  Despite the utility of tactile information, tactile sensors have yet to be
widely deployed in industrial robotics settings. Part of the challenge lies in
identifying slip and other key events from the tactile data stream. In this
paper, we present a learning-based method to detect slip using barometric
tactile sensors. Although these sensors have a low resolution, they have many
other desirable properties including high reliability and durability, a very
slim profile, and a low cost. We are able to achieve slip detection accuracies
of greater than 91% while being robust to the speed and direction of the slip
motion. Further, we test our detector on two robot manipulation tasks involving
common household objects and demonstrate successful generalization to
real-world scenarios not seen during training. We show that barometric tactile
sensing technology, combined with data-driven learning, is potentially suitable
for complex manipulation tasks such as slip compensation.
