[Submitted on 8 Sep 2026]
Title:No Free Checker: A Survey of Verifiers for Robot Policies
View a PDF of the paper titled No Free Checker: A Survey of Verifiers for Robot Policies, by Yang Wan and 9 other authors
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Abstract:A verifier for robot policies reads a candidate behavior and returns a score for how well it did, used both to evaluate vision-language-action policies and to train them. Verifiers range from success detectors and reward models to runtime monitors, safety filters, and temporal-logic specifications. We survey roughly 150 verifiers and compare them along two properties. Availability is how much a verdict costs, how early in a rollout the verdict arrives, and how often a verdict can be asked for. Availability rises as verdicts get cheaper, earlier, and denser. Credibility is how much a high score tells us about the task. Credibility falls as the judgment becomes gameable and self-serving. We group the verifiers by who supplies the judgment: human verifiers, rule-based and formal verifiers, learned and pretrained verifiers, and model-intrinsic verifiers. Across the four families, we find that credibility falls as availability rises. Regardless of who supplies the judgment, there is no free checker. We then examine what validates a verifier itself, and how much a high score tells us. Three measures appear in the literature: agreement with human labels, the performance of the policy it trains, and behavior under reward hacking. We close with nine metrics that make a verifier claim checkable, and coordinates for the verifiers still to be built.
Comments: Survey. 31 pages, 5 figures, 7 tables, 187 references. Covers reward models, success and failure detection, temporal-logic and formal verification, world-model evaluation, and reward hacking. Project page: this https URL
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Systems and Control (eess.SY)
Cite as: arXiv:2609.09250 [cs.RO]
(or arXiv:2609.09250v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.09250
arXiv-issued DOI via DataCite
Submission history
From: Yang Wan [view email] [v1] Tue, 8 Sep 2026 13:52:59 UTC (2,747 KB)
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