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Imitation learning is an effective tool for robotic learning tasks where specifying a reinforcement learning (RL) reward is not feasible or where the exploration problem is particularly difficult.
Learning to achieve goals
Leslie Pack Kaelbling · 1993
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A framework for behavioural cloning
Michael Bain and Claude Sammut · 1995
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Learning agents for uncertain environments
Stuart Russell · 1998
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Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart J Russell, et al · 2000
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The mirror-neuron system
Giacomo Rizzolatti and Laila Craighero · 2004
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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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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Cad2rl: Real single-image flight without a single real image
Fereshteh Sadeghi and Sergey Levine · 2016
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One-Shot imitation learning
Yan Duan, Marcin Andrychowicz, Bradly Stadie, Openai Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba · 2017
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One-shot visual imitation learning via meta-learning
Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2017
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Imitation from observation: Learning to imitate behaviors from raw video via context translation
Yuxuan Liu, Abhishek Gupta, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Third-person imitation learning
Bradly C Stadie, Pieter Abbeel, and Ilya Sutskever · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Maximum a posteriori policy optimisation
Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa, Remi Munos, Nicolas Heess, and Martin Riedmiller · 2018
Self-supervised sim-to-real adaptation for visual robotic manipulation
Rae Jeong, Yusuf Aytar, David Khosid, Yuxiang Zhou, Jackie Kay, Thomas Lampe, Konstantinos Bousmalis, and Francesco Nori · 2019
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Planning with goal-conditioned policies
Soroush Nasiriany, Vitchyr Pong, Steven Lin, and Sergey Levine · 2019
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Third-person visual imitation learning via decoupled hierarchical controller
Pratyusha Sharma, Deepak Pathak, and Abhinav Gupta · 2019
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Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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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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Neural probabilistic motor primitives for humanoid control
Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, and Nicolas Heess · 2018
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One-shot high-fidelity imitation: Training large-scale deep nets with rl
Tom Le Paine, Sergio Gómez Colmenarejo, Ziyu Wang, Scott Reed, Yusuf Aytar, Tobias Pfaff, Matt W Hoffman, Gabriel Barth-Maron, Serkan Cabi, David Budden, et al · 2018
Cited alongside, same era.
Zero-shot visual imitation
Deepak Pathak, Parsa Mahmoudieh, Guanghao Luo, Pulkit Agrawal, Dian Chen, Yide Shentu, Evan Shelhamer, Jitendra Malik, Alexei A Efros, and Trevor Darrell · 2018
Cited alongside, same era.
Time-contrastive networks: Self-supervised learning from video
Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine, and Google Brain · 2018
Cited alongside, same era.
One-shot imitation from observing humans via domain-adaptive meta-learning
Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, and Konstantinos Bousmalis · 2019
Cited alongside, same era.
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Transformers for one-shot visual imitation
Sudeep Dasari and Abhinav Gupta · 2020
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CoMic: Complementary task learning & mimicry for reusable skills
Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, and Josh Merel · 2020
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Learning latent plans from play
Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet · 2020
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Learning to generalize across long-horizon tasks from human demonstrations
Ajay Mandlekar, Danfei Xu, Roberto Martín-Martín, Silvio Savarese, and Li Fei-Fei · 2020
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Learning agile robotic locomotion skills by imitating animals
Xue Bin Peng, Erwin Coumans, Tingnan Zhang, Tsang-Wei Lee, Jie Tan, and Sergey Levine · 2020
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Avid: Learning multi-stage tasks via pixel-level translation of human videos
Laura Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, and Sergey Levine · 2020
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Asymmetric self-play for automatic goal discovery in robotic manipulation
Openai OpenAI, Matthias Plappert, Raul Sampedro, Tao Xu, Ilge Akkaya, Vineet Kosaraju, Peter Welinder, Ruben D’Sa, Arthur Petron, Henrique Ponde de Oliveira Pinto, Alex Paino, Hyeonwoo Noh, Lilian Weng, Qiming Yuan, Casey Chu, and Wojciech Zaremba · 2021
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