  We introduce a general self-supervised approach to predict the future outputs
of a short-range sensor (such as a proximity sensor) given the current outputs
of a long-range sensor (such as a camera); we assume that the former is
directly related to some piece of information to be perceived (such as the
presence of an obstacle in a given position), whereas the latter is
information-rich but hard to interpret directly. We instantiate and implement
the approach on a small mobile robot to detect obstacles at various distances
using the video stream of the robot's forward-pointing camera, by training a
convolutional neural network on automatically-acquired datasets. We
quantitatively evaluate the quality of the predictions on unseen scenarios,
qualitatively evaluate robustness to different operating conditions, and
demonstrate usage as the sole input of an obstacle-avoidance controller. We
additionally instantiate the approach on a different simulated scenario with
complementary characteristics, to exemplify the generality of our contribution.
