arXiv (OAI Expanded)
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Mem-World: Memory-Augmented Action-Conditioned World Models for Persistent Robot Manipulation
Action-conditioned world models have emerged as a promising paradigm for robot learning, offering a scalable alternative to costly real-world experimentation by generating action-consistent video rollouts. However, persistent world modeling remains challenging in manipulation: frequent end-effector
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