Deep reinforcement learning for high precision assembly tasks

@article{Inoue2017DeepRL,
  title={Deep reinforcement learning for high precision assembly tasks},
  author={Tadanobu Inoue and G. Magistris and A. Munawar and T. Yokoya and Ryuki Tachibana},
  journal={2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2017},
  pages={819-825}
}
  • Tadanobu Inoue, G. Magistris, +2 authors Ryuki Tachibana
  • Published 2017
  • Computer Science, Engineering
  • 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • The high precision assembly of mechanical parts requires precision that exceeds that of robots. Conventional part-mating methods used in the current manufacturing require numerous parameters to be tediously tuned before deployment. We show how a robot can successfully perform a peg-in-hole task with a tight clearance through training a recurrent neural network with reinforcement learning. In addition to reducing manual effort, the proposed method also shows a better fitting performance with a… CONTINUE READING
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