Latent Energy Action Planning with World Models
cs.LG
Submitted: 2026-09-03
Updated: 2026-09-03
Comments: 9 pages, 5 figures, 8 tables
License: http://creativecommons.org/licenses/by/4.0/
The gist: Latent world models support efficient model predictive control from high-dimensional observations, yet optimizing a single learned latent objective can favor action sequences whose decoder-predicted
Terminology
Abstract
Latent world models support efficient model predictive control from high-dimensional observations, yet optimizing a single learned latent objective can favor action sequences whose decoder-predicted terminal descriptor does not match the goal descriptor. We introduce Latent Energy Action Planning (LEAP), which treats the complete action horizon as a differentiable variable and optimizes it through a frozen LeWorldModel (LeWM). LEAP couples terminal latent goal matching with a terminal-window state energy. Low energy requires the predicted terminal latent to agree with the goal latent and the decoder-predicted terminal descriptor to agree with the goal descriptor. A frozen goal-conditioned proposal initializes the search, a quasi-Newton solver refines actions through the autoregressive rollout, and post-optimization projection enforces the admissible action range. Across four control domains using the officially released LeWM checkpoints, the complete LEAP planning system raises mean success from 77.5% for LeWM planned with the cross-entropy method (LeWM+CEM) to 94.8% under a matched protocol, a 17.3-percentage-point improvement, while retaining the frozen LeWM representation and predictor.
Sources
- LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels
- Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
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