  Many industrial assembly tasks involve peg-in-hole like insertions with
sub-millimeter tolerances which are challenging, even in highly calibrated
robot cells. Visual servoing can be employed to increase the robustness towards
uncertainties in the system, however, state of the art methods either rely on
accurate 3D models for synthetic renderings or manual involvement in
acquisition of training data. We present a novel self-supervised visual
servoing method for high precision peg-in-hole insertion, which is fully
automated and does not rely on synthetic data. We demonstrate its applicability
for insertion of electronic components into a printed circuit board with tight
tolerances. We show that peg-in-hole insertion can be drastically sped up by
preceding a robust but slow force-based insertion strategy with our proposed
visual servoing method, the configuration of which is fully autonomous.
