Decoupling state reachability from action execution in hierarchical world models dramatically reduces planning errors for long-horizon robotic tasks—achieving 135% improvement over single-level approaches by prioritizing local feasibility over global behavioral patterns.
This paper presents ID-WM, a hierarchical world model for robotic planning that separates high-level state transitions from low-level action execution. A high-level model identifies reachable subgoals across the state space, while a low-level inverse dynamics model converts these subgoals into precise actions.