  Self-Supervised Learning (SSL) is a reliable learning mechanism in which a
robot uses an original, trusted sensor cue for training to recognize an
additional, complementary sensor cue. We study for the first time in SSL how a
robot's learning behavior should be organized, so that the robot can keep
performing its task in the case that the original cue becomes unavailable. We
study this persistent form of SSL in the context of a flying robot that has to
avoid obstacles based on distance estimates from the visual cue of stereo
vision. Over time it will learn to also estimate distances based on monocular
appearance cues. A strategy is introduced that has the robot switch from stereo
vision based flight to monocular flight, with stereo vision purely used as
'training wheels' to avoid imminent collisions. This strategy is shown to be an
effective approach to the 'feedback-induced data bias' problem as also
experienced in learning from demonstration. Both simulations and real-world
experiments with a stereo vision equipped AR drone 2.0 show the feasibility of
this approach, with the robot successfully using monocular vision to avoid
obstacles in a 5 x 5 room. The experiments show the potential of persistent SSL
as a robust learning approach to enhance the capabilities of robots. Moreover,
the abundant training data coming from the own sensors allows to gather large
data sets necessary for deep learning approaches.
