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Vision-Language-Action (VLA) models have shown substantial potential in real-world robotic manipulation.
Reinforcement learning by reward-weighted regression for operational space control
Jan Peters and Stefan Schaal · 2007
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Reinforcement learning for robot soccer
Martin A. Riedmiller, Thomas Gabel, Roland Hafner, and Sascha Lange · 2009
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Deep reinforcement learning from human preferences
Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 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 · 2018
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Overcoming exploration in reinforcement learning with demonstrations
Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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HG-DAgger: interactive imitation learning with human experts
Michael Kelly, Chelsea Sidrane, Katherine Rose Driggs-Campbell, and Mykel J. Kochenderfer · 2019
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Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 2019
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Residual reinforcement learning for robot control
Tobias Johannink, Shikhar Bahl, Ashvin Nair, Jianlan Luo, Avinash Kumar, Matthias Loskyll, Juan Aparicio Ojea, Eugen Solowjow, 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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The ingredients of real world robotic reinforcement learning
Henry Zhu, Justin Yu, Abhishek Gupta, Dhruv Shah, Kristian Hartikainen, Avi Singh, Vikash Kumar, and Sergey Levine · 2020
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, and other · 2022
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Offline meta-reinforcement learning for industrial insertion
Tony Z. Zhao, Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Nicolas Heess, Jon Scholz, Stefan Schaal, and Sergey Levine · 2022
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Reincarnating reinforcement learning: Reusing prior computation to accelerate progress
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C. Courville, and Marc G. Bellemare · 2022
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LoRA: low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2022
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RT-2: vision-language-action models transfer web knowledge to robotic control
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al · 2023
Cited alongside, same era.
Is reinforcement learning (not) for natural language processing: Benchmarks, baselines, and building blocks for natural language policy optimization
Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kianté Brantley, Jack Hessel, Rafet Sifa, Christian Bauckhage, Hannaneh Hajishirzi, and Yejin Choi · 2023
Cited alongside, same era.
Grounding large language models in interactive environments with online reinforcement learning
Thomas Carta, Clément Romac, Thomas Wolf, Sylvain Lamprier, Olivier Sigaud, and Pierre-Yves Oudeyer · 2023
Cited alongside, same era.
Cal-QL: Calibrated offline RL pre-training for efficient online fine-tuning
Mitsuhiko Nakamoto, Simon Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, and Sergey Levine · 2023
RLDG: robotic generalist policy distillation via reinforcement learning
Charles Xu, Qiyang Li, Jianlan Luo, and Sergey Levine · 2024
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ReFT: reasoning with reinforced fine-tuning
Luong Quoc Trung, Xinbo Zhang, Zhanming Jie, Peng Sun, Xiaoran Jin, and Hang Li · 2024
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Iterative reasoning preference optimization
Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, He He, Sainbayar Sukhbaatar, and Jason Weston · 2024
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DigiRL: training in-the-wild device-control agents with autonomous reinforcement learning
Hao Bai, Yifei Zhou, Mert Cemri, Jiayi Pan, Alane Suhr, Sergey Levine, and Aviral Kumar · 2024
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Policy agnostic RL: offline RL and online RL fine-tuning of any class and backbone
Max Sobol Mark, Tian Gao, Georgia Gabriela Sampaio, Mohan Kumar Srirama, Archit Sharma, Chelsea Finn, and Aviral Kumar · 2024
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Cited alongside, same era.
Open problems and fundamental limitations of reinforcement learning from human feedback
Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, et al · 2023
Cited alongside, same era.
Efficient online reinforcement learning with offline data
Philip J Ball, Laura Smith, Ilya Kostrikov, and Sergey Levine · 2023
Cited alongside, same era.
REBOOT: reuse data for bootstrapping efficient real-world dexterous manipulation
Zheyuan Hu, Aaron Rovinsky, Jianlan Luo, Vikash Kumar, Abhishek Gupta, and Sergey Levine · 2023
Cited alongside, same era.
Robot parkour learning
Ziwen Zhuang, Zipeng Fu, Jianren Wang, Christopher G. Atkeson, Sören Schwertfeger, Chelsea Finn, and Hang Zhao · 2023
Cited alongside, same era.
MOTO: offline pre-training to online fine-tuning for model-based robot learning
Rafael Rafailov, Kyle Beltran Hatch, Victor Kolev, John D. Martin, Mariano Phielipp, and Chelsea Finn · 2023
Cited alongside, same era.
Robot learning on the job: Human-in-the-loop autonomy and learning during deployment
Huihan Liu, Soroush Nasiriany, Lance Zhang, Zhiyao Bao, and Yuke Zhu · 2023
Cited alongside, same era.
Diffusion policy: Visuomotor policy learning via action diffusion
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song · 2023
Cited alongside, same era.
Later among the works it cites.
Efficient online reinforcement learning fine-tuning need not retain offline data
Zhiyuan Zhou, Andy Peng, Qiyang Li, Sergey Levine, and Aviral Kumar · 2024
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Boosting continuous control with consistency policy
Yuhui Chen, Haoran Li, and Dongbin Zhao · 2024
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Fine-tuning large vision-language models as decision-making agents via reinforcement learning
Yuexiang Zhai, Hao Bai, Zipeng Lin, Jiayi Pan, Shengbang Tong, Yifei Zhou, Alane Suhr, Saining Xie, Yann LeCun, Yi Ma, et al · 2024
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DeepSeekMath: pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al · 2024
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Continuously improving mobile manipulation with autonomous real-world RL
Russell Mendonca, Emmanuel Panov, Bernadette Bucher, Jiuguang Wang, and Deepak Pathak · 2024
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Consistency policy: Accelerated visuomotor policies via consistency distillation
Aaditya Prasad, Kevin Lin, Jimmy Wu, Linqi Zhou, and Jeannette Bohg · 2024
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Octo: an open-source generalist robot policy
Dibya Ghosh, Homer Rich Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, Jianlan Luo, You Liang Tan, Lawrence Yunliang Chen, Quan Vuong, Ted Xiao, Pannag R. Sanketi, Dorsa Sadigh, Chelsea Finn, and Sergey Levine · 2024
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Beyond sight: Finetuning generalist robot policies with heterogeneous sensors via language grounding
Joshua Jones, Oier Mees, Carmelo Sferrazza, Kyle Stachowicz, Pieter Abbeel, and Sergey Levine · 2025
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Consistency policy with categorical critic for autonomous driving
Xing Fang, Qichao Zhang, Haoran Li, and Dongbin Zhao · 2025
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