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In imitation and reinforcement learning, the cost of human supervision limits the amount of data that robots can be trained on.
Tossingbot: Learning to throw arbitrary objects with residual physics, 2019
Andy Zeng, Shuran Song, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 1903
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Deep dynamics models for learning dexterous manipulation, 2019
Anusha Nagabandi, Kurt Konoglie, Sergey Levine, and Vikash Kumar · 1909
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Approximately optimal approximate reinforcement learning
Sham Kakade and John Langford · 2002
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The ingredients of real-world robotic reinforcement learning, 2020
Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, and Sergey Levine · 2004
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Continual learning of control primitives: Skill discovery via reset-games, 2020
Kelvin Xu, Siddharth Verma, Chelsea Finn, and Sergey Levine · 2011
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Autonomous reinforcement learning on raw visual input data in a real world application
Sascha Lange, Martin Riedmiller, and Arne Voigtländer · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Reinforcement learning in robotics: A survey
Jens Kober, J Andrew Bagnell, and Jan Peters · 2013
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Generative adversarial networks, 2014
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Learning compound multi-step controllers under unknown dynamics
Weiqiao Han, Sergey Levine, and Pieter Abbeel · 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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Generative adversarial imitation learning, 2016
Jonathan Ho and Stefano Ermon · 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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f-GAN: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 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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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
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Leave no trace: Learning to reset for safe and autonomous reinforcement learning, 2017
Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, and Sergey Levine · 2017
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
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Learning robust rewards with adversarial inverse reinforcement learning
Justin Fu, Katie Luo, 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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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2017
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CURL: Contrastive unsupervised representations for reinforcement learning
Michael Laskin, Aravind Srinivas, and Pieter Abbeel · 2020
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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 · 2020
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Reset-free lifelong learning with skill-space planning
Kevin Lu, Aditya Grover, Pieter Abbeel, and Igor Mordatch · 2020
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Emergent real-world robotic skills via unsupervised off-policy reinforcement learning
Archit Sharma, Michael Ahn, Sergey Levine, Vikash Kumar, Karol Hausman, and Shixiang Gu · 2020
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Randomized ensembled double q-learning: Learning fast without a model, 2021
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mixup: Beyond empirical risk minimization, 2017
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2017
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Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos Storkey, and Oleg Klimov · 2018
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Robustness via retrying: Closed-loop robotic manipulation with self-supervised learning
Frederik Ebert, Sudeep Dasari, Alex X. Lee, Sergey Levine, and Chelsea Finn · 2018
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Variational inverse control with events: A general framework for data-driven reward definition, 2018
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, 2018
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, and Sergey Levine · 2018
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Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi, Sergey Levine, and Jonathan Tompson · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Xinyue Chen, Che Wang, Zijian Zhou, and Keith Ross · 2021
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Abhishek Gupta, Justin Yu, Tony Z. Zhao, Vikash Kumar, Aaron Rovinsky, Kelvin Xu, Thomas Devlin, and Sergey Levine · 2021
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Mt-opt: Continuous multi-task robotic reinforcement learning at scale, 2021
Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, and Karol Hausman · 2021
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Mastering visual continuous control: Improved data-augmented reinforcement learning, 2021
Denis Yarats, Rob Fergus, Alessandro Lazaric, and Lerrel Pinto · 2021
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Towards real robot learning in the wild: A case study in bipedal locomotion
Michael Bloesch, Jan Humplik, Viorica Patraucean, Roland Hafner, Tuomas Haarnoja, Arunkumar Byravan, Noah Yamamoto Siegel, Saran Tunyasuvunakool, Federico Casarini, Nathan Batchelor, et al · 2022
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RT-1: Robotics transformer for real-world control at scale
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, et al · 2022
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Bootstrapped autonomous practicing via multi-task reinforcement learning
Abhishek Gupta, Corey Lynch, Brandon Kinman, Garrett Peake, Sergey Levine, and Karol Hausman · 2022
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Vision-based manipulators need to also see from their hands
Kyle Hsu, Moo Jin Kim, Rafael Rafailov, Jiajun Wu, and Chelsea Finn · 2022
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Bc-z: Zero-shot task generalization with robotic imitation learning
Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, and Chelsea Finn · 2022
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A state-distribution matching approach to non-episodic reinforcement learning
Archit Sharma, Rehaan Ahmad, and Chelsea Finn · 2022
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Legged robots that keep on learning: Fine-tuning locomotion policies in the real world
Laura Smith, J Chase Kew, Xue Bin Peng, Sehoon Ha, Jie Tan, and Sergey Levine · 2022
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Don’t start from scratch: Leveraging prior data to automate robotic reinforcement learning, 2022
Homer Walke, Jonathan Yang, Albert Yu, Aviral Kumar, Jedrzej Orbik, Avi Singh, and Sergey Levine · 2022
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When to ask for help: Proactive interventions in autonomous reinforcement learning
Annie Xie, Fahim Tajwar, Archit Sharma, and Chelsea Finn · 2022
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