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Current action-conditioned world models are limited by their reliance on visual patterns, failing to generalize physical dynamics across different robot embodiments.
Memory-augmented manipulation models can now achieve state-of-the-art performance while remaining data-efficient and generalizable across diverse tasks and environments.
