Learning Goal-Conditioned Subgoals through Frozen Hierarchical World Models
Hierarchical world models plan over long horizons by passing predicted states from a coarse model to a lower-level controller. Choosing these states can be expensive: the upper-level planner repeatedly differentiates through candidate macro-action trajectories. We replace this search with a lightweight goal-conditioned network trained through the frozen high-level predictor. The network supplies a conditioning vector, while the predictor generates the next latent waypoint. Training uses hindsight goals and state-prediction losses on offline trajectories, without search-solution targets or macro-action labels. Inference retains an autoregressive high-level rollout, but requires no high-lev...
| Authors | Xiang Kaibo, Liu Qingfeng |
|---|---|
| Primary category | cs.CV |
| Published | 19 October 2026 |
| Last updated at source | 20 October 2026 |
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