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ATLAS: Aligned Transport of Latent Structure for Reliable World Model Planning

The paper introduces ATLAS, a training objective that preserves relational geometry in latent representations for reliable world model planning. It transfers normalized pairwise structure from an encoder to the planning latent and uses Wasserstein embedding matching to calibrate the marginal distribution, improving goal-reaching success in various environments.

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PublishedSeptember 30, 2026Ke Fang, Yupu Yao, Lu Cheng
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ATLAS helps improve the reliability of world model planning by preserving the relational geometry of latent representations, which is crucial for accurate action selection and planning in novel environments.

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