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Agents that can learn to imitate given video observation -- \emph{without direct access to state or action information} are more applicable to learning in the natural world.
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Michael Gleicher · 1998
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Learning and synthesizing human body motion and posture
Rómer Rosales and Stan Sclaroff · 2000
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From the perception of action to the understanding of intention
Sarah-Jayne Blakemore and Jean Decety · 2001
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Interactive control of avatars animated with human motion data
Jehee Lee, Jinxiang Chai, Paul SA Reitsma, Jessica K Hodgins, and Nancy S Pollard · 2002
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y. Ng · 2004
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Learning a similarity metric discriminatively, with application to face verification
Sumit Chopra, Raia Hadsell, and Yann LeCun · 2005
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A survey of robot learning from demonstration
Brenna D. Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2008
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Maximum entropy inverse reinforcement learning
Brian D. Ziebart, Andrew Maas, J. Andrew Bagnell, and Anind K. Dey · 2008
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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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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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A recurrent latent variable model for sequential data
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C Courville, and Yoshua Bengio · 2015
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Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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Supervised representation learning: Transfer learning with deep autoencoders
Fuzhen Zhuang, Xiaohu Cheng, Ping Luo, Sinno Jialin Pan, and Qing He · 2015
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Deep learning representation using autoencoder for 3d shape retrieval
Zhuotun Zhu, Xinggang Wang, Song Bai, Cong Yao, and Xiang Bai · 2016
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Combining model-based and model-free updates for trajectory-centric reinforcement learning
Yevgen Chebotar, Karol Hausman, Marvin Zhang, Gaurav Sukhatme, Stefan Schaal, and Sergey Levine · 2017
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Infogail: Interpretable imitation learning from visual demonstrations
Yunzhu Li, Jiaming Song, and Stefano Ermon · 2017
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Learning human behaviors from motion capture by adversarial imitation
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
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Sim-to-real: Learning agile locomotion for quadruped robots
Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, and Vincent Vanhoucke · 2018
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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
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Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations
Daniel Brown, Wonjoon Goo, Prabhat Nagarajan, and Scott Niekum · 2019
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Imitating latent policies from observation
Ashley Edwards, Himanshu Sahni, Yannick Schroecker, and Charles Isbell · 2019
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Josh Merel, Yuval Tassa, Dhruva TB, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, and Nicolas Heess · 2017
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Learning locomotion skills using deeprl: Does the choice of action space matter?
Xue Bin Peng and Michiel van de Panne · 2017
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Robust imitation of diverse behaviors
Ziyu Wang, Josh S Merel, Scott E Reed, Nando de Freitas, Gregory Wayne, and Nicolas Heess · 2017
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Learning actionable representations from visual observations
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Visual reinforcement learning with imagined goals
Ashvin V Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Third-person visual imitation learning via decoupled hierarchical controller
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Better-than-demonstrator imitation learning via automatically-ranked demonstrations
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Woofer and pupper: Low-cost open-source quadrupeds for research and education
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Amp: Adversarial motion priors for stylized physics-based character control
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