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In recent decades, the tremendous benefits surgical robots have brought to surgeons and patients have been witnessed.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , “Human-level control through deep reinforcement learning,” nature , vol. 518, no. 7540, pp. 529–533, 2015
2015
Earlier work this paper cites.
H. Van Hasselt, A. Guez, and D. Silver, “Deep reinforcement learning with double q-learning,” in Proceedings of the AAAI conference on artificial intelligence , vol. 30, no. 1, 2016
2016
Earlier work this paper cites.
M. Yip and N. Das, “Robot autonomy for surgery,” in The Encyclopedia of MEDICAL ROBOTICS: Volume 1 Minimally Invasive Surgical Robotics . World Scientific, 2019, pp. 281–313
2019
Cited alongside, same era.
A. Munawar, Y. Wang, R. Gondokaryono, and G. S. Fischer, “A real-time dynamic simulator and an associated front-end representation format for simulating complex robots and environments,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 1875–1882
2019
Cited alongside, same era.
J. Chen, D. Zhang, A. Munawar, R. Zhu, B. Lo, G. S. Fischer, and G.-Z. Yang, “Supervised semi-autonomous control for surgical robot based on bayesian optimization,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 2943–2949
2020
Later among the works it cites.
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