  This paper demonstrates a visual servoing method which is robust towards
uncertainties related to system calibration and grasping, while significantly
reducing the peg-in-hole time compared to classical methods and recent attempts
based on deep learning. The proposed visual servoing method is based on peg and
hole point estimates from a deep neural network in a multi-cam setup, where the
model is trained on purely synthetic data. Empirical results show that the
learnt model generalizes to the real world, allowing for higher success rates
and lower cycle times than existing approaches.
