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Differentiable physics enables efficient gradient-based optimizations of neural network (NN) controllers.
Large steps in cloth simulation
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John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Libin Liu and Jessica Hodgins · 2017
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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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A moving least squares material point method with displacement discontinuity and two-way rigid body coupling
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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
Trajectory optimization for cable-driven soft robot locomotion
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Differentiable cloth simulation for inverse problems
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Learning predict-and-simulate policies from unorganized human motion data
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Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein · 2020
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Add: Analytically differentiable dynamics for multi-body systems with frictional contact
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Scalable differentiable physics for learning and control
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A scalable approach to control diverse behaviors for physically simulated characters
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Plasticinelab: A soft-body manipulation benchmark with differentiable physics
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Learning and exploring motor skills with spacetime bounds
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Efficient hyperparameter optimization for physics-based character animation
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