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Imitation learning is the process by which one agent tries to learn how to perform a certain task using information generated by another, often more-expert agent performing that same task.
Learning from demonstration
Stefan Schaal · 1997
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Learning agents for uncertain environments
Stuart Russell · 1998
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 1998
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A framework for behavioural claning
Michael Bain and Claude Sommut · 1999
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Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart J Russell, et al · 2000
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Trajectory formation for imitation with nonlinear dynamical systems
Auke Jan Ijspeert, Jun Nakanishi, and Stefan Schaal · 2001
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Humanoid robot learning and game playing using pc-based vision
Darrin C Bentivegna, Ales Ude, Christopher G Atkeson, and Gordon Cheng · 2002
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Movement imitation with nonlinear dynamical systems in humanoid robots
Auke Jan Ijspeert, Jun Nakanishi, and Stefan Schaal · 2002
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
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Learning from demonstration and adaptation of biped locomotion
Jun Nakanishi, Jun Morimoto, Gen Endo, Gordon Cheng, Stefan Schaal, and Mitsuo Kawato · 2004
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Incremental learning of gestures by imitation in a humanoid robot
Sylvain Calinon and Aude Billard · 2007
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Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey · 2008
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A survey of robot learning from demonstration
Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2009
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Motion capture in robotics review
Matthew Field, David Stirling, Fazel Naghdy, and Zengxi Pan · 2009
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Efficient reductions for imitation learning
Stéphane Ross and Drew Bagnell · 2010
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Marionet: Motion acquisition for robots through iterative online evaluative training
Adam Setapen, Michael Quinlan, and Peter Stone · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey J Gordon, and Drew Bagnell · 2011
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Synthesis and stabilization of complex behaviors through online trajectory optimization
Yuval Tassa, Tom Erez, and Emanuel Todorov · 2012
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
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Learning transferable policies for monocular reactive mav control
Shreyansh Daftry, J Andrew Bagnell, and Martial Hebert · 2016
Cited alongside, same era.
Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
Cited alongside, same era.
A machine learning approach to visual perception of forest trails for mobile robots
Alessandro Giusti, Jérôme Guzzi, Dan C Cireşan, Fang-Lin He, Juan P Rodríguez, Flavio Fontana, Matthias Faessler, Christian Forster, Jürgen Schmidhuber, Gianni Di Caro, et al · 2016
Cited alongside, same era.
Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
Cited alongside, same era.
A deep learning framework for character motion synthesis and editing
Daniel Holden, Jun Saito, and Taku Komura · 2016
Cited alongside, same era.
Shuffle and learn: unsupervised learning using temporal order verification
An algorithmic perspective on imitation learning
Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J Andrew Bagnell, Pieter Abbeel, Jan Peters, et al · 2018
Later among the works it cites.
Zero-shot visual imitation
Deepak Pathak, Parsa Mahmoudieh, Michael Luo, Pulkit Agrawal, Dian Chen, Fred Shentu, Evan Shelhamer, Jitendra Malik, Alexei A. Efros, and Trevor Darrell · 2018
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Deepmimic: Example-guided deep reinforcement learning of physics-based character skills
Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne · 2018
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Sfv: Reinforcement learning of physical skills from videos
Xue Bin Peng, Angjoo Kanazawa, Jitendra Malik, Pieter Abbeel, and Sergey Levine · 2018
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Time-contrastive networks: Self-supervised learning from video
Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, and Sergey Levine · 2018
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Behavioral cloning from observation
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Ishan Misra, C Lawrence Zitnick, and Martial Hebert · 2016
Cited alongside, same era.
Realtime multi-person 2d pose estimation using part affinity fields
Zhe Cao, Tomas Simon, Shih-En Wei, and Yaser Sheikh · 2017
Cited alongside, same era.
Grounded action transformation for robot learning in simulation
Josiah P Hanna and Peter Stone · 2017
Cited alongside, same era.
Learning human behaviors from motion capture by adversarial imitation
Josh Merel, Yuval Tassa, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, and Nicolas Heess · 2017
Cited alongside, same era.
Combining self-supervised learning and imitation for vision-based rope manipulation
Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2017
Cited alongside, same era.
Third-person imitation learning
Bradly C Stadie, Pieter Abbeel, and Ilya Sutskever · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
Cited alongside, same era.
Faraz Torabi, Garrett Warnell, and Peter Stone · 2018
Later among the works it cites.
Deep visual domain adaptation: A survey
Mei Wang and Weihong Deng · 2018
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Reinforcement and imitation learning for diverse visuomotor skills
Yuke Zhu, Ziyu Wang, Josh Merel, Andrei Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell, Nando de Freitas, et al · 2018
Later among the works it cites.
Reinforced imitation learning from observations
Konrad Zolna, Negar Rostamzadeh, Yoshua Bengio, Sungjin Ahn, and Pedro O Pinheiro · 2018
Later among the works it cites.
Imitating latent policies from observation
Ashley D Edwards, Himanshu Sahni, Yannick Schroeker, and Charles L Isbell · 2019
Closest in time.
One-shot learning of multi-step tasks from observation via activity localization in auxiliary video
Wonjoon Goo and Scott Niekum · 2019
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Hybrid reinforcement learning with expert state sequences
Xiaoxiao Guo, Shiyu Chang, Mo Yu, Gerald Tesauro, and Murray Campbell · 2019
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Ridm: Reinforced inverse dynamics modeling for learning from a single observed demonstration
Brahma Pavse, Faraz Torabi, Josiah Hanna, Garrett Warnell, and Peter Stone · 2019
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Provably efficient imitation learning from observation alone
Wei Sun, Hanzhang Hul, Byron Boots, and J Andrew Bagnell · 2019
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Sample-efficient adversarial imitation learning from observation
Faraz Torabi, Sean Geiger, Garrett Warnell, and Peter Stone · 2019
Closest in time.
Adversarial imitation learning from state-only demonstrations
Faraz Torabi, Garrett Warnell, and Peter Stone · 2019
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Generative adversarial imitation from observation
Faraz Torabi, Garrett Warnell, and Peter Stone · 2019
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Imitation learning from video by leveraging proprioception
Faraz Torabi, Garrett Warnell, and Peter Stone · 2019
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3d human pose machines with self-supervised learning
Keze Wang, Liang Lin, Chenhan Jiang, Chen Qian, and Pengxu Wei · 2019
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