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Robots no longer need human intervention to learn from their mistakes: foundation-model-guided exploration over modular skills can autonomously synthesize corrective rollouts that distill directly into low-level policy weights.
CI-MSE dramatically improves the correlation between offline validation and real-world performance, making it a game-changer for robot policy evaluation.
