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While deep reinforcement learning (RL) agents have demonstrated incredible potential in attaining dexterous behaviours for robotics, they tend to make errors when deployed in the real world due to mismatches between the training and execution environments.
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Tsuneo Yoshikawa · 1985
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Charles W Warren · 1989
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Yoram Koren and Johann Borenstein · 1991
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Anthony Dickinson and Bernard Balleine · 2002
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Alexander Hans, Daniel Schneegaß, Anton Maximilian Schäfer, and Steffen Udluft · 2008
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Sang Wan Lee, Shinsuke Shimojo, and John P O’Doherty · 2014
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Manipulator performance measures-a comprehensive literature survey
Sarosh Patel and Tarek Sobh · 2015
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Fangyi Zhang, Jürgen Leitner, Michael Milford, Ben Upcroft, and Peter Corke · 2015
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Zachary C Lipton, Jianfeng Gao, Lihong Li, Jianshu Chen, and Li Deng · 2016
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Ali Ghadirzadeh, Atsuto Maki, Danica Kragic, and Mårten Björkman · 2017
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Gregory Kahn, Adam Villaflor, Vitchyr Pong, Pieter Abbeel, and Sergey Levine · 2017
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Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Pyrep: Bringing v-rep to deep robot learning
Stephen James, Marc Freese, and Andrew J. Davison · 2019
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Learning dexterous in-hand manipulation
OpenAI: Marcin Andrychowicz, Bowen Baker, Maciek Chociej, Rafal Józefowicz, Bob McGrew, Jakub Pachocki, Arthur Petron, Matthias Plappert, Glenn Powell, Alex Ray, Jonas Schneider, Szymon Sidor, Josh Tobin, Peter Welinder, Lilian Weng, and Wojciech Zaremba · 2020
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Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh, and Ville Kyrki · 2020
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Combining optimal control and learning for visual navigation in novel environments
Somil Bansal, Varun Tolani, Saurabh Gupta, Jitendra Malik, and Claire Tomlin · 2020
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Sim-to-real robot learning from pixels with progressive nets
Andrei A Rusu, Matej Večerík, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, and Raia Hadsell · 2017
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Jacob Bruce, Niko Suenderhauf, Piotr Mirowski, Raia Hadsell, and Michael Milford · 2018
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Residual reinforcement learning for robot control
Tobias Johannink, Shikhar Bahl, Ashvin Nair, Jianlan Luo, Avinash Kumar, Matthias Loskyll, Juan Aparicio Ojea, Eugen Solowjow, and Sergey Levine · 2018
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Ian Osband, John Aslanides, and Albin Cassirer · 2018
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Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Tom Silver, Kelsey Allen, Josh Tenenbaum, and Leslie Kaelbling · 2018
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Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, and Sergey Levine
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Scaling simulation-to-real transfer by learning a latent space of robot skills
Ryan C Julian, Eric Heiden, Zhanpeng He, Hejia Zhang, Stefan Schaal, Joseph J Lim, Gaurav S Sukhatme, and Karol Hausman · 2020
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Exi-net: Explicitly/implicitly conditioned network for multiple environment sim-to-real transfer
Takayuki Murooka, Masashi Hamaya, Felix von Drigalski, Kazutoshi Tanaka, Yoshihisa Ijiri, and Yutaro Konta · 2020
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Krishan Rana, Ben Talbot, Vibhavari Dasagi, Michael Milford, and Niko Sünderhauf · 2020
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A purely-reactive manipulability-maximising motion controller
Jesse Haviland and Peter Corke · 2021
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Class anchor clustering: A loss for distance-based open set recognition
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