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Insertion operations are a critical element of most robotic assembly operation, and peg-in-hole (PiH) insertion is one of the most widely studied tasks in the industrial and academic manipulation communities.
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S. Dong and A. Rodriguez, “Tactile-Based Insertion for Dense Box-Packing,” in IEEE International Conference on Intelligent Robots and Systems . Institute of Electrical and Electronics Engineers Inc., 11 2019, pp. 7953–7960
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S. Dong, D. Jha, D. Romeres, S. Kim, D. Nikovski, and A. Rodriguez, “Tactile-RL for insertion: Generalization to objects of unknown geometry,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021
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S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” Journal of Machine Learning Research , vol. 17, pp. 1–40, 2016
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T. Inoue, G. De Magistris, A. Munawar, T. Yokoya, and R. Tachibana, “Deep reinforcement learning for high precision assembly tasks,” in IEEE International Conference on Intelligent Robots and Systems . Institute of Electrical and Electronics Engineers Inc., 12 2017, pp. 819–825
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K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision , 2017
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P. Kulkarni, J. Kober, R. Babuška, and C. D. Santina, “Learning Assembly Tasks in a Few Minutes by Combining Impedance Control and Residual Recurrent Reinforcement Learning,” Advanced Intelligent Systems , p. 2100095, 9 2021
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2021
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S. Jain, D. Romeres, D. K. Jha, W. Yerazunis, D. Nikovski, and A. Sullivan, “Automated visual hole detection for robotic peg-in-hole assembly,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, p. under review
2022
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