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Many industrial assembly tasks involve peg-in-hole like insertions withsub-millimeter tolerances which are challenging, even in highly calibratedrobot cells. Visual servoing can be employed to increase the robustness towardsuncertainties in the system, however, state of the art methods either rely onaccurate 3D models for synthetic renderings or manual involvement inacquisition of training data. We present a novel self-supervised visualservoing method for high precision peg-in-hole insertion, which is fullyautomated and does not rely on synthetic data. We demonstrate its applicabilityfor insertion of electronic components into a printed circuit board with tighttolerances. We show that peg-in-hole insertion can be drastically sped up bypreceding a robust but slow force-based insertion strategy with our proposedvisual servoing method, the configuration of which is fully autonomous.

Self-supervised deep visual servoing for high precision peg-in-hole insertion