Seeing Tomorrow Is Not Controlling It: World Models as an Architecture Decision
TL;DR for operators A robot can predict a plausible future and still take the wrong action. The operational question is therefore not simply how accurately a system models what happens next, but how that prediction is represented and connected to control. The tutorial distinguishes world models, which predict future task-relevant observations or states under actions, from world action models, which couple future prediction with action generation. It then turns this distinction into an architecture map: predict raw observations or compact states; expose the future explicitly or keep it latent; connect prediction to action through a separate controller, predictive features, joint generation, or auxiliary training. ...
