Review for NeurIPS paper: Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control

This paper makes it possible to learn Lagrangian dynamics from images and use them for energy-based control. This represents an important and significant advance for this fledgling new research subfield of physics-aware prediction, which might very well go on to prove important and significant in the coming years. I believe the reviewers are all in agreement on this point. However, by entering this new territory for physics-aware prediction, this paper has also exposed itself to interest from a broader community of readers and NeurIPS attendees who are familiar with the progress in image-based *intuitive physics* modeling and control methods over the last 5 years or so (R2 and R4 point to some such approaches). A lot of the difficulty in arriving at a reviewer consensus for this paper can be put down to the fact that its positioning is somewhat myopic and ignores this broader context, perhaps because the authors themselves might not be familiar with these approaches.