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Learning physically structured representations of dynamical systems that include contact between different objects is an important problem for learning-based approaches in robotics.
“Unilateral contact and dry friction in finite freedom dynamics”
Jean Moreau · 1988
Earlier work this paper cites.
“Symplectic integrators for Hamiltonian problems: an overview”
Jesus Sanz-Serna · 1992
Earlier work this paper cites.
“The non-smooth contact dynamics method”
Michel Jean · 1999
Earlier work this paper cites.
“New developments in contact problems”
Peter Wriggers and Panagiotis Panagiotopoulos · 1999
Earlier work this paper cites.
“Discrete mechanics and variational integrators”
Jerrold Marsden and Matthew West · 2001
Earlier work this paper cites.
“Nonsmooth Lagrangian mechanics and variational collision integrators”
Razvan Fetecau, Jerrold Marsden, Michael Ortiz and Matthew West · 2003
Earlier work this paper cites.
“Decomposition contact response (DCR) for explicit finite element dynamics”
Fehmi Cirak and Matthew West · 2005
Earlier work this paper cites.
“Variational integrators for constrained dynamical systems”
Sigrid Leyendecker, Jerrold Marsden and Michael Ortiz · 2008
Earlier work this paper cites.
“Fundamentals of Physics”
David Halliday, Robert Resnick and Jearl Walker · 2013
Earlier work this paper cites.
“Gaussian processes for data-efficient learning in robotics and control”
Marc Deisenroth, Dieter Fox and Carl Rasmussen · 2015
Cited alongside, same era.
“Kinematics-based estimation of contact constraints using only proprioception”
Valerio Ortenzi et al · 2016
Cited alongside, same era.
“A proposal on machine learning via dynamical systems”
Weinan E · 2017
Cited alongside, same era.
“A new heterogeneous asynchronous explicit–implicit time integrator for nonsmooth dynamics”
Fatima-Ezzahra Fekak, Michael Brun, Anthony Gravouil and Bruno Depale · 2017
Cited alongside, same era.
“Stable architectures for deep neural networks”
Eldad Haber and Lars Ruthotto · 2017
Cited alongside, same era.
“Deep learning in fluid dynamics”
J Kutz · 2017
Cited alongside, same era.
“Unsupervised contact learning for humanoid estimation and control”
Nicholas Rotella, Stefan Schaal and Ludovic Righetti · 2018
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“Deep neural networks motivated by partial differential equations”
Lars Ruthotto and Eldad Haber · 2018
Later among the works it cites.
“Learning a structured neural network policy for a hopping task”
Julian Viereck, Jules Kozolinsky, Alexander Herzog and Ludovic Righetti · 2018
Later among the works it cites.
“Benchmark cases for robust explicit time integrators in non-smooth transient dynamics”
Jean Di et al · 2019
Later among the works it cites.
“Hamiltonian Neural Networks”
Sam Greydanus, Misko Dzamba and Jason Yosinski · 2019
Later among the works it cites.
“DyNODE: Neural ordinary differential equations for dynamics modeling in continuous control”
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“Neural ordinary differential equations”
Ricky Chen, Yulia Rubanova, Jesse Bettencourt and David Duvenaud · 2018
Cited alongside, same era.
“End-to-end differentiable physics for learning and control”
Filipe de Avila-Peres et al · 2018
Cited alongside, same era.
“Deep hidden physics models: deep learning of nonlinear partial differential equations”
Maziar Raissi · 2018
Cited alongside, same era.
Victor Alvarez, Rareş Roşca and Cristian Fălcuţescu · 2020
Later among the works it cites.
“Lagrangian neural networks”
Miles Cranmer et al · 2020
Later among the works it cites.
“Deep Lagrangian networks: using physics as model prior for deep learning”
Michael Lutter, Christian Ritter and Jan Peters · 2020
Later among the works it cites.
“Variational integrator networks for physically structured embeddings”
S Sæmundsson, A Terenin, K Hofmann and M.. Deisenroth · 2020
Later among the works it cites.