  Robotic grasping plays an important role in the field of robotics. The
current state-of-the-art robotic grasping detection systems are usually built
on the conventional vision, such as RGB-D camera. Compared to traditional
frame-based computer vision, neuromorphic vision is a small and young community
of research. Currently, there are limited event-based datasets due to the
troublesome annotation of the asynchronous event stream. Annotating large scale
vision dataset often takes lots of computation resources, especially the
troublesome data for video-level annotation. In this work, we consider the
problem of detecting robotic grasps in a moving camera view of a scene
containing objects. To obtain more agile robotic perception, a neuromorphic
vision sensor (DAVIS) attaching to the robot gripper is introduced to explore
the potential usage in grasping detection. We construct a robotic grasping
dataset named Event-Stream Dataset with 91 objects. A spatio-temporal mixed
particle filter (SMP Filter) is proposed to track the led-based grasp
rectangles which enables video-level annotation of a single grasp rectangle per
object. As leds blink at high frequency, the Event-Stream dataset is annotated
in a high frequency of 1 kHz. Based on the Event-Stream dataset, we develop a
deep neural network for grasping detection which consider the angle learning
problem as classification instead of regression. The method performs high
detection accuracy on our Event-Stream dataset with 93% precision at
object-wise level. This work provides a large-scale and well-annotated dataset,
and promotes the neuromorphic vision applications in agile robot.
