  The ability to perceive object slip via tactile feedback enables humans to
accomplish complex manipulation tasks including maintaining a stable grasp.
Despite the utility of tactile information for many applications, tactile
sensors have yet to be widely deployed in industrial robotics settings; part of
the challenge lies in identifying slip and other events from the tactile data
stream. In this paper, we present a learning-based method to detect slip using
barometric tactile sensors. These sensors have many desirable properties
including high durability and reliability, and are built from inexpensive,
off-the-shelf components. We train a temporal convolution neural network to
detect slip, achieving high detection accuracies while displaying robustness to
the speed and direction of the slip motion. Further, we test our detector on
two manipulation tasks involving a variety of common objects and demonstrate
successful generalization to real-world scenarios not seen during training. We
argue that barometric tactile sensing technology, combined with data-driven
learning, is suitable for many manipulation tasks such as slip compensation.
