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Physical reasoning requires forward prediction: the ability to forecast what will happen next given some initial world state.
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Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
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Learning visual predictive models of physics for playing billiards
Katerina Fragkiadaki, Pulkit Agrawal, Sergey Levine, and Jitendra Malik · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Learning physical intuition of block towers by example
Adam Lerer, Sam Gross, and Rob Fergus · 2016
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To fall or not to fall: A visual approach to physical stability prediction
Wenbin Li, Seyedmajid Azimi, Aleš Leonardis, and Mario Fritz · 2016
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"what happens if…" learning to predict the effect of forces in images
Roozbeh Mottaghi, Mohammad Rastegari, Abhinav Gupta, and Ali Farhadi · 2016
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Arunkumar Byravan and Dieter Fox · 2017
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Joao Carreira and Andrew Zisserman · 2017
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A compositional object-based approach to learning physical dynamics
Michael Chang, Tomer Ullman, Antonio Torralba, and Joshua Tenenbaum · 2017
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Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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James R Kubricht, Keith J Holyoak, and Hongjing Lu · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Ruben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin, and Honglak Lee · 2017
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Visual interaction networks: Learning a physics simulator from video
Nicholas Watters, Daniel Zoran, Theophane Weber, Peter Battaglia, Razvan Pascanu, and Andrea Tacchetti · 2017
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Yilun Du and Karthik Narasimhan · 2019
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Genesis: Generative scene inference and sampling with object-centric latent representations
Martin Engelcke, Adam R Kosiorek, Oiwi Parker Jones, and Ingmar Posner · 2019
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Multi-object representation learning with iterative variational inference
Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Christopher Burgess, Daniel Zoran, Loic Matthey, Matthew Botvinick, and Alexander Lerchner · 2019
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Hamiltonian neural networks
Samuel Greydanus, Misko Dzamba, and Jason Yosinski · 2019
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Learning to see physics via visual de-animation
Jiajun Wu, Erika Lu, Pushmeet Kohli, William T Freeman, and Joshua B Tenenbaum · 2017
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Learning and querying fast generative models for reinforcement learning
Lars Buesing, Theophane Weber, Sébastien Racaniere, SM Eslami, Danilo Rezende, David P Reichert, Fabio Viola, Frederic Besse, Karol Gregor, Demis Hassabis, and Daan Wierstra · 2018
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
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Shapestacks: Learning vision-based physical intuition for generalised object stacking
Oliver Groth, Fabian B Fuchs, Ingmar Posner, and Andrea Vedaldi · 2018
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Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
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Neural relational inference for interacting systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel · 2018
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Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Reasoning about physical interactions with object-oriented prediction and planning
Michael Janner, Sergey Levine, William T. Freeman, Joshua B. Tenenbaum, Chelsea Finn, and Jiajun Wu · 2019
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Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids
Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B Tenenbaum, and Antonio Torralba · 2019
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Soft rasterizer: A differentiable renderer for image-based 3D reasoning
Shichen Liu, Tianye Li, Weikai Chen, , and Hao Li · 2019
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Compositional video prediction
Yufei Ye, Maneesh Singh, Abhinav Gupta, and Shubham Tulsiani · 2019
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The Tools challenge: Rapid trial-and-error learning in physical problem solving
Kelsey R Allen, Kevin A Smith, and Joshua B Tenenbaum · 2020
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Cophy: Counterfactual learning of physical dynamics
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Lagrangian neural networks
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Contrastive learning of structured world models
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Visual grounding of learned physical models
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Learning to simulate complex physics with graph networks
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Entity abstraction in visual model-based reinforcement learning
Rishi Veerapaneni, John D Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn, Jiajun Wu, Joshua Tenenbaum, and Sergey Levine · 2020
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CLEVRER: Collision events for video representation and reasoning
Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, and Joshua B Tenenbaum · 2020
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Learning long-term visual dynamics with region proposal interaction networks
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