LeWAM: End-to-End World and Action Modeling with JEPAs
Joint-embedding predictive architectures (JEPAs) learn compact latent world models without pixel reconstruction. Existing JEPA pipelines typically train visual representations and action-conditioned dynamics before adding a control policy or solver. We introduce LeWAM, an end-to-end JEPA for control with: (1) a joint t...
Ming-Hao Fu, Tavis Siebert, Eryk Halicki et al.
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