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We present an approach to learn an object-centric forward model, and show that this allows us to plan for sequences of actions to achieve distant desired goals.
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A compositional object-based approach to learning physical dynamics
M. B. Chang, T. Ullman, A. Torralba, and J. B. Tenenbaum · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Interaction networks for learning about objects, relations and physics
P. Battaglia, R. Pascanu, M. Lai, D. J. Rezende, et al · 2016
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Self-supervised visual planning with temporal skip connections
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Neural relational inference for interacting systems
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Reasoning about physical interactions with object-oriented prediction and planning
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Compositional video prediction
Y. Ye, M. Singh, A. Gupta, and S. Tulsiani · 2019
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Densephysnet: Learning dense physical object representations via multi-step dynamic interactions
Z. Xu, J. Wu, A. Zeng, J. B. Tenenbaum, and S. Song · 2019
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