  This paper presents a data-driven approach to learning vision-based
collective behavior from a simple flocking algorithm. We simulate a swarm of
quadrotor drones and formulate the controller as a regression problem in which
we generate 3D velocity commands directly from raw camera images. The dataset
is created by simultaneously acquiring omnidirectional images and computing the
corresponding control command from the flocking algorithm. We show that a
convolutional neural network trained on the visual inputs of the drone can
learn not only robust collision avoidance but also coherence of the flock in a
sample-efficient manner. The neural controller effectively learns to localize
other agents in the visual input, which we show by visualizing the regions with
the most influence on the motion of an agent. This weakly supervised saliency
map can be computed efficiently and may be used as a prior for subsequent
detection and relative localization of other agents. We remove the dependence
on sharing positions among flock members by taking only local visual
information into account for control. Our work can therefore be seen as the
first step towards a fully decentralized, vision-based flock without the need
for communication or visual markers to aid detection of other agents.
