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We propose an action-conditioned dynamics model that predicts scene changes caused by object and agent interactions in a viewpoint-invariant 3D neural scene representation space, inferred from RGB-D videos.
Robonet: Large-scale multi-robot learning
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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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Deep multi-scale video prediction beyond mean square error
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Action-conditional video prediction using deep networks in atari games
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ShapeNet: An Information-Rich 3D Model Repository
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Interaction networks for learning about objects, relations and physics
P. W. Battaglia, R. Pascanu, M. Lai, D. J. Rezende, and K. Kavukcuoglu · 2016
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Deep visual foresight for planning robot motion
C. Finn and S. Levine · 2016
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Learning to poke by poking: Experiential learning of intuitive physics
P. Agrawal, A. Nair, P. Abbeel, J. Malik, and S. Levine · 2016
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The curious robot: Learning visual representations via physical interactions
L. Pinto, D. Gandhi, Y. Han, Y. Park, and A. Gupta · 2016
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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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Unsupervised learning for physical interaction through video prediction
C. Finn, I. Goodfellow, and S. Levine · 2016
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SE3-Nets: Learning rigid body motion using deep neural networks
A. Byravan and D. Fox · 2016
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More than a million ways to be pushed: A high-fidelity experimental data set of planar pushing
K. Yu, M. Bauzá, N. Fazeli, and A. Rodriguez · 2016
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Self-supervised visual planning with temporal skip connections
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Vision-as-inverse-graphics: Obtaining a rich 3d explanation of a scene from a single image
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L. Romaszko, C. K. I. Williams, P. Moreno, and P. Kohli · 2017
Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
F. Ebert, C. Finn, S. Dasari, A. Xie, A. X. Lee, and S. Levine · 2018
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Relational inductive biases, deep learning, and graph networks
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al · 2018
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Flexible neural representation for physics prediction
D. Mrowca, C. Zhuang, E. Wang, N. Haber, L. Fei-Fei, J. B. Tenenbaum, and D. L. Yamins · 2018
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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
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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
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Learning spatial common sense with geometry-aware recurrent networks
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Embodied view-contrastive 3d feature learning
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Learning latent dynamics for planning from pixels
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Object-centric forward modeling for model predictive control
Y. Ye, D. Gandhi, A. Gupta, and S. Tulsiani · 2019
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Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids
Y. Li, J. Wu, R. Tedrake, J. B. Tenenbaum, and A. Torralba · 2019
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Tossingbot: Learning to throw arbitrary objects with residual physics
A. Zeng, S. Song, J. Lee, A. Rodriguez, and T. A. Funkhouser · 2019
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Dynamics learning with cascaded variational inference for multi-step manipulation, 2019
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Object-centric forward modeling for model predictive control, 2019
Y. Ye, D. Gandhi, A. Gupta, and S. Tulsiani · 2019
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Learning to simulate complex physics with graph networks, 02 2020
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