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Unlabeled robot deployment failures don't need expensive corrective demonstrations to be useful: steering linearly separable latent states away from failure modes fixes policy execution with just 0.5% added parameters.
Real-world robot failures can be autonomously converted into interactive digital twins for targeted RL, boosting out-of-distribution manipulation by 18.75% while completely bypassing manual teleoperated recovery.
DMRL transforms the way advertising recommendation systems optimize skill documents, achieving superior performance through structured editing and advanced reward estimation techniques.