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R2S-Eval: Robot Evaluation with Real-to-Sim Calibration via Vision-Language Models

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

Evaluating robot manipulation policies is becoming increasingly important as generalist models, particularly vision-language-action (VLA) models, are deployed on physical robots. However, conventional real-world evaluation remains labor-intensive, unstable, and insufficiently informative. It requires repeated hardware trials, manual scene resets, and continuous operator monitoring, may produce different policy rankings across repeated evaluations, and primarily relies on success-rate metrics that provide limited information about execution quality. In contrast, humans assess robot performance

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