Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control. (arXiv:2007.01926v3 [cs.LG] UPDATED)

Recent approaches for modelling dynamics of physical systems with neural
networks enforce Lagrangian or Hamiltonian structure to improve prediction and
generalization. However, when coordinates are embedded in high-dimensional data
such as images, these approaches either lose interpretability or can only be
applied to one particular example. We introduce a new unsupervised neural
network model that learns Lagrangian dynamics from images, with
interpretability that benefits prediction and control. The model infers
Lagrangian dynamics on generalized coordinates that are simultaneously learned
with …

arxiv dynamics images learning prediction unsupervised unsupervised learning