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We study the problem of learning physical object representations for robot manipulation.
Estimation of inertial parameters of manipulator loads and links
Christopher G Atkeson, Chae H An, and John M Hollerbach · 1986
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Estimation of object inertia parameters on robot pushing operation
Yong Yu, Tetsu Arima, and Showzow Tsujio · 2005
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Bullet physics engine
Erwin Coumans · 2010
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Automated Construction of Robotic Manipulation Programs
Rosen Diankov · 2010
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Scene coordinate regression forests for camera relocalization in rgb-d images
Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon · 2013
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Learning 6d object pose estimation using 3d object coordinates
Eric Brachmann, Alexander Krull, Frank Michel, Stefan Gumhold, Jamie Shotton, and Carsten Rother · 2014
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Shapenet: An information-rich 3d model repository
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 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, William T Freeman, and Joshua B Tenenbaum · 2015
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Learning to poke by poking: Experiential learning of intuitive physics
Pulkit Agrawal, Ashvin Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2016
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Universal correspondence network
Christopher B Choy, JunYoung Gwak, Silvio Savarese, and Manmohan Chandraker · 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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The curious robot: Learning visual representations via physical interactions
Lerrel Pinto, Dhiraj Gandhi, Yuanfeng Han, Yong-Lae Park, and Abhinav Gupta · 2016
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Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks
Tianfan Xue, Jiajun Wu, Katherine Bouman, and William T Freeman · 2016
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Se3-nets: Learning rigid body motion using deep neural networks
Arunkumar Byravan and Dieter Fox · 2017
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Learning to perform physics experiments via deep reinforcement learning
Unsupervised learning of object frames by dense equivariant image labelling
James Thewlis, Hakan Bilen, and Andrea Vedaldi · 2017
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Visual interaction networks
Nicholas Watters, Andrea Tacchetti, Theophane Weber, Razvan Pascanu, Peter Battaglia, and Daniel Zoran · 2017
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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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Preparing for the unknown: Learning a universal policy with online system identification
Wenhao Yu, Jie Tan, C Karen Liu, and Greg Turk · 2017
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3dmatch: Learning the matching of local 3d geometry in range scans
Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, and Jianxiong Xiao · 2017
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Misha Denil, Pulkit Agrawal, Tejas D Kulkarni, Tom Erez, Peter Battaglia, and Nando de Freitas · 2017
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Taking visual motion prediction to new heightfields
Sebastien Ehrhardt, Aron Monszpart, Niloy Mitra, and Andrea Vedaldi · 2017
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Learning to push by grasping: Using multiple tasks for effective learning
Lerrel Pinto and Abhinav Gupta · 2017
Cited alongside, same era.
Self-supervised visual descriptor learning for dense correspondence
Tanner Schmidt, Richard Newcombe, and Dieter Fox · 2017
Cited alongside, same era.
Peter R Florence, Lucas Manuelli, and Russ Tedrake · 2018
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Push-net : Deep planar pushing for objects with unknown physical properties
Jue Kun Li, David Hsu, and Wee Sun Lee · 2018
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Interpretable intuitive physics model
Tian Ye, Xiaolong Wang, James Davidson, and Abhinav Gupta · 2018
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Learning synergies between pushing and grasping with self-supervised deep reinforcement learning
Andy Zeng, Shuran Song, Stefan Welker, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 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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Environment probing interaction policies
Wenxuan Zhou, Lerrel Pinto, and Abhinav Gupta · 2019
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