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GaP outperforms traditional methods in variational automation tasks by leveraging directed computation graphs for real-time adaptability and improved success rates.
Robots can now autonomously learn to avoid failures by transforming recovery experiences into abstract planning skills, achieving over 50% better performance on unseen tasks.
