World Model-Based Planning

World model-based planning studies how an agent can predict the consequences of actions and use those predictions to reach goals, avoid risks, or coordinate with others. Models may predict images, compact latent states, object relations, or physical dynamics, and planners use methods such as model-predictive control, CEM, tree search, and learned action policies. Work spans robot manipulation and navigation, autonomous driving, games, and language-guided agents, with approaches including hierarchical subgoals, uncertainty estimates, and symbolic or physics-based structure. A central challenge is that accurate-looking or low-error predictions may still rank actions poorly, especially over long horizons or under distribution shift, so researchers are developing planning-focused representations, diagnostics, and more efficient closed-loop adaptation.