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Visual foresight gives an agent a window into the future, which it can use to anticipate events before they happen and plan strategic behavior.
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Recognizing human actions: a local SVM approach
C. Schüldt, I. Laptev, and B. Caputo · 2004
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Visualizing data using t-sne
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Deep multi-scale video prediction beyond mean square error
M. Mathieu, C. Couprie, and Y. LeCun · 2015
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Action-conditional video prediction using deep networks in atari games
J. Oh, X. Guo, H. Lee, R. L. Lewis, and S. Singh · 2015
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Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, et al · 2016
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H. Edwards and A. Storkey · 2016
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Pixel recurrent neural networks
A. v. d. Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
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Unsupervised learning for physical interaction through video prediction
C. Finn, I. Goodfellow, and S. Levine · 2016
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Rl 2 : Fast reinforcement learning via slow reinforcement learning
Y. Duan, J. Schulman, X. Chen, P. L. Bartlett, I. Sutskever, and P. Abbeel · 2016
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Deep visual foresight for planning robot motion
C. Finn and S. Levine · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
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Se3-nets: Learning rigid body motion using deep neural networks
A. Byravan and D. Fox · 2017
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Learning to generate long-term future via hierarchical prediction
R. Villegas, J. Yang, Y. Zou, S. Sohn, X. Lin, and H. Lee · 2017
Cited alongside, same era.
Video pixel networks
N. Kalchbrenner, A. van den Oord, K. Simonyan, I. Danihelka, O. Vinyals, A. Graves, and K. Kavukcuoglu · 2017
Solar: deep structured representations for model-based reinforcement learning
M. Zhang, S. Vikram, L. Smith, P. Abbeel, M. Johnson, and S. Levine · 2018
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Hierarchical long-term video prediction without supervision
N. Wichers, R. Villegas, D. Erhan, and H. Lee · 2018
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D. Ha and J. Schmidhuber · 2018
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Few-shot goal inference for visuomotor learning and planning
A. Xie, A. Singh, S. Levine, and C. Finn · 2018
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Stochastic video generation with a learned prior
E. Denton and R. Fergus · 2018
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Cited alongside, same era.
Self-supervised visual planning with temporal skip connections
F. Ebert, C. Finn, A. X. Lee, and S. Levine · 2017
Cited alongside, same era.
Stochastic variational video prediction
M. Babaeizadeh, C. Finn, D. Erhan, R. H. Campbell, and S. Levine · 2017
Cited alongside, same era.
Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
F. Ebert, C. Finn, S. Dasari, A. Xie, A. Lee, and S. Levine · 2018
Cited alongside, same era.
Zero-shot visual imitation
D. Pathak, P. Mahmoudieh, G. Luo, P. Agrawal, D. Chen, Y. Shentu, E. Shelhamer, J. Malik, A. A. Efros, and T. Darrell · 2018
Cited alongside, same era.
Task-embedded control networks for few-shot imitation learning
S. James, M. Bloesch, and A. J. Davison · 2018
Cited alongside, same era.
Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
A. Nagabandi, I. Clavera, S. Liu, R. S. Fearing, P. Abbeel, S. Levine, and C. Finn
Cited in the paper.
A. X. Lee, R. Zhang, F. Ebert, P. Abbeel, C. Finn, and S. Levine · 2018
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The variational homoencoder: Learning to learn high capacity generative models from few examples
L. B. Hewitt, M. I. Nye, A. Gane, T. Jaakkola, and J. B. Tenenbaum · 2018
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F. Alet, T. Lozano-Pérez, and L. P. Kaelbling · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang · 2018
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Videoflow: A flow-based generative model for video
M. Kumar, M. Babaeizadeh, D. Erhan, C. Finn, S. Levine, L. Dinh, and D. Kingma · 2019
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Omnipush: accurate, diverse, real-world dataset of pushing dynamics with rgb-d video
M. Bauza, F. Alet, Y. Lin, T. Lozano-Perez, L. Kaelbling, P. Isola, and A. Rodriguez · 2019
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