JEPA is very similar to Model Predictive Control. Here, you swap the model of the system with black-box simulator (a neural net), apply some version of dynamic programming / Bellman equation to find the optimal action over planning horizon.

In the planning stage, by factoring out the optimization step from the rollout step, you can free the model to focus on learning the dynamics in latent space. (The other case, where the rollout+optimization happen in the learning loop, just becomes model-free RL.)

My background straddles classical & data-driven controls. This was the first similarity that popped out to me when I first started learning about world models.

Is JEPA just MPC but where the "M" is a latent-space model?