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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.
GaP outperforms traditional methods in variational automation tasks by leveraging directed computation graphs for real-time adaptability and improved success rates.
