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Domain randomization (DR) is a successful technique for learning robust policies for robot systems, when the dynamics of the target robot system are unknown.
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X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel, “Sim-to-real transfer of robotic control with dynamics randomization,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 1–8
2018
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2018
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T. Chen, A. Murali, and A. Gupta, “Hardware conditioned policies for multi-robot transfer learning,” in Advances in Neural Information Processing Systems , 2018, pp. 9355–9366
2018
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R. Sutton and A. Barto, Reinforcement Learning: An Introduction , ser. Adaptive Computation and Machine Learning series. MIT Press, 2018. [Online]. Available: https://books.google.ca/books?id=6DKPtQEACAAJ
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F. Ramos, R. Possas, and D. Fox, “Bayessim: Adaptive domain randomization via probabilistic inference for robotics simulators,” in Proceedings of Robotics: Science and Systems , FreiburgimBreisgau, Germany, June 2019
2019
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