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Experiments show that SyncWorld can accurately simulate action outcomes in previously unseen settings, and that its capability of simulating rollouts enables test-time policy improvement without training.
Hierarchical sub-goal policies enable robots to adapt to novel tasks with just a few human demonstrations, significantly enhancing their manipulation capabilities.
Stabilizing test-time training with an elastic prior lets you reconstruct 4D scenes from long video sequences without catastrophic forgetting, even with smaller memory chunks.
