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Intelligent agents need a physical understanding of the world to predict the impact of their actions in the future.
Fast, robust adaptive control by learning only forward models
Andrew W. Moore · 1992
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Locally weighted learning for control
Christopher G. Atkeson, Andrew W. Moore, and Stefan Schaal · 1997
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Iterative linear quadratic regulator design for nonlinear biological movement systems
Weiwei Li and Emanuel Todorov · 2004
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Rigid Body Dynamics Algorithms
Roy Featherstone · 2007
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Gaussian processes and reinforcement learning for identification and control of an autonomous blimp
J. Ko, D. J. Klein, D. Fox, and D. Haehnel · 2007
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A dynamic simulator for humanoid robots
Tomislav Reichenbach · 2009
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Pilco: A model-based and data-efficient approach to policy search
Marc Deisenroth and Carl E Rasmussen · 2011
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Mujoco: A physics engine for model-based control
E. Todorov, T. Erez, and Y. Tassa · 2012
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Humanoid robots learning to walk faster: From the real world to simulation and back
Alon Farchy, Samuel Barrett, Patrick MacAlpine, and Peter Stone · 2013
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Approximate real-time optimal control based on sparse gaussian process models
J. Boedecker, J. T. Springenberg, J. Wülfing, and M. Riedmiller · 2014
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The stan math library: Reverse-mode automatic differentiation in C++
Bob Carpenter, Matthew D. Hoffman, Marcus Brubaker, Daniel Lee, Peter Li, and Michael Betancourt · 2015
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Physically consistent state estimation and system identification for contacts
Svetoslav Kolev and Emanuel Todorov · 2015
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 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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Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
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Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
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Aggressive driving with model predictive path integral control
G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou · 2016
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End-to-end differentiable physics for learning and control
Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum, and J. Zico Kolter · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Flexible neural representation for physics prediction
Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B Tenenbaum, and Daniel LK Yamins · 2018
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Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia · 2018
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Spnets: Differentiable fluid dynamics for deep neural networks
Connor Schenck and Dieter Fox · 2018
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Neural networks and differential dynamic programming for reinforcement learning problems
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OptNet: Differentiable optimization as a layer in neural networks
Brandon Amos and J. Zico Kolter · 2017
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Survey of model-based reinforcement learning: Applications on robotics
Athanasios S. Polydoros and Lazaros Nalpantidis · 2017
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Learning to see physics via visual de-animation
Jiajun Wu, Erika Lu, Pushmeet Kohli, Bill Freeman, and Josh Tenenbaum · 2017
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Analytical derivatives of rigid body dynamics algorithms
Justin Carpentier and Nicolas Mansard · 2018
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Closing the sim-to-real loop: Adapting simulation randomization with real world experience
Yevgen Chebotar, Ankur Handa, Viktor Makoviychuk, Miles Macklin, Jan Issac, Nathan D. Ratliff, and Dieter Fox · 2018
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Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, Timothy P. Lillicrap, and Martin A. Riedmiller · 2018
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Fast model identification via physics engines for data-efficient policy search
Shaojun Zhu, Andrew Kimmel, Kostas E. Bekris, and Abdeslam Boularias · 2018
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A differentiable physics engine for deep learning in robotics
Jonas Degrave, Michiel Hermans, Joni Dambre, and Francis wyffels · 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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Modeling parts, structure, and system dynamics via predictive learning
Zhijian Liu, Jiajun Wu, Zhenjia Xu, Chen Sun, Kevin Murphy, William T. Freeman, and Joshua B. Tenenbaum · 2019
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Deep lagrangian networks: Using physics as model prior for deep learning
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