and Calibration of Univariate Tactile Signals