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WRAP achieves a 27.4% reduction in localization error by decoupling mean adaptation from covariance robustification, revolutionizing how we approach robot localization under variable conditions.
Safety in imitation learning can be achieved even in the face of significant distribution shifts, as shown by a UAV navigating uncertain environments while avoiding danger zones.
