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Robot skills can be dramatically improved post-training by autonomously generating practice tasks, diagnosing failures with privileged simulator states, and iteratively refining symbolic skills and system prompts, achieving near-perfect success on novel manipulation tasks.
Robots can now peel cucumbers, apples, and potatoes with 90% success by learning from a small number of human preferences, even generalizing to new produce types.
