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.
|Number of pages||10|
|Publication status||Published - 2020|
|Event||4th Conference on Robot Learning (CoRL 2020) - Cambridge, United States|
Duration: 16. Nov 2020 → 18. Nov 2020
|Conference||4th Conference on Robot Learning (CoRL 2020)|
|Period||16/11/2020 → 18/11/2020|