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Humanoid robots can now learn complex loco-manipulation tasks from sparse demonstrations by iteratively evolving their own training data and policies.
GRAIL achieves an impressive 84% success rate in real-world object pick-up tasks using only synthetic data, revolutionizing humanoid robot training.
