Open-Sourced Reinforcement Learning Environments for Surgical Robotics

R. S. Sutton, D. A. McAllester, S. P. Singh, and Y. Mansour, “Policy gradient methods for reinforcement learning with function approximation,” in Advances in neural information processing systems

2000

Earlier work this paper cites.

V. R. Konda and J. N. Tsitsiklis, “Actor-critic algorithms,” in Advances in neural information processing systems

2000

Earlier work this paper cites.

J. Van Den Berg, S. Miller, D. Duckworth, H. Hu, A. Wan, X.-Y. Fu, K. Goldberg, and P. Abbeel, “Superhuman performance of surgical tasks by robots using iterative learning from human-guided demonstrations,” in 2010 IEEE International Conference on Robotics and Automation (ICRA)

2010

Earlier work this paper cites.

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602

Original

2013

Earlier work this paper cites.

T. Osa, N. Sugita, and M. Mitsuishi, “Online trajectory planning in dynamic environments for surgical task automation.,” in Robotics: Science and Systems

2014

Earlier work this paper cites.

B. Kehoe, G. Kahn, J. Mahler, J. Kim, A. Lee, A. Lee, K. Nakagawa, S. Patil, W. D. Boyd, P. Abbeel, et al

2014

Earlier work this paper cites.

P. Kazanzides, Z. Chen, A. Deguet, G. S. Fischer, R. H. Taylor, and S. P. DiMaio, “An open-source research kit for the da vinci ®surgical system,” IEEE Intl. Conf. on Robotics and Automation

2014

Earlier work this paper cites.

A. Murali, S. Sen, B. Kehoe, A. Garg, S. McFarland, S. Patil, W. D. Boyd, S. Lim, P. Abbeel, and K. Goldberg, “Learning by observation for surgical subtasks: Multilateral cutting of 3d viscoelastic and 2d orthotropic tissue phantoms,” in 2015 IEEE International Conference on Robotics and Automation (ICRA)

2015

Earlier work this paper cites.

J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz, “Trust region policy optimization,” in International Conference on Machine Learning

2015

Earlier work this paper cites.

T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971

Original

2015

Earlier work this paper cites.

A. Nair, P. Srinivasan, S. Blackwell, C. Alcicek, R. Fearon, A. De Maria, V. Panneershelvam, M. Suleyman, C. Beattie, S. Petersen, et al

Original

2015

Earlier work this paper cites.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al

2016

Earlier work this paper cites.

G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” CoRR

Original

2016

Earlier work this paper cites.

H. Van Hasselt, A. Guez, and D. Silver, “Deep reinforcement learning with double q-learning.,” in AAAI

2016

Cited alongside, same era.

T. Tang, C. Liu, W. Chen, and M. Tomizuka, “Robotic manipulation of deformable objects by tangent space mapping and non-rigid registration,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2016

Cited alongside, same era.

Y. Duan, X. Chen, R. Houthooft, J. Schulman, and P. Abbeel, “Benchmarking deep reinforcement learning for continuous control,” in International Conference on Machine Learning

2016

Cited alongside, same era.

J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2017

Cited alongside, same era.

J. J. Ji, S. Krishnan, V. Patel, D. Fer, and K. Goldberg, “Learning 2d surgical camera motion from demonstrations,” in 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)

2018

Later among the works it cites.

C. D’Ettorre et al

2018

Later among the works it cites.

D. Seita, S. Krishnan, R. Fox, S. McKinley, J. Canny, and K. Goldberg, “Fast and reliable autonomous surgical debridement with cable-driven robots using a two-phase calibration procedure,” in 2018 IEEE International Conference on Robotics and Automation (ICRA)

2018

Later among the works it cites.

G. A. Fontanelli, M. Selvaggio, M. Ferro, F. Ficuciello, M. Vendittelli, and B. Siciliano, “A v-rep simulator for the da vinci research kit robotic platform,” in BioRob

2018

Later among the works it cites.

M. Plappert, M. Andrychowicz, A. Ray, B. McGrew, B. Baker, G. Powell, J. Schneider, J. Tobin, M. Chociej, P. Welinder, V. Kumar, and W. Zaremba, “Multi-goal reinforcement learning: Challenging robotics environments and request for research,” arXiv preprint arXiv:1802.09464