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We present a differentiable simulation architecture for articulated rigid-body dynamics that enables the augmentation of analytical models with neural networks at any point of the computation.
Interactive differentiable simulation
Eric Heiden, David Millard, Hejia Zhang, and Gaurav S. Sukhatme · 1905
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Coefficient of restitution interpreted as damping in vibroimpact
Kenneth H Hunt and Frank R Erskine Crossley · 1975
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Formulating dynamic multi-rigid-body contact problems with friction as solvable linear complementarity problems
Mihai Anitescu and Florian A Potra · 1997
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Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter W Battaglia · 2002
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Bullet physics library
Erwin Coumans et al · 2013
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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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Automatic differentiation of rigid body dynamics for optimal control and estimation
Markus Giftthaler, Michael Neunert, Markus Stäuble, Marco Frigerio, Claudio Semini, and Jonas Buchli · 2017
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Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing
Anurag Ajay, Jiajun Wu, Nima Fazeli, Maria Bauza, Leslie P Kaelbling, Joshua B Tenenbaum, and Alberto Rodriguez · 2018
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Learning beyond simulated physics
Alexis Asseman, Tomasz Kornuta, and Ahmet Ozcan · 2018
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Analytical derivatives of rigid body dynamics algorithms
Justin Carpentier and Nicolas Mansard · 2018
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Neural ordinary differential equations
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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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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Sim-to-real transfer with neural-augmented robot simulation
Florian Golemo, Adrien Ali Taiga, Aaron Courville, and Pierre-Yves Oudeyer · 2018
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Data-augmented contact model for rigid body simulation
Yifeng Jiang and C. Karen Liu · 2018
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Dart: Dynamic animation and robotics toolkit
Jeongseok Lee, Michael X. Grey, Sehoon Ha, Tobias Kunz, Sumit Jain, Yuting Ye, Siddhartha S. Srinivasa, Mike Stilman, and C. Karen Liu · 2018
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Parallel monotonic basin hopping for low thrust trajectory optimization
Julia for robotics: simulation and real-time control in a high-level programming language
Twan Koolen and Robin Deits · 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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Differentiable cloth simulation for inverse problems
Junbang Liang, Ming Lin, and Vladlen Koltun · 2019
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Deep lagrangian networks: Using physics as model prior for deep learning
Michael Lutter, Christian Ritter, and Jan Peters · 2019
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Mitsuba 2: A retargetable forward and inverse renderer
Merlin Nimier-David, Delio Vicini, Tizian Zeltner, and Wenzel Jakob · 2019
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Steven L McCarty, Laura M Burke, and Melissa McGuire · 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 L K Yamins · 2018
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Learning agile and dynamic motor skills for legged robots
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Ceres solver
Sameer Agarwal, Keir Mierle, and Others
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DensePhysNet: Learning dense physical object representations via multi-step dynamic interactions
Zhenjia Xu, Jiajun Wu, Andy Zeng, Joshua B Tenenbaum, and Shuran Song · 2019
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Tossingbot: Learning to throw arbitrary objects with residual physics
Andy Zeng, Shuran Song, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 2019
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Lagrangian neural networks, 2020
Miles Cranmer, Sam Greydanus, Stephan Hoyer, Peter Battaglia, David Spergel, and Shirley Ho · 2020
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Physics-based simulation of continuous-wave LIDAR for localization, calibration and tracking
Eric Heiden, Ziang Liu, Ragesh K. Ramachandran, and Gaurav S. Sukhatme · 2020
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DiffTaichi: Differentiable programming for physical simulation
Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, and Frédo Durand · 2020
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