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General-purpose robotic systems must master a large repertoire of diverse skills to be useful in a range of daily tasks.
Lifelong robot learning
Sebastian Thrun and Tom M Mitchell · 1995
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Richard S. Sutton and Andrew G. Barto · 1998
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Richard S. Sutton, Doina Precup, and Satinder Singh · 1999
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Hierarchical reinforcement learning with the maxq value function decomposition
Thomas G. Dietterich · 2000
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Acquisition of stand-up behavior by a real robot using hierarchical reinforcement learning
Jun Morimoto and Kenji Doya · 2001
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Gradient surgery for multi-task learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2001
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Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2001
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Recent advances in hierarchical reinforcement learning
Andrew G. Barto and Sridhar Mahadevan · 2003
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Hierarchical policy gradient algorithms
Mohammad Ghavamzadeh and Sridhar Mahadevan · 2003
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Intrinsically motivated learning of hierarchical collections of skills
A. G. Barto, S. Singh, and N. Chentanez · 2004
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Matthew E. Taylor and Peter Stone · 2007
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Multi-task reinforcement learning: a hierarchical bayesian approach
Aaron Wilson, Alan Fern, Soumya Ray, and Prasad Tadepalli · 2007
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Transfer of samples in batch reinforcement learning
Alessandro Lazaric, Marcello Restelli, and Andrea Bonarini · 2008
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Learning parameterized skills
Bruno Castro da Silva, George Dimitri Konidaris, and Andrew G. Barto · 2012
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Hierarchical relative entropy policy search
Christian Daniel, Gerhard Neumann, and Jan Peters · 2012
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Reinforcement learning to adjust parametrized motor primitives to new situations
Jens Kober, Andreas Wilhelm, Erhan Öztop, and Jan Peters · 2012
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Learning to select and generalize striking movements in robot table tennis
Katharina Mülling, Jens Kober, Oliver Kroemer, and Jan Peters · 2013
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Multi-task policy search for robotics
Marc Peter Deisenroth, Peter Englert, Jan Peters, and Dieter Fox · 2014
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Actor-mimic: Deep multitask and transfer reinforcement learning
Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov · 2015
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Andrei A Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, and Raia Hadsell · 2015
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r l 2 rl^{2} : Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
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Deep visual foresight for planning robot motion
Chelsea Finn 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
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Cross-stitch networks for multi-task learning
Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert · 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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The curious robot: Learning visual representations via physical interactions
Lerrel Pinto, Dhiraj Gandhi, Yuanfeng Han, Yong-Lae Park, and Abhinav Gupta · 2016
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
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Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich · 2017
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One-shot imitation learning
Yan Duan, Marcin Andrychowicz, Bradly Stadie, OpenAI Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba · 2017
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Pathnet: Evolution channels gradient descent in super neural networks
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A Rusu, Alexander Pritzel, and Daan Wierstra · 2017
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Learning by playing-solving sparse reward tasks from scratch
Martin Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Volodymyr Mnih, Nicolas Heess, and Jost Tobias Springenberg · 2018
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Promp: Proximal meta-policy search
Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, and Pieter Abbeel · 2018
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Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun · 2018
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Learning general purpose distributed sentence representations via large scale multi-task learning
Sandeep Subramanian, Adam Trischler, Yoshua Bengio, and Christopher J Pal · 2018
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One-shot imitation from observing humans via domain-adaptive meta-learning
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Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, and Sergey Levine · 2017
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Multi-modal imitation learning from unstructured demonstrations using generative adversarial nets
Karol Hausman, Yevgen Chebotar, Stefan Schaal, Gaurav Sukhatme, and Joseph J Lim · 2017
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Google’s multilingual neural machine translation system: Enabling zero-shot translation
Melvin Johnson, Mike Schuster, Quoc V Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, et al · 2017
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Deep generative dual memory network for continual learning
Nitin Kamra, Umang Gupta, and Yan Liu · 2017
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Infogail: Interpretable imitation learning from visual demonstrations
Yunzhu Li, Jiaming Song, and Stefano Ermon · 2017
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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2017
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Film: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2017
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Learning to push by grasping: Using multiple tasks for effective learning
Lerrel Pinto and Abhinav Gupta · 2017
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Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2018
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Taskonomy: Disentangling task transfer learning
Amir R. Zamir, Alexander Sax, William B. Shen, Leonidas J. Guibas, Jitendra Malik, and Silvio Savarese · 2018
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Learning synergies between pushing and grasping with self-supervised deep reinforcement learning, 2018
Andy Zeng, Shuran Song, Stefan Welker, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 2018
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Solving rubik’s cube with a robot hand
Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, et al · 2019
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Scaling data-driven robotics with reward sketching and batch reinforcement learning
Serkan Cabi, Sergio Gómez Colmenarejo, Alexander Novikov, Ksenia Konyushkova, Scott Reed, Rae Jeong, Konrad Zolna, Yusuf Aytar, David Budden, Mel Vecerik, et al · 2019
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Experience-embedded visual foresight
Yen-Chen Lin, Maria Bauzá, and Phillip Isola · 2019
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Multi-task deep neural networks for natural language understanding
Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao · 2019
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Guided meta-policy search
Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2019
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Meta-learning of sequential strategies
Pedro A Ortega, Jane X Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alex Pritzel, Pablo Sprechmann, et al · 2019
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Efficient off-policy meta-reinforcement learning via probabilistic context variables
Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, and Deirdre Quillen · 2019
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Ray interference: a source of plateaus in deep reinforcement learning
Tom Schaul, Diana Borsa, Joseph Modayil, and Razvan Pascanu · 2019
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Improvisation through physical understanding: Using novel objects as tools with visual foresight
Annie Xie, Frederik Ebert, Sergey Levine, and Chelsea Finn · 2019
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One-shot hierarchical imitation learning of compound visuomotor tasks
Tianhe Yu, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2019
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Varibad: A very good method for bayes-adaptive deep rl via meta-learning
Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, and Shimon Whiteson · 2019
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Learning precise 3d manipulation from multiple uncalibrated cameras
Iretiayo Akinola, Jacob Varley, and Dmitry Kalashnikov · 2020
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Learning one-shot imitation from humans without humans
Alessandro Bonardi, Stephen James, and Andrew J Davison · 2020
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Rewriting history with inverse rl: Hindsight inference for policy improvement
Benjamin Eysenbach, Xinyang Geng, Sergey Levine, and Ruslan Salakhutdinov · 2020
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Discovering motor programs by recomposing demonstrations
Tanmay Shankar, Shubham Tulsiani, Lerrel Pinto, and Abhinav Gupta · 2020
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Concept2robot: Learning manipulation concepts from instructions and human demonstrations
Lin Shao, Toki Migimatsu, Qiang Zhang, Karen Yang, and Jeannette Bohg · 2020
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Scalable multi-task imitation learning with autonomous improvement
Avi Singh, Eric Jang, Alexander Irpan, Daniel Kappler, Murtaza Dalal, Sergey Levine, Mohi Khansari, and Chelsea Finn · 2020
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Which tasks should be learned together in multi-task learning?, 2020
Trevor Standley, Amir R. Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese · 2020
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The surprising effectiveness of linear models for visual foresight in object pile manipulation
H. J. Terry Suh and Russ Tedrake · 2020
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Multi-task reinforcement learning with soft modularization
Ruihan Yang, Huazhe Xu, Yi Wu, and Xiaolong Wang · 2020
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