he 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. In particular, key developments include a systematic data collection process yielding millions of sensory data points, a spectral analysis of sensory responses providing insight into sensor behavior, and the application of Long Short-Term Memory (LSTM) neural networks to produce robust slip detectors from three commercially available tactile sensors. 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.
tactile sensors, slip detection, neural networks, deep learning