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Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works, and then use this learned model to plan coordinated sequences of actions to bring about desired outcomes.
Premotor cortex and the recognition of motor actions
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William Lotter, Gabriel Kreiman, and David Cox · 2016
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Deep multi-scale video prediction beyond mean square error
M. Mathieu, C. Couprie, and Y. LeCun · 2016
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Tianfan Xue, Jiajun Wu, Katherine L. Bouman, and William T. Freeman · 2016
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Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Krishnan · 2017
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Baoyang Chen, Wenmin Wang, Jinzhuo Wang, and Xiongtao Chen · 2017
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Silvia Chiappa, Sébastien Racanière, Daan Wierstra, and Shakir Mohamed · 2017
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Chelsea Finn and Sergey Levine · 2017
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Video frame synthesis using deep voxel flow
Ziwei Liu, Raymond A Yeh, Xiaoou Tang, Yiming Liu, and Aseem Agarwala · 2017
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Faraz Torabi, Garrett Warnell, and Peter Stone · 2018
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Nevan Wichers, Ruben Villegas, Dumitru Erhan, and Honglak Lee · 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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Unsupervised Cross-Domain Image Generation
Yaniv Taigman, Adam Polyak, and Lior Wolf · 2017
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