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Hierarchical Planning with Latent World Models
Hierarchical PlanningLatent World ModelsModel Predictive ControlZero Shot Robot ControlLong Horizon Planning
Summary
This paper introduces HWM, a hierarchical planning method for learned latent world models that improves long-horizon zero-shot control. It plans at two time scales in a shared latent space, which raises success rates on real robot manipulation and simulation tasks while cutting planning compute.
Problem
- Learned world models can support zero-shot control, but flat model predictive control gets weak on long-horizon tasks because rollout errors grow over time.
- Planning over long sequences of primitive actions is expensive because the search space grows quickly with horizon.
- This matters for embodied control because many robot and navigation tasks need multi-stage, non-greedy behavior from high-dimensional observations, often with only a goal image at test time.
Approach
- HWM trains two latent world models in the same latent space: a low-level model for short-horizon primitive actions and a high-level model for longer-horizon transitions.
- The high-level model predicts waypoint latents using learned macro-actions. These macro-actions come from an action encoder that compresses chunks of low-level actions between waypoints.
- At inference, the high-level planner searches over latent macro-actions to move the current latent state toward the goal latent. Its first predicted latent waypoint becomes a subgoal.
- The low-level planner then searches over primitive actions to reach that latent subgoal, and the system replans in MPC style during execution.
- Because both levels share the same latent space, HWM does not need task-specific rewards, skill learning, inverse models, or hierarchical policies.
Results
- On a real Franka pick-and-place task with VJEPA2-AC and only a final goal image, flat planning gets 0% success and HWM gets 70% success, a +70 point gain.
- On Franka drawer open/close, VJEPA2-AC improves from 30% to 70%, a +40 point gain.
- On Push-T with DINO-WM, success rises from 17% to 61%, a +44 point gain.
- On Diverse Maze with PLDM, success rises from 44% to 83%, a +39 point gain.
- On pick-and-place with oracle subgoals, the flat planner and HWM both get 80%, which shows the main gain comes from automatic subgoal generation rather than easier low-level control.
- The paper claims similar or better success with about 3× less planning time in the figure caption and up to 4× lower inference-time planning cost in the contribution summary.