  Recently developed neuromorphic vision sensors have become promising
candidates for agile and autonomous robotic applications primarily due to, in
particular, their high temporal resolution and low latency. Each pixel of this
sensor independently fires an asynchronous stream of "retinal events" once a
change in the light field is detected. Existing computer vision algorithms can
only process periodic frames and so a new class of algorithms needs to be
developed that can efficiently process these events for control tasks. In this
paper, we investigate the problem of regulating a continuous-time linear time
invariant (LTI) system to a desired point using measurements from a
neuromorphic sensor. We present an $H_\infty$ controller that regulates the LTI
system to a desired set-point and provide the set of neuromorphic sensor based
cameras for the given system that fulfill the regulation task. The
effectiveness of our approach is illustrated on an unstable system.
