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

Learning Long-Range Perception Using Self-Supervision from Short-Range Sensors and Odometry