[Animats]

In the 1990s, I was thinking of this for legged locomotion over rough terrain, fast turns, and such. The idea was to use a mediocre but fast physical simulation to answer "what-if" questions, allowing planning of moves about two or three steps ahead in the real world, or at least a realistic simulator. Then use a learning model to learn corrections for differences between the mediocre simulator and the "real world", or good simulator. The system would start out somewhat klutzy and get better. Eventually, perhaps to the parkour level.

Once you have a model, you can invert it to make a controller, as the post above points out. For classical linear models, this can be done analytically. For non-linear models, you can use the model to train a controller, running the model with random inputs to generate a training set.

(I spent several years working on the simulator problem, shipped a simulation product ("Falling Bodies", the first ragdoll simulator that didn't suck)[1] and eventually sold the technology to a physics engine startup and went on to other things. Even today, as Sony and Boston Dynamics have demonstrated at great time and expense, there's no market for legged robots yet.)

[1] https://www.youtube.com/watch?v=5lHqEwk7YHs