  Decentralized drone swarms deployed today either rely on sharing of positions
among agents or detecting swarm members with the help of visual markers. This
work proposes an entirely visual approach to coordinate markerless drone swarms
based on imitation learning. Each agent is controlled by a small and efficient
convolutional neural network that takes raw omnidirectional images as inputs
and predicts 3D velocity commands that match those computed by a flocking
algorithm. We start training in simulation and propose a simple yet effective
unsupervised domain adaptation approach to transfer the learned controller to
the real world. We further train the controller with data collected in our
motion capture hall. We show that the convolutional neural network trained on
the visual inputs of the drone can learn not only robust inter-agent collision
avoidance but also cohesion of the swarm 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. We remove the dependence on sharing positions among swarm
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 swarm without the need for communication or visual markers.
