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This paper introduces a discrete-continuous action space to learn insertion primitives for robotic assembly tasks.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ Int. Conf. on Intelligent Robots and Syst. IEEE, 2012, pp. 5026–5033
2012
Earlier work this paper cites.
2013
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2015
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2015
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W. Masson, P. Ranchod, and G. Konidaris, “Reinforcement learning with parameterized actions,” in Thirtieth AAAI Conference on Artificial Intelligence , 2016
2016
Earlier work this paper cites.
T. Inoue, G. De Magistris, A. Munawar, T. Yokoya, and R. Tachibana, “Deep reinforcement learning for high precision assembly tasks,” in 2017 IEEE/RSJ Int. Conf. on Intelligent Robots and Syst. (IROS) . IEEE, 2017, pp. 819–825
2017
Earlier work this paper cites.
2018
Cited alongside, same era.
S. Fujimoto, H. Hoof, and D. Meger, “Addressing function approximation error in actor-critic methods,” in International Conference on Machine Learning . PMLR, 2018, pp. 1587–1596
2018
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2018
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2019
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G. Schoettler, A. Nair, J. Luo, S. Bahl, J. A. Ojea, E. Solowjow, and S. Levine, “Deep reinforcement learning for industrial insertion tasks with visual inputs and natural rewards,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 5548–5555
2020
Later among the works it cites.
D. Son, H. Yang, and D. Lee, “Sim-to-real transfer of bolting tasks with tight tolerance,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 9056–9063
2020
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S. A. Khader, H. Yin, P. Falco, and D. Kragic, “Stability-guaranteed reinforcement learning for contact-rich manipulation,” IEEE Robotics and Automation Letters , vol. 6, no. 1, pp. 1–8, 2020
2020
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G. Schoettler, A. Nair, J. A. Ojea, S. Levine, and E. Solowjow, “Meta-reinforcement learning for robotic industrial insertion tasks,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 9728–9735
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2019
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S. Hoppe, M. Giftthaler, R. Krug, and M. Toussaint, “Sample-efficient learning for industrial assembly using qgraph-bounded ddpg,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 9080–9087
2020
Cited alongside, same era.
K. Tanaka, R. Yonetani, M. Hamaya, R. Lee, F. von Drigalski, and Y. Ijiri, “Trans-am: Transfer learning by aggregating dynamics models for soft robotic assembly.”
Cited in the paper.
Rail-Berkeley, “Rail-berkeley/rlkit: Collection of reinforcement learning algorithms.” [Online]. Available: https://github.com/rail-berkeley/rlkit
Cited in the paper.
2020
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R. Chitnis, S. Tulsiani, S. Gupta, and A. Gupta, “Efficient bimanual manipulation using learned task schemas,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 1149–1155
2020
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