Fast robust peg-in-hole insertion with continuous visual servoing

Understand

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.