  Robotic vision plays a major role in factory automation to service robot
applications. However, the traditional use of frame-based camera sets a
limitation on continuous visual feedback due to their low sampling rate and
redundant data in real-time image processing, especially in the case of
high-speed tasks. Event cameras give human-like vision capabilities such as
observing the dynamic changes asynchronously at a high temporal resolution
($1\mu s$) with low latency and wide dynamic range.
  In this paper, we present a visual servoing method using an event camera and
a switching control strategy to explore, reach and grasp to achieve a
manipulation task. We devise three surface layers of active events to directly
process stream of events from relative motion. A purely event based approach is
adopted to extract corner features, localize them robustly using heat maps and
generate virtual features for tracking and alignment. Based on the visual
feedback, the motion of the robot is controlled to make the temporal upcoming
event features converge to the desired event in spatio-temporal space. The
controller switches its strategy based on the sequence of operation to
establish a stable grasp. The event based visual servoing (EVBS) method is
validated experimentally using a commercial robot manipulator in an eye-in-hand
configuration. Experiments prove the effectiveness of the EBVS method to track
and grasp objects of different shapes without the need for re-tuning.
