Latent Geometry Beyond Search: Amortizing Planning in World Models
Hoang Nguyen, Xiaohao Xu, Xiaonan Huang

TL;DR
This paper introduces a method to amortize planning in world models by learning a latent inverse-dynamics model, significantly reducing computation while maintaining or improving performance across various environments.
Contribution
It demonstrates that structured latent spaces enable replacing iterative planning with a learned inverse-dynamics model, simplifying control in vision-based world models.
Findings
The proposed GC-IDM matches or exceeds CEM performance in most benchmarks.
Per-decision cost is reduced by 100-130x with the new method.
The approach is robust across different test-time planners.
Abstract
Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these spaces remains challenging. This raises a central question: when does a learned representation simplify control, rather than merely enabling prediction? We study this question in a pretrained LeWorldModel, whose latent geometry is regularized for smoothness and uniformity. Our key insight is that, under such geometry, planning can be amortized into a latent inverse-dynamics mapping instead of requiring online search. We therefore replace iterative planning with a lightweight Goal-Conditioned Inverse Dynamics Model (GC-IDM) that maps the current latent state, goal latent state, and remaining horizon directly to the next action. Empirically, across four benchmark environments spanning navigation, contact-rich manipulation, and continuous control,…
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