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

Research output: Contribution to conference without publisher/journalPaperResearchpeer-review


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.
Original languageEnglish
Publication date2020
Number of pages10
Publication statusPublished - 2020
Event4th Conference on Robot Learning (CoRL 2020) - Cambridge, United States
Duration: 16. Nov 202018. Nov 2020


Conference4th Conference on Robot Learning (CoRL 2020)
Country/TerritoryUnited States


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