Evidence map›Paper›PMID 42590534›Full record

ArticleSensors (Basel, Switzerland)2026

ROEP: A Robotics-Oriented Evaluation Protocol for Deployment-Facing Vision-Language-Action Manipulation Policies.

Sangwoo Han, Hyunguk Choi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Sangwoo HanDepartment of Artificial Intelligence Software, Cheongju University, Cheongju 28503, Republic of Korea.ORCID 0009-0006-5942-2752
Hyunguk ChoiDepartment of Artificial Intelligence Software, Cheongju University, Cheongju 28503, Republic of Korea.ORCID 0000-0002-5277-6113

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vision-Language-Action (VLA) policies are increasingly evaluated on language-conditioned robotic manipulation benchmarks, but success rate alone often obscures runtime-interface alignment, the repeatability of observation-degradation effects, and the recovery-relevant semantics of failures. This study proposes ROEP (Robotics-Oriented Evaluation Protocol), a VLA-targeted deployment-oriented evaluation protocol that converts rollout outcomes into claim-level evidence for closed-loop robotic manipulation. ROEP first verifies clean-condition evaluability and runtime fidelity of the sensor-to-action execution interface, then evaluates controlled visual perturbations, repeated-run reference variability, timeout-dominant failures, and recovery/shield support. We apply ROEP to OpenVLA, X-VLA, and VLA-Adapter on eight LIBERO Goal and Object tasks, producing a 24-row evaluation matrix with complete clean and medium-perturbation evidence. Under the evaluated LIBERO simulation setting, the results show that runtime-interface fidelity and checkpoint provenance are important for interpreting X-VLA and OpenVLA Object results, while VLA-Adapter maintains strong Goal-suite performance but exhibits a substantial Object-suite clean-to-perturbation drop dominated by timeout termination. ROEP therefore clarifies which deployment-relevant claims are supported, withheld, or insufficiently evidenced by the available rollouts without certifying open-world deployment safety or recovery success. The protocol complements existing VLA benchmarks by reporting success rate together with runtime fidelity, repeatability, failure semantics, and recovery/shield support evidence.

Indexed as

deployment-oriented evaluationmultimodal sensingrobotic manipulationrobustness evaluationvision–language–action

Identifiers

PMID42590534
PMCPMC13469403

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.