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Research in cognitive science has provided extensive evidence of human cognitive ability in performing physical reasoning of objects from noisy perceptual inputs.
Naive beliefs in “sophisticated” subjects: Misconceptions about trajectories of objects
Alfonso Caramazza, Michael McCloskey, and Bert Green · 1981
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The development of causal reasoning
Merry Bullock, Rochel Gelman, and Renée Baillargeon · 1982
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Intuitive physics: The straight-down belief and its origin
Michael McCloskey, Allyson Washburn, and Linda Felch · 1983
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Intuitive physics: the straight-down belief and its origin
Michael McCloskey, Allyson Washburn, and Linda Felch · 1983
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Spatiotemporal continuity and the perception of causality in infants
Alan M Leslie · 1984
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Heuristic judgment of mass ratio in two-body collisions
David L Gilden and Dennis R Proffitt · 1994
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The origin of concepts
Susan Carey · 2000
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Visual perception of dynamic properties: Cue heuristics versus direct-perceptual competence
Sverker Runeson, Peter Juslin, and Henrik Olsson · 2000
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Infants’ physical world
Renée Baillargeon · 2004
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Mechanical reasoning by mental simulation
Mary Hegarty · 2004
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Safe, stable and intuitive control for physical human-robot interaction
Vincent Duchaine and Clément Gosselin · 2009
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Simulation as an engine of physical scene understanding
Peter W Battaglia, Jessica B Hamrick, and Joshua B Tenenbaum · 2013
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Reconciling intuitive physics and newtonian mechanics for colliding objects
Adam N Sanborn, Vikash K Mansinghka, and Thomas L Griffiths · 2013
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Beyond point clouds: Scene understanding by reasoning geometry and physics
Bo Zheng, Yibiao Zhao, Joey C Yu, Katsushi Ikeuchi, and Song-Chun Zhu · 2013
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3d reasoning from blocks to stability
Zhaoyin Jia, Andrew C Gallagher, Ashutosh Saxena, and Tsuhan Chen · 2014
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Humans predict liquid dynamics using probabilistic simulation
Christopher Bates, Peter W Battaglia, Ilker Yildirim, and Joshua B Tenenbaum · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Galileo: Perceiving physical object properties by integrating a physics engine with deep learning
Jiajun Wu, Ilker Yildirim, Joseph J Lim, Bill Freeman, and Josh Tenenbaum · 2015
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Scene understanding by reasoning stability and safety
Bo Zheng, Yibiao Zhao, Joey Yu, Katsushi Ikeuchi, and Song-Chun Zhu · 2015
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Interaction networks for learning about objects, relations and physics
Peter W. Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, and Koray Kavukcuoglu · 2016
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A compositional object-based approach to learning physical dynamics
Michael B Chang, Tomer Ullman, Antonio Torralba, and Joshua B Tenenbaum · 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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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 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
Intphys: A framework and benchmark for visual intuitive physics reasoning
Ronan Riochet, Mario Ynocente Castro, Mathieu Bernard, Adam Lerer, Rob Fergus, Véronique Izard, and Emmanuel Dupoux · 2018
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Inferring 3d shapes of unknown rigid objects in clutter through inverse physics reasoning
Changkyu Song and Abdeslam Boularias · 2018
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Interpretable intuitive physics model
Tian Ye, Xiaolong Wang, James Davidson, and Abhinav Gupta · 2018
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Unsupervised learning of latent physical properties using perception-prediction networks
David Zheng, Vinson Luo, Jiajun Wu, and Joshua B Tenenbaum · 2018
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Phyre: A new benchmark for physical reasoning
Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, and Ross Girshick · 2019
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Newtonian scene understanding: Unfolding the dynamics of objects in static images
Roozbeh Mottaghi, Hessam Bagherinezhad, Mohammad Rastegari, and Ali Farhadi · 2016
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”what happens if…” learning to predict the effect of forces in images
Roozbeh Mottaghi, Mohammad Rastegari, Abhinav Kumar Gupta, and Ali Farhadi · 2016
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Physics 101: Learning physical object properties from unlabeled videos
Jiajun Wu, Joseph J. Lim, Hongyi Zhang, Joshua B. Tenenbaum, and William T. Freeman · 2016
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Intuitive physics: Current research and controversies
James R Kubricht, Keith J Holyoak, and Hongjing Lu · 2017
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Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
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Mind games: Game engines as an architecture for intuitive physics
Tomer D Ullman, Elizabeth Spelke, Peter Battaglia, and Joshua B Tenenbaum · 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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Unsupervised intuitive physics from past experiences
Sébastien Ehrhardt, Aron Monszpart, Niloy J Mitra, and Andrea Vedaldi · 2019
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Unsupervised intuitive physics from past experiences
Sébastien Ehrhardt, Aron Monszpart, Niloy Jyoti Mitra, and Andrea Vedaldi · 2019
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Physics-as-inverse-graphics: Unsupervised physical parameter estimation from video
Miguel Jaques, Michael Burke, and Timothy Hospedales · 2019
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Modeling expectation violation in intuitive physics with coarse probabilistic object representations
Kevin Smith, Lingjie Mei, Shunyu Yao, Jiajun Wu, Elizabeth Spelke, Joshua Tenenbaum, and Tomer Ullman · 2019
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Craft: A benchmark for causal reasoning about forces and interactions
Tayfun Ates, Muhammed Samil Atesoglu, Cagatay Yigit, Ilker Kesen, Mert Kobas, Erkut Erdem, Aykut Erdem, Tilbe Goksun, and Deniz Yuret · 2020
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Cophy: Counterfactual learning of physical dynamics
Fabien Baradel, Natalia Neverova, Julien Mille, Greg Mori, and Christian Wolf · 2020
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Causal world models by unsupervised deconfounding of physical dynamics
Minne Li, Mengyue Yang, Furui Liu, Xu Chen, Zhitang Chen, and Jun Wang · 2020
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Visual grounding of learned physical models
Yunzhu Li, Toru Lin, Kexin Yi, Daniel Bear, Daniel Yamins, Jiajun Wu, Joshua Tenenbaum, and Antonio Torralba · 2020
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Learning 3d dynamic scene representations for robot manipulation
Zhenjia Xu, Zhanpeng He, Jiajun Wu, and Shuran Song · 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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Physion: Evaluating physical prediction from vision in humans and machines
Daniel M Bear, Elias Wang, Damian Mrowca, Felix J Binder, Hsiau-Yu Fish Tung, RT Pramod, Cameron Holdaway, Sirui Tao, Kevin Smith, Fan-Yun Sun, et al · 2021
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A benchmark for modeling violation-of-expectation in physical reasoning across event categories
Arijit Dasgupta, Jiafei Duan, Marcelo H Ang Jr, Yi Lin, Su-hua Wang, Renée Baillargeon, and Cheston Tan · 2021
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Pip: Physical interaction prediction via mental imagery with span selection
Jiafei Duan, Samson Yu, Soujanya Poria, Bihan Wen, and Cheston Tan · 2021
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A survey of embodied ai: From simulators to research tasks
Jiafei Duan, Samson Yu, Hui Li Tan, Hongyuan Zhu, and Cheston Tan · 2021
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A bayesian-symbolic approach to reasoning and learning in intuitive physics
Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer Ullman, Josh Tenenbaum, and Charles Sutton · 2021
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Physical representation learning and parameter identification from video using differentiable physics
Rama Krishna Kandukuri, Jan Achterhold, Michael Moeller, and Joerg Stueckler · 2022
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