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Recent advancements in robot learning have used imitation learning with large models and extensive demonstrations to develop effective policies.
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Andrew Y Ng, Stuart J Russell, et al · 2000
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Efficient reductions for imitation learning
Stéphane Ross and Drew Bagnell · 2010
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Development through sensorimotor coordination
Adam Sheya and Linda B Smith · 2010
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A reduction from apprenticeship learning to classification
Umar Syed and Robert E Schapire · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Learning to search better than your teacher
Kai-Wei Chang, Akshay Krishnamurthy, Alekh Agarwal, Hal Daumé III, and John Langford · 2015
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Human-level control through deep reinforcement learning
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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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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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Attention is all you need
A Vaswani · 2017
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Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Mel Vecerik, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, and Martin Riedmiller · 2017
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Playing hard exploration games by watching youtube
Yusuf Aytar, Tobias Pfaff, David Budden, Thomas Paine, Ziyu Wang, and Nando De Freitas · 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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Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado Van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, and David Silver · 2018
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Policy optimization with demonstrations
Bingyi Kang, Zequn Jie, and Jiashi Feng · 2018
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Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi, Sergey Levine, and Jonathan Tompson · 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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Film: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
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Active learning from infancy to childhood
M Saylor and P Ganea · 2018
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Tom Silver, Kelsey Allen, Josh Tenenbaum, and Leslie Kaelbling · 2018
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Few-shot goal inference for visuomotor learning and planning
Annie Xie, Avi Singh, Sergey Levine, and Chelsea Finn · 2018
Cited alongside, same era.
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
Cited alongside, same era.
End-to-end robotic reinforcement learning without reward engineering
Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, and Sergey Levine · 2019
Cited alongside, same era.
A practical approach to insertion with variable socket position using deep reinforcement learning
Mel Vecerik, Oleg Sushkov, David Barker, Thomas Rothörl, Todd Hester, and Jon Scholz · 2019
Cited alongside, same era.
Deep residual reinforcement learning
Shangtong Zhang, Wendelin Boehmer, and Shimon Whiteson · 2019
Cited alongside, same era.
Mildly conservative q-learning for offline reinforcement learning
Jiafei Lyu, Xiaoteng Ma, Xiu Li, and Zongqing Lu · 2022
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From one hand to multiple hands: Imitation learning for dexterous manipulation from single-camera teleoperation
Yuzhe Qin, Hao Su, and Xiaolong Wang · 2022
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Behavior transformers: Cloning k k modes with one stone
Nur Muhammad Shafiullah, Zichen Cui, Ariuntuya Arty Altanzaya, and Lerrel Pinto · 2022
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Diffusion policies as an expressive policy class for offline reinforcement learning
Zhendong Wang, Jonathan J Hunt, and Mingyuan Zhou · 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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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 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.
Awac: Accelerating online reinforcement learning with offline datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal, and Sergey Levine · 2020
Cited alongside, same era.
Error bounds of imitating policies and environments
Tian Xu, Ziniu Li, and Yang Yu · 2020
Cited alongside, same era.
Residual reinforcement learning from demonstrations
Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal, Jean Ponce, and Cordelia Schmid · 2021
Cited alongside, same era.
Perception, action, and intrinsic motivation in infants’ motor-skill development
Daniela Corbetta · 2021
Cited alongside, same era.
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine · 2023
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Robocat: A self-improving foundation agent for robotic manipulation
Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Devin, Alex X Lee, Maria Bauza, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, et al · 2023
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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
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Diffusion policy: Visuomotor policy learning via action diffusion
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song · 2023
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Open X-Embodiment: Robotic learning datasets and RT-X models
Open X-Embodiment Collaboration · 2023
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Consistency models as a rich and efficient policy class for reinforcement learning
Zihan Ding and Chi Jin · 2023
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Optimizing ddpm sampling with shortcut fine-tuning
Ying Fan and Kangwook Lee · 2023
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Maniskill2: A unified benchmark for generalizable manipulation skills
Jiayuan Gu, Fanbo Xiang, Xuanlin Li, Zhan Ling, Xiqiaing Liu, Tongzhou Mu, Yihe Tang, Stone Tao, Xinyue Wei, Yunchao Yao, Xiaodi Yuan, Pengwei Xie, Zhiao Huang, Rui Chen, and Hao Su · 2023
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Idql: Implicit q-learning as an actor-critic method with diffusion policies
Philippe Hansen-Estruch, Ilya Kostrikov, Michael Janner, Jakub Grudzien Kuba, and Sergey Levine · 2023
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Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
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Learning a diffusion model policy from rewards via q-score matching
Michael Psenka, Alejandro Escontrela, Pieter Abbeel, and Yi Ma · 2023
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Memory-consistent neural networks for imitation learning
Kaustubh Sridhar, Souradeep Dutta, Dinesh Jayaraman, James Weimer, and Insup Lee · 2023
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Jump-start reinforcement learning
Ikechukwu Uchendu, Ted Xiao, Yao Lu, Banghua Zhu, Mengyuan Yan, Joséphine Simon, Matthew Bennice, Chuyuan Fu, Cong Ma, Jiantao Jiao, et al · 2023
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From imitation to refinement–residual rl for precise visual assembly
Lars Ankile, Anthony Simeonov, Idan Shenfeld, Marcel Torne, and Pulkit Agrawal · 2024
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Transic: Sim-to-real policy transfer by learning from online correction
Yunfan Jiang, Chen Wang, Ruohan Zhang, Jiajun Wu, and Li Fei-Fei · 2024
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Behavior generation with latent actions
Seungjae Lee, Yibin Wang, Haritheja Etukuru, H Jin Kim, Nur Muhammad Mahi Shafiullah, and Lerrel Pinto · 2024
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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 · 2024
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Diffusion policy policy optimization
Allen Z Ren, Justin Lidard, Lars L Ankile, Anthony Simeonov, Pulkit Agrawal, Anirudha Majumdar, Benjamin Burchfiel, Hongkai Dai, and Max Simchowitz · 2024
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Understanding reinforcement learning-based fine-tuning of diffusion models: A tutorial and review
Masatoshi Uehara, Yulai Zhao, Tommaso Biancalani, and Sergey Levine · 2024
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