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One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments.
Automatic programming of behavior-based robots using reinforcement learning
Sridhar Mahadevan and Jonathan Connell · 1992
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Acquiring robot skills via reinforcement learning
Vijaykumar Gullapalli, Judy A Franklin, and Hamid Benbrahim · 1994
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Lifelong Learning Algorithms , page 181–209
Sebastian Thrun · 1998
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Machine learning for fast quadrupedal locomotion
Nate Kohl and Peter Stone · 2004
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Applied optimal control for dynamically stable legged locomotion
Russell L. Tedrake · 2004
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Boosting for transfer learning
Wenyuan Dai, Qiang Yang, Gui-Rong Xue, and Yong Yu · 2007
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Self-taught learning: transfer learning from unlabeled data
Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and Andrew Y Ng · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Policy search for motor primitives in robotics
Jens Kober and Jan R Peters · 2009
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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Transfer learning for reinforcement learning domains: A survey
Matthew E Taylor and Peter Stone · 2009
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Reinforcement learning in robotics: A survey
Jens Kober, J Andrew Bagnell, and Jan Peters · 2013
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Decaf: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Robots that can adapt like animals
Antoine Cully, Jeff Clune, Danesh Tarapore, and Jean-Baptiste Mouret · 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, et al · 2015
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Learning to poke by poking: Experiential learning of intuitive physics
Pulkit Agrawal, Ashvin V Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2016
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Deep spatial autoencoders for visuomotor learning
Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, and Pieter Abbeel · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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What makes imagenet good for transfer learning?
Minyoung Huh, Pulkit Agrawal, and Alexei A Efros · 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Learning to navigate in complex environments
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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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Lerrel Pinto and Abhinav Gupta · 2016
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Sim-to-real robot learning from pixels with progressive nets
Andrei A. Rusu, Matej Vecerík, Thomas Rothörl, Nicolas Manfred Otto Heess, Razvan Pascanu, and Raia Hadsell · 2016
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
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One-shot visual imitation learning via meta-learning
Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2017
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Deep predictive policy training using reinforcement learning
Ali Ghadirzadeh, Atsuto Maki, Danica Kragic, and Mårten Björkman · 2017
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Adapting learned robotics behaviours through policy adjustment
Learning by playing solving sparse reward tasks from scratch
Martin A. Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Volodymyr Mnih, Nicolas Manfred Otto Heess, and Jost Tobias Springenberg · 2018
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Sim2real viewpoint invariant visual servoing by recurrent control
Fereshteh Sadeghi, Alexander Toshev, Eric Jang, and Sergey Levine · 2018
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Sim-to-real: Learning agile locomotion for quadruped robots
Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, and Vincent Vanhoucke · 2018
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One-shot imitation from observing humans via domain-adaptive meta-learning
Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2018
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Learning synergies between pushing and grasping with self-supervised deep reinforcement learning
Andy Zeng, Shuran Song, Stefan Welker, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 2018
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Juan Camilo Gamboa Higuera, David Meger, and Gregory Dudek · 2017
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An overview of multi-task learning in deep neural networks, 2017
Sebastian Ruder · 2017
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Cad2rl: Real single-image flight without a single real image
Fereshteh Sadeghi and Sergey Levine · 2017
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Unsupervised perceptual rewards for imitation learning
Pierre Sermanet, Kelvin Xu, and Sergey Levine · 2017
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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
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Ferran Alet, Tomás Lozano-Pérez, and Leslie P Kaelbling · 2018
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Using parameterized black-box priors to scale up model-based policy search for robotics
Konstantinos Chatzilygeroudis and Jean-Baptiste Mouret · 2018
Cited alongside, same era.
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Learning one-shot imitation from humans without humans
Alessandro Bonardi, Stephen James, and Andrew J Davison · 2019
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Robonet: Large-scale multi-robot learning, 2019
Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, and Chelsea Finn · 2019
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Affordance learning for end-to-end visuomotor robot control
Aleksi Hämäläinen, Karol Arndt, Ali Ghadirzadeh, and Ville Kyrki · 2019
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Transferring generalizable motor primitives from simulation to real world
Murtaza Hazara and Ville Kyrki · 2019
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Off-policy evaluation via off-policy classification
Alexander Irpan, Kanishka Rao, Konstantinos Bousmalis, Chris Harris, Julian Ibarz, and Sergey Levine · 2019
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Modelling generalized forces with reinforcement learning for sim-to-real transfer
Rae Jeong, Jackie Kay, Francesco Romano, Thomas Lampe, Tom Rothorl, Abbas Abdolmaleki, Tom Erez, Yuval Tassa, and Francesco Nori · 2019
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Do better imagenet models transfer better?
Simon Kornblith, Jonathon Shlens, and Quoc V Le · 2019
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Reusable neural skill embeddings for vision-guided whole body movement and object manipulation
Josh Merel, Saran Tunyasuvunakool, Arun Ahuja, Yuval Tassa, Leonard Hasenclever, Vu Pham, Tom Erez, Greg Wayne, and Nicolas Heess · 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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Solving rubik’s cube with a robot hand, 2019
OpenAI, Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Jerry Tworek, Peter Welinder, Lilian Weng, Qiming Yuan, Wojciech Zaremba, and Lei Zhang · 2019
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Skew-fit: State-covering self-supervised reinforcement learning
Vitchyr H Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, and Sergey Levine · 2019
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Mid-level visual representations improve generalization and sample efficiency for learning visuomotor policies
Alexander Sax, Bradley Emi, Amir R. Zamir, Leonidas J. Guibas, Silvio Savarese, and Jitendra Malik · 2019
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Deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals
Gerrit Schoettler, Ashvin Nair, Jianlan Luo, Shikhar Bahl, Juan Aparicio Ojea, Eugen Solowjow, and Sergey Levine · 2019
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Experience-embedded visual foresight
Lin Yen-Chen, Maria Bauza, and Phillip Isola · 2019
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Unsupervised visuomotor control through distributional planning networks
Tianhe Yu, Gleb Shevchuk, Dorsa Sadigh, and Chelsea Finn · 2019
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Tossingbot: Learning to throw arbitrary objects with residual physics
Andy Zeng, Shuran Song, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 2019
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Adaptive prior selection for repertoire-based online adaptation in robotics
Rituraj Kaushik, Pierre Desreumaux, and Jean-Baptiste Mouret · 2020
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