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While imitation learning provides us with an efficient toolkit to train robots, learning skills that are robust to environment variations remains a significant challenge.
A markovian decision process
Richard Bellman · 1957
Earlier work this paper cites.
Locally weighted learning
Christopher G Atkeson, Andrew W Moore, and Stefan Schaal · 1997
Earlier work this paper cites.
An autonomous land vehicle in a neural network
D Pomerleau · 1998
Earlier work this paper cites.
Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart J Russell, et al · 2000
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
Earlier work this paper cites.
Imitation learning for locomotion and manipulation
Nathan Ratliff, J Andrew Bagnell, and Siddhartha S Srinivasa · 2007
Earlier work this paper cites.
The sinkhorn–knopp algorithm: convergence and applications
Philip A Knight · 2008
Earlier work this paper cites.
A survey of robot learning from demonstration
Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Optimal transport: old and new , volume 338
Cédric Villani · 2009
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
Earlier work this paper cites.
Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
Earlier work this paper cites.
Gromov-wasserstein averaging of kernel and distance matrices
Gabriel Peyré, Marco Cuturi, and Justin Solomon · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Earlier work this paper cites.
Soft-dtw: a differentiable loss function for time-series
Marco Cuturi and Mathieu Blondel · 2017
Earlier work this paper cites.
Learning robust rewards with adversarial inverse reinforcement learning
Justin Fu, Katie Luo, and Sergey Levine · 2017
Earlier work this paper cites.
Imitation learning: A survey of learning methods
Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, and Chrisina Jayne · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Ilya Kostrikov, Kumar Krishna Agrawal, Debidatta Dwibedi, Sergey Levine, and Jonathan Tompson · 2018
Earlier work this paper cites.
Overcoming exploration in reinforcement learning with demonstrations
Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
Earlier work this paper cites.
On the convergence and robustness of training gans with regularized optimal transport
Maziar Sanjabi, Jimmy Ba, Meisam Razaviyayn, and Jason D Lee · 2018
Cited alongside, same era.
Multiple interactions made easy (mime): Large scale demonstrations data for imitation
Pratyusha Sharma, Lekha Mohan, Lerrel Pinto, and Abhinav Gupta · 2018
Cited alongside, same era.
Tom Silver, Kelsey Allen, Josh Tenenbaum, and Leslie Kaelbling · 2018
Cited alongside, same era.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Cited alongside, same era.
Exploring the limitations of behavior cloning for autonomous driving
Felipe Codevilla, Eder Santana, Antonio M López, and Adrien Gaidon · 2019
Cited alongside, same era.
Survey of imitation learning for robotic manipulation
Residual reinforcement learning from demonstrations
Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal, Jean Ponce, and Cordelia Schmid · 2021
Later among the works it cites.
Domain-robust visual imitation learning with mutual information constraints
Edoardo Cetin and Oya Celiktutan · 2021
Later among the works it cites.
Aligning time series on incomparable spaces
Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, and Marc Deisenroth · 2021
Later among the works it cites.
Cross-domain imitation learning via optimal transport
Arnaud Fickinger, Samuel Cohen, Stuart Russell, and Brandon Amos · 2021
Later among the works it cites.
Coarse-to-fine imitation learning: Robot manipulation from a single demonstration
Edward Johns · 2021
Later among the works it cites.
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Bin Fang, Shidong Jia, Di Guo, Muhua Xu, Shuhuan Wen, and Fuchun Sun · 2019
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.
Computational optimal transport: With applications to data science
Gabriel Peyré, Marco Cuturi, et al · 2019
Cited alongside, same era.
Learning vision-based flight in drone swarms by imitation
Fabian Schilling, Julien Lecoeur, Fabrizio Schiano, and Dario Floreano · 2019
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Recent advances in imitation learning from observation
Faraz Torabi, Garrett Warnell, and Peter Stone · 2019
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Reinforced cross-modal matching and self-supervised imitation learning for vision-language navigation
Xin Wang, Qiuyuan Huang, Asli Celikyilmaz, Jianfeng Gao, Dinghan Shen, Yuan-Fang Wang, William Yang Wang, and Lei Zhang · 2019
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Wasserstein adversarial imitation learning
Huang Xiao, Michael Herman, Joerg Wagner, Sebastian Ziesche, Jalal Etesami, and Thai Hong Linh · 2019
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What matters in learning from offline human demonstrations for robot manipulation
Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín · 2021
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The surprising effectiveness of representation learning for visual imitation
Jyothish Pari, Nur Muhammad Shafiullah, Sridhar Pandian Arunachalam, and Lerrel Pinto · 2021
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State-only imitation learning for dexterous manipulation
Ilija Radosavovic, Xiaolong Wang, Lerrel Pinto, and Jitendra Malik · 2021
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Visual adversarial imitation learning using variational models
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, and Chelsea Finn · 2021
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Behavioral cloning from noisy demonstrations
Fumihiro Sasaki and Ryota Yamashina · 2021
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Mastering visual continuous control: Improved data-augmented reinforcement learning
Denis Yarats, Rob Fergus, Alessandro Lazaric, and Lerrel Pinto · 2021
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Imitation learning from pixel observations for continuous control, 2022
Samuel Cohen, Brandon Amos, Marc Peter Deisenroth, Mikael Henaff, Eugene Vinitsky, and Denis Yarats · 2022
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From play to policy: Conditional behavior generation from uncurated robot data
Zichen Jeff Cui, Yibin Wang, Nur Muhammad, Lerrel Pinto, et al · 2022
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Implicit behavioral cloning
Pete Florence, Corey Lynch, Andy Zeng, Oscar A Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, and Jonathan Tompson · 2022
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Watch and match: Supercharging imitation with regularized optimal transport
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Voila: Visual-observation-only imitation learning for autonomous navigation
Haresh Karnan, Garrett Warnell, Xuesu Xiao, and Peter Stone · 2022
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Graph inverse reinforcement learning from diverse videos
Sateesh Kumar, Jonathan Zamora, Nicklas Hansen, Rishabh Jangir, and Xiaolong Wang · 2022
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Towards more generalizable one-shot visual imitation learning
Zhao Mandi, Fangchen Liu, Kimin Lee, and Pieter Abbeel · 2022
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R3m: A universal visual representation for robot manipulation
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, and Abhinav Gupta · 2022
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Real-world robot learning with masked visual pre-training
Ilija Radosavovic, Tete Xiao, Stephen James, Pieter Abbeel, Jitendra Malik, and Trevor Darrell · 2022
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Behavior transformers: Cloning k k modes with one stone
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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 · 2022
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Masked visual pre-training for motor control
Tete Xiao, Ilija Radosavovic, Trevor Darrell, and Jitendra Malik · 2022
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Playful interactions for representation learning
Sarah Young, Jyothish Pari, Pieter Abbeel, and Lerrel Pinto · 2022
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