ATLAS preserves relational geometry while calibrating the latent distribution in LeWM, improving goal-reaching success on PushT, TwoRoom, and OGBench-Cube, with the largest gain on higher-novelty TwoRoom episodes
The authors introduce ATLAS, a training objective that transfers normalized pairwise structure from an encoder's mean-pooled patch features to the planning latent and uses Wasserstein embedding matching (WEMReg) based on one-dimensional Wasserstein-2 transport to calibrate its marginal; instantiated in LeWM, ATLAS improves mean goal-reaching success across PushT, TwoRoom, and OGBench-Cube on both lower- and higher-novelty evaluation subsets, with the largest gain on higher-novelty TwoRoom episodes, while diagnostics show stronger novelty-related structure in the planning latent, improved marginal calibration, and lower multi-step prediction error.