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The success of reinforcement learning for real world robotics has been, in many cases limited to instrumented laboratory scenarios, often requiring arduous human effort and oversight to enable continuous learning.
Continual learning for robotics
Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni, David Filliat, and Natalia Díaz Rodríguez · 1907
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Lifelong robot learning
Sebastian Thrun and Tom M. Mitchell · 1995
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Algorithms for inverse reinforcement learning
Andrew Y. Ng and Stuart J. Russell · 2000
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Developmental robotics: a survey
Max Lungarella, Giorgio Metta, Rolf Pfeifer, and Giulio Sandini · 2003
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Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts
Jürgen Schmidhuber · 2006
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Cognitive developmental robotics: A survey
Minoru Asada, Koh Hosoda, Yasuo Kuniyoshi, Hiroshi Ishiguro, Toshio Inui, Yuichiro Yoshikawa, Masaki Ogino, and Chisato Yoshida · 2009
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2013
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Guided policy search
Sergey Levine and Vladlen Koltun · 2013
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Learning compound multi-step controllers under unknown dynamics
Weiqiao Han, Sergey Levine, and Pieter Abbeel · 2015
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Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Adapting Deep Visuomotor Representations with Weak Pairwise Constraints
Eric Tzeng, Coline Devin, Judy Hoffman, Chelsea Finn, Pieter Abbeel, Sergey Levine, Kate Saenko, and Trevor Darrell · 2015
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Path integral guided policy search
Yevgen Chebotar, Mrinal Kalakrishnan, Ali Yahya, Adrian Li, Stefan Schaal, and Sergey Levine · 2016
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2016
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Learning dexterous manipulation for a soft robotic hand from human demonstrations
Abhishek Gupta, Clemens Eppner, Sergey Levine, and Pieter Abbeel · 2016
Cited alongside, same era.
Reinforcement learning with unsupervised auxiliary tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z Leibo, David Silver, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Optimal control with learned local models: Application to dexterous manipulation
Vikash Kumar, Emanuel Todorov, and Sergey Levine · 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Cited alongside, same era.
Combining model-based and model-free updates for trajectory-centric reinforcement learning
Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav Sukhatme, Stefan Schaal, and Sergey Levine · 2017
Cited alongside, same era.
Leave no trace: Learning to reset for safe and autonomous reinforcement learning
Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, and Sergey Levine · 2018
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Variational inverse control with events: A general framework for data-driven reward definition
Justin Fu, Avi Singh, Dibya Ghosh, Larry Yang, and Sergey Levine · 2018
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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
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Imitation from observation: Learning to imitate behaviors from raw video via context translation
YuXuan Liu, Abhishek Gupta, Pieter Abbeel, and Sergey Levine · 2018
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
Cited alongside, same era.
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
Cited alongside, same era.
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2017
Cited alongside, same era.
Visual closed-loop control for pouring liquids
Connor Schenck and Dieter Fox · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
Cited alongside, same era.
Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Matej Vecerik, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, and Martin A. Riedmiller · 2017
Cited alongside, same era.
Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Intrinsic motivation and automatic curricula via asymmetric self-play
Sainbayar Sukhbaatar, Zeming Lin, Ilya Kostrikov, Gabriel Synnaeve, Arthur Szlam, and Rob Fergus · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz · 2018
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ROBEL: RObotics BEnchmarks for Learning with low-cost robots
Michael Ahn, Henry Zhu, Kristian Hartikainen, Hugo Ponte, Abhishek Gupta, Sergey Levine, and Vikash Kumar · 2019
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Unsupervised state representation learning in atari
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, and R. Devon Hjelm · 2019
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2019
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Katie Kang, Suneel Belkhale, Gregory Kahn, Pieter Abbeel, and Sergey Levine · 2019
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Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model
Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, and Sergey Levine · 2019
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Deep Dynamics Models for Learning Dexterous Manipulation
Anusha Nagabandi, Kurt Konoglie, Sergey Levine, and Vikash Kumar · 2019
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End-to-end robotic reinforcement learning without reward engineering
Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, and Sergey Levine · 2019
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Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost
Henry Zhu, Abhishek Gupta, Aravind Rajeswaran, Sergey Levine, and Vikash Kumar · 2019
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