  We propose a novel self-supervised approach for learning to visually localize
robots equipped with controllable LEDs. We rely on a few training samples
labeled with position ground truth and many training samples in which only the
LED state is known, whose collection is cheap. We show that using LED state
prediction as a pretext task significantly helps to learn the visual
localization end task. The resulting model does not require knowledge of LED
states during inference. We instantiate the approach to visual relative
localization of nano-quadrotors: experimental results show that using our
pretext task significantly improves localization accuracy (from 68.3% to 76.2%)
and outperforms alternative strategies, such as a supervised baseline, model
pre-training, and an autoencoding pretext task. We deploy our model aboard a
27-g Crazyflie nano-drone, running at 21 fps, in a position-tracking task of a
peer nano-drone. Our approach, relying on position labels for only 300 images,
yields a mean tracking error of 4.2 cm versus 11.9 cm of a supervised baseline
model trained without our pretext task. Videos and code of the proposed
approach are available at https://github.com/idsia-robotics/leds-as-pretext
