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Simulation-to-real transfer is an important strategy for making reinforcement learning practical with real robots.
N. Jakobi, P. Husbands, and I. Harvey, “Noise and the reality gap: The use of simulation in evolutionary robotics,” in Advances in Artificial Life , F. Morán, A. Moreno, J. J. Merelo, and P. Chacón, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995, pp. 704–720
1995
Earlier work this paper cites.
S. Barrett, M. E. Taylor, and P. Stone, “Transfer learning for reinforcement learning on a physical robot,” in Ninth International Conference on Autonomous Agents and Multiagent Systems - Adaptive Learning Agents Workshop (AAMAS - ALA) , May 2010. [Online]. Available: http://www.cs.utexas.edu/users/ai-lab/?AAMASWS10-barrett
2010
Earlier work this paper cites.
P. Pastor, M. Kalakrishnan, L. Righetti, and S. Schaal, “Towards associative skill memories,” in Humanoids , Nov 2012
2012
Earlier work this paper cites.
“MuJoCo: A physics engine for model-based control,” in IROS , 2012
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, pp. 529–533, 2015. [Online]. Available: https://www.nature.com/nature/journal/v518/n7540/pdf/nature1423 6.pdf
2015
Earlier work this paper cites.
E. Rueckert, J. Mundo, A. Paraschos, J. Peters, and G. Neumann, “Extracting low-dimensional control variables for movement primitives,” in ICRA , May 2015
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, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. P. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of go with deep neural networks and tree search,” Nature , vol. 529, no. 7587, pp. 484–489, 2016. [Online]. Available: https://doi.org/10.1038/nature16961
2016
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2016
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2017
Cited alongside, same era.
2017
Later among the works it cites.
M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, P. Abbeel, and W. Zaremba, “Hindsight experience replay,” in NIPS , 2017
2017
Later among the works it cites.
K. Hausman, J. Springenberg, Z. Wang, N. Heess, and M. Riedmiller, “Learning an embedding space for transferable robot skills,” in ICLR , 2018. [Online]. Available: https://openreview.net/forum?id=rk07ZXZRb
2018
Closest in time.
R. C. Julian, E. Heiden, Z. He, H. Zhang, S. Schaal, J. Lim, G. S. Sukhatme, and K. Hausman, “Scaling simulation-to-real transfer by learning composable robot skills,” in International Symposium on Experimental Robotics . Springer, 2018. [Online]. Available: https://ryanjulian.me/iser_2018.pdf
2018
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2017
Cited alongside, same era.
S. Gu, E. Holly, T. Lillicrap, and S. Levine, “Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,” in ICRA . IEEE, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
F. Sadeghi and S. Levine, “CAD2RL: Real single-image flight without a single real image,” in RSS , 2017
2017
Cited alongside, same era.
A. A. Visser, N. Dijkshoorn, M. V. D. Veen, and R. Jurriaans, “Closing the gap between simulation and reality in the sensor and motion models of an autonomous ar.drone.” [Online]. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.841.1746&re p=rep1&type=pdf
Cited in the paper.
2018
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2018
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