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Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions.
Hierarchical geometric models for visible surface algorithms
James H. Clark · 1976
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The mechanics of fine manipulation by pushing
Kevin M Lynch · 1992
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An implicit time-stepping scheme for rigid body dynamics with Coulomb friction
D Stewart and JC J.C. Trinkle · 1996
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Formulating dynamic multi-rigid-body contact problems with friction as solvable linear complementarity problems
M Anitescu and F A Potra · 1997
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Signed distance computation using the angle weighted pseudonormal
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Meshless deformations based on shape matching
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Erwin Coumans · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Kuan-Ting Yu, Maria Bauza, Nima Fazeli, and Alberto Rodriguez · 2016
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A probabilistic data-driven model for planar pushing
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Empirical evaluation of common contact models for planar impact
Nima Fazeli, Elliott Donlon, Evan Drumwright, and Alberto Rodriguez · 2017
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Google vizier: A service for black-box optimization
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Augmenting physical simulators with stochastic neural networks: Case study of planar pushing and bouncing
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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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Eric Heiden, David Millard, Erwin Coumans, Yizhou Sheng, and Gaurav S Sukhatme · 2021
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Fundamental challenges in deep learning for stiff contact dynamics
Mihir Parmar, Mathew Halm, and Michael Posa · 2021
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Learning agile and dynamic motor skills for legged robots
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Propagation networks for model-based control under partial observation
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Learning to control self-assembling morphologies: A study of generalization via modularity
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Drake: Model-based design and verification for robotics, 2019
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Validating robotics simulators on real-world impacts
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Graph network simulators can learn discontinuous, rigid contact dynamics
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