I think this is why it has a lot of potential. I'm also controls background, so I'm going to just throw stuff at the wall.

I'm thinking it's both M and P. M for the reasons you've mentioned. Then there is P where a forward horizon prediction of both plant and the world.

I think JEPA also touches on the perception tasks: What to do with sensor data? Classical control objectives are quite simple, drive the state to stable, follow a trajectory, stay safe. It's very deterministic: Sensor data is used to determine state. State is used to update dynamics, dynamics is used for the forward horizon.

So. MPC works for things like self-driving because the car model is fairly knowable and world model is predictable (rules of the road, knowable dynamics of other cars).

JEPA seems like a fusion of control, sensing, world model into an embedding space where the learning that occurs is a model free approach to how these are all related to each other.

Yeah not sure where the C comes from; models lack access to knobs and switches of the system they're on and taking over the OS isn't control over the hardware still since it can't upgrade its RAM, or GPU

But the M+P aspect def fits.

Yeah it looks like humans, other animals, and life in general are much more willing to instinctively control things in the transient regime, away from equilibrium

I guess this is the new frontier. Rather than doing safe, predictable, boring control, push the envelope on what trajectories are possible, so that control becomes more lifelike and has a degree of autonomy to recover from error states