  We propose a general self-supervised approach to learn neural models that
solve spatial perception tasks, such as estimating the pose of an object
relative to the robot, from onboard sensor readings. The model is learned from
training episodes, by relying on: a continuous state estimate, possibly
inaccurate and affected by odometry drift; and a detector, that sporadically
provides supervision about the target pose. We demonstrate the general approach
in three different concrete scenarios: a simulated robot arm that visually
estimates the pose of an object of interest; a small differential drive robot
using 7 infrared sensors to localize a nearby wall; an omnidirectional mobile
robot that localizes itself in an environment from camera images. Quantitative
results show that the approach works well in all three scenarios, and that
explicitly accounting for uncertainty yields statistically significant
performance improvements.
