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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.
Achieving a 35% success rate in real-world robotic tasks using only synthetic data marks a groundbreaking advance in sim-to-real transfer for world-action models.
