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diffmimic
[ICLR 2023] DiffMimic: Efficient Motion Mimicking with Differentiable Physics https://arxiv.org/abs/2304.03274
I remember several years ago when differentiable programming was an object of interest to the programming community and Lattner was trying to make Swift for Tensorflow happen[1].
I'm of the opinion that it was ahead of its time: Swift hadn't (and still hasn't) made enough progress on Linux support for it to be taken seriously as a language for writing anything that isn't associated with Apple. However, as a result, Swift now has language-level differentiability in its compiler. I'd love to see Swift get used for projects like this, but I suppose the reality of the matter is that there are so many performant runtimes for 2D/3D physics that there just isn't much of a need for automatic differentiation (and its overhead) to solve these problems. The tooling nerd in me thinks this stuff is fascinating.
https://github.com/tensorflow/swift
Mostly I use pytorch for statistical modeling https://pyro.ai . Under the hood that package uses a lot of Monte Carlo integration and variational methods (i.e. integration by optimization). It does support neural nets, but probably >80% of pyro users stick to simpler hierarchical Bayesian models.
Ehh, there’s a lot that goes into it. Just because a physics engine is differentiable doesn’t mean that its gradients going to be useful. For example, if you look at Brax/Mujoco in JAX, the gradients generated by Mujoco are absolute garbage if you’re trying to train a robotics controller, but the more video-game like engines give pretty good results (see https://github.com/jiawei-ren/diffmimic).