Model-Based Planning

Model-based planning uses a model of how actions change the world to look ahead before acting, supporting decisions in robotics, navigation, games, tool use, and scheduling. Common approaches include model predictive control, Monte Carlo tree search and dynamic programming, symbolic action models, and learned latent or generative world models; language-model agents increasingly combine these with search, constraints, and execution feedback. Benchmarks range from simulated continuous control and manipulation to partially observable tasks, dynamic environments, and real-robot trials. A central challenge is that good predictions do not necessarily yield good plans: models must rank actions reliably over useful horizons while controlling computation, adapting to uncertainty and distribution shifts, and avoiding plans that exploit model errors.