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JEPA-Bisim: Learning Robust Visual Representations for Planning with Joint-Embedding Predictive World Models
World models learned from high-dimensional visual observations allow agents to make decisions and plan directly in latent space, avoiding pixel-level reconstruction. However, recent latent predictive architectures (JEPAs), including the DINO world model (DINO-WM), display a degradation in test time
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