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To succeed in the real world, robots must cope with situations that differ from those seen during training.
Never stop learning: The effectiveness of fine-tuning in robotic reinforcement learning
Ryan Julian, Benjamin Swanson, Gaurav Sukhatme, Sergey Levine, Chelsea Finn, and Karol Hausman · 2004
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Reinforcement learning with multi-fidelity simulators
Mark Cutler, Thomas J Walsh, and Jonathan P How · 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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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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Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra · 2016
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Epopt: Learning robust neural network policies using model ensembles
Aravind Rajeswaran, Sarvjeet Ghotra, Balaraman Ravindran, and Sergey Levine · 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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Cad2rl: Real single-image flight without a single real image
Fereshteh Sadeghi and Sergey Levine · 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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The option-critic architecture
Pierre-Luc Bacon, Jean Harb, and Doina Precup · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, 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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Preparing for the unknown: Learning a universal policy with online system identification
Wenhao Yu, Jie Tan, C Karen Liu, and Greg Turk · 2017
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Variational option discovery algorithms
Joshua Achiam, Harrison Edwards, Dario Amodei, and Pieter Abbeel · 2018
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Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Evolved policy gradients
Rein Houthooft, Yuhua Chen, Phillip Isola, Bradly Stadie, Filip Wolski, OpenAI Jonathan Ho, and Pieter Abbeel · 2018
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Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
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Sim-to-real transfer of robotic control with dynamics randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 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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Meta-learning with latent embedding optimization
Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 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
Cited alongside, same era.
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
Cited alongside, same era.
Learning to coordinate manipulation skills via skill behavior diversification
Youngwoon Lee, Jingyun Yang, and Joseph J Lim · 2019
Cited alongside, same era.
Mcp: Learning composable hierarchical control with multiplicative compositional policies
Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, and Sergey Levine · 2019
Cited alongside, same era.
Dropout q-functions for doubly efficient reinforcement learning
Takuya Hiraoka, Takahisa Imagawa, Taisei Hashimoto, Takashi Onishi, and Yoshimasa Tsuruoka · 2021
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Rma: Rapid motor adaptation for legged robots
Ashish Kumar, Zipeng Fu, Deepak Pathak, and Jitendra Malik · 2021
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Guided reinforcement learning with learned skills
Karl Pertsch, Youngwoon Lee, Yue Wu, and Joseph J Lim · 2021
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Lifelong robotic reinforcement learning by retaining experiences
Annie Xie and Chelsea Finn · 2021
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Dynamics randomization revisited: A case study for quadrupedal locomotion
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Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, and Karol Hausman · 2019
Cited alongside, same era.
Sim-to-real transfer for biped locomotion
Wenhao Yu, Visak CV Kumar, Greg Turk, and C Karen Liu · 2019
Cited alongside, same era.
Efficient bimanual manipulation using learned task schemas
Rohan Chitnis, Shubham Tulsiani, Saurabh Gupta, and Abhinav Gupta · 2020
Cited alongside, same era.
Off-dynamics reinforcement learning: Training for transfer with domain classifiers
Benjamin Eysenbach, Swapnil Asawa, Shreyas Chaudhari, Sergey Levine, and Ruslan Salakhutdinov · 2020
Cited alongside, same era.
Self-supervised policy adaptation during deployment
Nicklas Hansen, Rishabh Jangir, Yu Sun, Guillem Alenyà, Pieter Abbeel, Alexei A Efros, Lerrel Pinto, and Xiaolong Wang · 2020
Cited alongside, same era.
Towards continual reinforcement learning: A review and perspectives
Khimya Khetarpal, Matthew Riemer, Irina Rish, and Doina Precup · 2020
Cited alongside, same era.
Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
Cited alongside, same era.
Learning quadrupedal locomotion over challenging terrain
Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter · 2020
Cited alongside, same era.
Zhaoming Xie, Xingye Da, Michiel Van de Panne, Buck Babich, and Animesh Garg · 2021
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Takuma Yoneda, Ge Yang, Matthew R Walter, and Bradly Stadie · 2021
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Combo: Conservative offline model-based policy optimization
Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, and Chelsea Finn · 2021
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Legged locomotion in challenging terrains using egocentric vision
Ananye Agarwal, Ashish Kumar, Jitendra Malik, and Deepak Pathak · 2022
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You only live once: Single-life reinforcement learning
Annie Chen, Archit Sharma, Sergey Levine, and Chelsea Finn · 2022
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Deep whole-body control: learning a unified policy for manipulation and locomotion
Zipeng Fu, Xuxin Cheng, and Deepak Pathak · 2022
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Concurrent training of a control policy and a state estimator for dynamic and robust legged locomotion
Gwanghyeon Ji, Juhyeok Mun, Hyeongjun Kim, and Jemin Hwangbo · 2022
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Cic: Contrastive intrinsic control for unsupervised skill discovery
Michael Laskin, Hao Liu, Xue Bin Peng, Denis Yarats, Aravind Rajeswaran, and Pieter Abbeel · 2022
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Rapid locomotion via reinforcement learning
Gabriel B Margolis, Ge Yang, Kartik Paigwar, Tao Chen, and Pulkit Agrawal · 2022
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Learning robust perceptive locomotion for quadrupedal robots in the wild
Takahiro Miki, Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter · 2022
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Augmenting reinforcement learning with behavior primitives for diverse manipulation tasks
Soroush Nasiriany, Huihan Liu, and Yuke Zhu · 2022
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A walk in the park: Learning to walk in 20 minutes with model-free reinforcement learning
Laura Smith, Ilya Kostrikov, and Sergey Levine · 2022
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Efficient online reinforcement learning with offline data
Philip J Ball, Laura Smith, Ilya Kostrikov, and Sergey Levine · 2023
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Learning agile soccer skills for a bipedal robot with deep reinforcement learning
Tuomas Haarnoja, Ben Moran, Guy Lever, Sandy H Huang, Dhruva Tirumala, Markus Wulfmeier, Jan Humplik, Saran Tunyasuvunakool, Noah Y Siegel, Roland Hafner, et al · 2023
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Lei Han, Qingxu Zhu, Jiapeng Sheng, Chong Zhang, Tingguang Li, Yizheng Zhang, He Zhang, Yuzhen Liu, Cheng Zhou, Rui Zhao, et al · 2023
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Predictable mdp abstraction for unsupervised model-based rl
Seohong Park and Sergey Levine · 2023
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Neural volumetric memory for visual locomotion control
Ruihan Yang, Ge Yang, and Xiaolong Wang · 2023
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Robot parkour learning
Ziwen Zhuang, Zipeng Fu, Jianren Wang, Christopher G Atkeson, Sören Schwertfeger, Chelsea Finn, and Hang Zhao · 2023
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