Reviews: End-to-End Differentiable Physics for Learning and Control

The paper proposes to include an intermediate differentiable physics "engine" i.e. a novel parameterization of an intermediate layer in a neural network that respects the forward dynamics as governed by physics. The proposed method is closer to the popular "system identification" paradigm and involves learning the parameters of the engine via gradient-decent, optimizing the squared loss between observed and predicted frames. The paper shows results on some simple, interesting simulation domains and demonstrates the value of including such a module. Depending on the rebuttal for some of the questions below, I am happy to revise my ratings for this paper. Positives: - In general, the paper addresses an interesting and challenging question of incorporating more domain / structured knowledge into differentiable and learnable networks.