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Sim-to-real transfer is a powerful paradigm for robotic reinforcement learning.
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Simple random search provides a competitive approach to reinforcement learning
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A. A. Rusu, M. Vecerík, T. Rothörl, N. Heess, R. Pascanu, and R. Hadsell · 2016
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Y. Huang, D. Buchler, O. Koç, B. Schölkopf, and J. Peters · 2016
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F. Sadeghi and S. Levine · 2017
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S. Zhu, A. Kimmel, K. E. Bekris, and A. Boularias · 2017
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A. Aly, S. Griffiths, and F. Stramandinoli · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Markerless racket pose detection and stroke classification based on stereo vision for table tennis robots
Y. Gao, J. Tebbe, J. Krismer, and A. Zell · 2019
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Provably robust blackbox optimization for reinforcement learning
K. Choromanski, A. Pacchiano, J. Parker-Holder, Y. Tang, D. Jain, Y. Yang, A. Iscen, J. Hsu, and V. Sindhwani · 2019
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X. B. Peng, E. Coumans, T. Zhang, T. E. Lee, J. Tan, and S. Levine · 2020
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J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter · 2020
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Sim-to-real transfer in deep reinforcement learning for robotics: a survey
W. Zhao, J. P. Queralta, and T. Westerlund · 2020
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Sim2real transfer for reinforcement learning without dynamics randomization
M. Kaspar, J. D. M. Osorio, and J. Bock · 2020
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Rapidly adaptable legged robots via evolutionary meta-learning
X. Song, Y. Yang, K. Choromanski, K. Caluwaerts, W. Gao, C. Finn, and J. Tan · 2020
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The ping pong robot to return a ball precisely
A. Kyohei, N. Masamune, and Y. Satoshi · 2020
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Robotic table tennis with model-free reinforcement learning
W. Gao, L. Graesser, K. Choromanski, X. Song, N. Lazic, P. Sanketi, V. Sindhwani, and N. Jaitly · 2020
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Learning high-speed flight in the wild
A. Loquercio, E. Kaufmann, R. Ranftl, M. Müller, V. Koltun, and D. Scaramuzza · 2021
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Sim2real in robotics and automation: Applications and challenges
S. Höfer, K. Bekris, A. Handa, J. C. Gamboa, M. Mozifian, F. Golemo, C. Atkeson, D. Fox, K. Goldberg, J. Leonard, C. Karen Liu, J. Peters, S. Song, P. Welinder, and M. White · 2021
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Legged robots that keep on learning: Fine-tuning locomotion policies in the real world
L. M. Smith, J. C. Kew, X. B. Peng, S. Ha, J. Tan, and S. Levine · 2021
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Sample-efficient reinforcement learning in robotic table tennis
J. Tebbe, L. Krauch, Y. Gao, and A. Zell · 2021
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The utility of explainable ai in ad hoc human-machine teaming
R. Paleja, M. Ghuy, N. Ranawaka Arachchige, R. Jensen, and M. Gombolay · 2021
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Optimal stroke learning with policy gradient approach for robotic table tennis
Y. Gao, J. Tebbe, and A. Zell · 2021
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Pybullet, a python module for physics simulation for games, robotics and machine learning
E. Coumans and Y. Bai · 2021
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Bi-manual manipulation and attachment via sim-to-real reinforcement learning, 2022
S. Kataoka, S. K. S. Ghasemipour, D. Freeman, and I. Mordatch · 2022
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Fedembed: Personalized private federated learning, 2022
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