  Tactile sensing represents a crucial technique that can enhance the
performance of robotic manipulators in various tasks. This work presents a
novel bioinspired neuromorphic vision-based tactile sensor that uses an
event-based camera to quickly capture and convey information about the
interactions between robotic manipulators and their environment. The camera in
the sensor observes the deformation of a flexible skin manufactured from a
cheap and accessible 3D printed material, whereas a 3D printed rigid casing
houses the components of the sensor together. The sensor is tested in a
grasping stage classification task involving several objects using a
data-driven learning-based approach. The results show that the proposed
approach enables the sensor to detect pressing and slip incidents within a
speed of 2 ms. The fast tactile perception properties of the proposed sensor
makes it an ideal candidate for safe grasping of different objects in
industries that involve high-speed pick-and-place operations.
