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The fundamental limitation of the behavioral cloning (BC) approach to imitation learning is that it only teaches an agent what the expert did at states the expert visited.
Alvinn: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1988
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Planning and acting in partially observable stochastic domains
Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra · 1998
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Reinforcement learning: An introduction , volume 1
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Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart Russell, et al · 2000
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A sparse sampling algorithm for near-optimal planning in large markov decision processes
Michael Kearns, Yishay Mansour, and Andrew Y Ng · 2002
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Policy search by dynamic programming
James Bagnell, Sham M Kakade, Jeff Schneider, and Andrew Ng · 2003
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A game-theoretic approach to apprenticeship learning
Umar Syed and Robert E Schapire · 2007
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Maximum entropy inverse reinforcement learning
Brian D Ziebart, J Andrew Bagnell, Anind K Dey, et al · 2008
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Learning to search: Functional gradient techniques for imitation learning
Nathan D Ratliff, David Silver, and J Andrew Bagnell · 2009
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Follow-the-regularized-leader and mirror descent: Equivalence theorems and l1 regularization
Brendan McMahan · 2011
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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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Agnostic system identification for model-based reinforcement learning
Stephane Ross and J Andrew Bagnell · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Guided policy search
Sergey Levine and Vladlen Koltun · 2013
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Reinforcement and imitation learning via interactive no-regret learning
Stephane Ross and J Andrew Bagnell · 2014
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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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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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Thinking fast and slow with deep learning and tree search
Thomas Anthony, Zheng Tian, and David Barber · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Model predictive path integral control: From theory to parallel computation
Grady Williams, Andrew Aldrich, and Evangelos A Theodorou · 2017
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David Ha and Jürgen Schmidhuber · 2018
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Tom Silver, Kelsey Allen, Josh Tenenbaum, and Leslie Kaelbling · 2018
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Dual policy iteration
Wen Sun, Geoffrey J Gordon, Byron Boots, and J Bagnell · 2018
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Group normalization
Yuxin Wu and Kaiming He · 2018
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Superhuman ai for multiplayer poker
Noam Brown and Tuomas Sandholm · 2019
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Hg-dagger: Interactive imitation learning with human experts
Michael Kelly, Chelsea Sidrane, Katherine Driggs-Campbell, and Mykel J Kochenderfer · 2019
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Mish: A self regularized non-monotonic activation function
Diganta Misra · 2019
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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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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Large language monkeys: Scaling inference compute with repeated sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini · 2024
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Genie: Generative interactive environments
Jake Bruce, Michael D Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, et al · 2024
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Universal manipulation interface: In-the-wild robot teaching without in-the-wild robots
Cheng Chi, Zhenjia Xu, Chuer Pan, Eric Cousineau, Benjamin Burchfiel, Siyuan Feng, Russ Tedrake, and Shuran Song · 2024
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Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Learning from interventions: Human-robot interaction as both explicit and implicit feedback
Jonathan Spencer, Sanjiban Choudhury, Matthew Barnes, Matthew Schmittle, Mung Chiang, Peter Ramadge, and Siddhartha Srinivasa · 2020
Cited alongside, same era.
Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano · 2020
Cited alongside, same era.
robosuite: A modular simulation framework and benchmark for robot learning
Yuke Zhu, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Abhishek Joshi, Soroush Nasiriany, Yifeng Zhu, and Kevin Lin · 2020
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Randomized ensembled double q-learning: Learning fast without a model
Xinyue Chen, Che Wang, Zijian Zhou, and Keith Ross · 2021
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Mastering atari with discrete world models
Danijar Hafner, Timothy P Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2021
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Discovering and achieving goals via world models, 2021
Russell Mendonca, Oleh Rybkin, Kostas Daniilidis, Danijar Hafner, and Deepak Pathak · 2021
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Sudeep Dasari, Oier Mees, Sebastian Zhao, Mohan Kumar Srirama, and Sergey Levine · 2024
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Stream of search (sos): Learning to search in language
Kanishk Gandhi, Denise Lee, Gabriel Grand, Muxin Liu, Winson Cheng, Archit Sharma, and Noah D Goodman · 2024
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Mastering diverse domains through world models, 2024
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2024
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Td-mpc2: Scalable, robust world models for continuous control, 2024
Nicklas Hansen, Hao Su, and Xiaolong Wang · 2024
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V-star: Training verifiers for self-taught reasoners
Arian Hosseini, Xingdi Yuan, Nikolay Malkin, Aaron Courville, Alessandro Sordoni, and Rishabh Agarwal · 2024
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Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al · 2024
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Droid: A large-scale in-the-wild robot manipulation dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair, Ashwin Balakrishna, Sudeep Dasari, Siddharth Karamcheti, Soroush Nasiriany, Mohan Kumar Srirama, Lawrence Yunliang Chen, Kirsty Ellis, et al · 2024
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Openvla: An open-source vision-language-action model
Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al · 2024
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Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0
Abby ONeill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, et al · 2024
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Genie 2: A large-scale foundation world model
J Parker-Holder, P Ball, J Bruce, V Dasagi, K Holsheimer, C Kaplanis, A Moufarek, G Scully, J Shar, J Shi, et al · 2024
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Alexander Popov, Alperen Degirmenci, David Wehr, Shashank Hegde, Ryan Oldja, Alexey Kamenev, Bertrand Douillard, David Nistér, Urs Muller, Ruchi Bhargava, et al · 2024
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Octo: An open-source generalist robot policy
Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, et al · 2024
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Gello: A general, low-cost, and intuitive teleoperation framework for robot manipulators
Philipp Wu, Yide Shentu, Zhongke Yi, Xingyu Lin, and Pieter Abbeel · 2024
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Cosmos world foundation model platform for physical ai
Niket Agarwal, Arslan Ali, Maciej Bala, Yogesh Balaji, Erik Barker, Tiffany Cai, Prithvijit Chattopadhyay, Yongxin Chen, Yin Cui, Yifan Ding, et al · 2025
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Efficient imitation under misspecification
Nicolas Espinosa-Dice, Sanjiban Choudhury, Wen Sun, and Gokul Swamy · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
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π 0.5 \pi_{0.5} : a vision-language-action model with open-world generalization, 2025
Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Manuel Y. Galliker, Dibya Ghosh, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Devin LeBlanc, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Allen Z. Ren, Lucy Xiaoyang Shi, Laura Smith, Jost Tobias Springenberg, Kyle Stachowicz, James Tanner, Quan Vuong, Homer Walke, Anna Walling, Haohuan Wang, Lili Yu, and Ury Zhilinsky · 2025
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All roads lead to likelihood: The value of reinforcement learning in fine-tuning
Gokul Swamy, Sanjiban Choudhury, Wen Sun, Zhiwei Steven Wu, and J Andrew Bagnell · 2025
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Maniskill3: Gpu parallelized robotics simulation and rendering for generalizable embodied ai
Stone Tao, Fanbo Xiang, Arth Shukla, Yuzhe Qin, Xander Hinrichsen, Xiaodi Yuan, Chen Bao, Xinsong Lin, Yulin Liu, Tse kai Chan, Yuan Gao, Xuanlin Li, Tongzhou Mu, Nan Xiao, Arnav Gurha, Viswesh Nagaswamy Rajesh, Yong Woo Choi, Yen-Ru Chen, Zhiao Huang, Roberto Calandra, Rui Chen, Shan Luo, and Hao Su · 2025
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Bootstrapped model predictive control
Yuhang Wang, Hanwei Guo, Sizhe Wang, Long Qian, and Xuguang Lan · 2025
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Policy decorator: Model-agnostic online refinement for large policy model
Xiu Yuan, Tongzhou Mu, Stone Tao, Yunhao Fang, Mengke Zhang, and Hao Su · 2025
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DINO-WM: World models on pre-trained visual features enable zero-shot planning
Gaoyue Zhou, Hengkai Pan, Yann LeCun, and Lerrel Pinto · 2025
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