Brief

Researchers have developed Hierarchical Planning with Latent World Models (HWM), a new architecture for improving long-horizon planning in embodied AI. HWM utilizes world models trained at multiple temporal scales within a shared latent space, allowing predictions from longer-horizon models to guide shorter-horizon planning without explicit task-specific rewards or policies. The system also incorporates an action encoder to compress primitive actions into latent macro-actions, making long-horizon search more tractable. In real-world manipulation tasks, HWM achieved a 70% success rate, a significant improvement over single-level planning which yielded 0% success, and demonstrated up to a 3x reduction in planning compute on simulated tasks. AI