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Surprisingly, the "think before answer" paradigm fails to enhance generative recommendation models, prompting a novel approach that redefines how reasoning is integrated into these systems.
GRAIL achieves an impressive 84% success rate in real-world object pick-up tasks using only synthetic data, revolutionizing humanoid robot training.
By jointly modeling hand-object motion, WHOLE overcomes occlusion and out-of-sight challenges in egocentric videos, achieving state-of-the-art results in hand and object pose estimation.
