I've been playing around with this for a few weeks. Newton is a pretty thin wrapper around mujoco-warp, which is trying to port mujoco, originally a CPU sim, over to warp on the GPU. There is also mujoco-mjx for this purpose, but using jax instead of warp. I think mjx/jax has the edge on performance because there are mature RL libraries for jax (brax) and big advantages to using Jax for everything, especially with its ability to "vmap" over each layer of abstraction. But I can see why nvidia wants to move away from IsaacLab using physX+pytorch because physX was made for games and interfacing with it through IsaacSim is a bit of a kludge. And apparently mjx isn't so accurate with collisions because of the way it has to be treated in jax. Pytorch RL works decently with newton/warp, at least they can share GPU buffers and you don't have to copy things back and forth to the CPU, however you can't optimize with cuda graphs past the newton/warp boundary because newton/warp have their own cuda graph capture scheme going on at the same time underneath.

They already have a newton branch of IsaacLab on github but its pretty early for it. I just came across a dope project today that is a different wrapper around mujoco-warp that already mimics IsaacLab's api and you can run some robot environments on it. Clean code too, very promising: https://github.com/mujocolab/mjlab.git