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VLMs struggle with spatial reasoning, but a clever decomposition into sub-problems and probabilistic recombination unlocks significantly better metric-semantic grounding.
Forget retraining: a single, carefully chosen noise vector can boost your robot's pre-trained policy performance by up to 60% in the real world.
