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Training end-to-end deep robot policies requires a lot of domain-, task-, and hardware-specific data, which is often costly to provide.
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Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, and Koray Kavukcuoglu · 2016
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Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andrew J Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, et al · 2016
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Anh Nguyen, Dimitrios Kanoulas, Darwin G Caldwell, and Nikos G Tsagarakis · 2017
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Timo Luddecke and Florentin Worgotter · 2017
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Deep object-centric representations for generalizable robot learning
Coline Devin, Pieter Abbeel, Trevor Darrell, and Sergey Levine · 2018
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Affordancenet: An end-to-end deep learning approach for object affordance detection
Thanh-Toan Do, Anh Nguyen, and Ian Reid · 2018
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Sensorimotor Robot Policy Training using Reinforcement Learning
Ali Ghadirzadeh · 2018
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State representation learning for control: An overview
Timothée Lesort, Natalia Díaz-Rodríguez, Jean-Frano̧is Goudou, and David Filliat · 2018
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Ali Ghadirzadeh, Atsuto Maki, Danica Kragic, and Mårten Björkman · 2017
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Sergey Levine, Peter Pastor Sampedro, Alex Krizhevsky, Julian Ibarz, and Deirdre Quillen · 2017
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Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Stephen James, Andrew J Davison, and Edward Johns · 2017
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Avi Singh, Larry Yang, and Sergey Levine · 2017
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The theory of affordances
James J Gibson
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Visual affordance and function understanding: A survey
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Learning to act properly: Predicting and explaining affordances from images
Ching-Yao Chuang, Jiaman Li, Antonio Torralba, and Sanja Fidler · 2018
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Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, and Konstantinos Bousmalis · 2018
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Understanding disentangling in β-vae
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