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Kuafu's ability to synthesize robust policies from language and task semantics achieves over 82% success in complex manipulation tasks, revolutionizing how we approach control-to-learning paradigms.
RL-based sample selection beats traditional active learning for transfer learning when data is scarce and imbalanced, especially in clinical settings.
