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KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images. (arXiv:2206.11030v1 [cs.LG])
June 23, 2022, 1:10 a.m. | Rembert Daems, Jeroen Taets, Francis wyffels, Guillaume Crevecoeur
cs.LG updates on arXiv.org arxiv.org
We present KeyCLD, a framework to learn Lagrangian dynamics from images.
Learned keypoints represent semantic landmarks in images and can directly
represent state dynamics. Interpreting this state as Cartesian coordinates
coupled with explicit holonomic constraints, allows expressing the dynamics
with a constrained Lagrangian. Our method explicitly models kinetic and
potential energy, thus allowing energy based control. We are the first to
demonstrate learning of Lagrangian dynamics from images on the dm_control
pendulum, cartpole and acrobot environments. This is a step …
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