{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T09:05:09Z","timestamp":1785143109231,"version":"3.55.0"},"reference-count":29,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chungbuk Regional Innovation System & Education Center"},{"name":"Ministry of Education (MOE) and the Chungcheongbuk-do, Republic of Korea","award":["2026-RISE-013-03"],"award-info":[{"award-number":["2026-RISE-013-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vision\u2013Language\u2013Action (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.<\/jats:p>","DOI":"10.3390\/s26154757","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T08:15:56Z","timestamp":1785140156000},"page":"4757","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ROEP: A Robotics-Oriented Evaluation Protocol for Deployment-Facing Vision\u2013Language\u2013Action Manipulation Policies"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5942-2752","authenticated-orcid":false,"given":"Sangwoo","family":"Han","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence Software, Cheongju University, Cheongju 28503, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5277-6113","authenticated-orcid":false,"given":"Hyunguk","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Software, Cheongju University, Cheongju 28503, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Dabis, J., Finn, C., Gopalakrishnan, K., Hausman, K., Herzog, A., and Hsu, J. (2022). RT-1: Robotics Transformer for Real-World Control at Scale. arXiv.","DOI":"10.15607\/RSS.2023.XIX.025"},{"key":"ref_2","unstructured":"Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., and Finn, C. (2023). RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. arXiv."},{"key":"ref_3","unstructured":"Open X-Embodiment Collaboration, O\u2019Neill, A., Rehman, A., Gupta, A., Gupta, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., and Gupta, A. (2023). Open X-Embodiment: Robotic Learning Datasets and RT-X Models. arXiv."},{"key":"ref_4","unstructured":"Kim, M.J., Pertsch, K., Karamcheti, S., Xiao, T., Balakrishna, A., Nair, S., Rafailov, R., Foster, E., Lam, G., and Sanketi, P. (2024). OpenVLA: An Open-Source Vision-Language-Action Model. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Shao, X., Xu, L., Zhang, S., Sun, G., and Wu, C. (2026). Fractional-Order Dynamics Learning and Control via Data-Driven Approaches: Taking Soft Manipulator as an Example. IEEE Trans. Neural Netw. Learn. Syst., 1\u201316.","DOI":"10.1109\/TNNLS.2026.3665811"},{"key":"ref_6","unstructured":"Octo Model Team, Ghosh, D., Walke, H., Pertsch, K., Black, K., Mees, O., Dasari, S., Hejna, J., Kreiman, T., and Xu, C. (2024). Octo: An Open-Source Generalist Robot Policy. arXiv."},{"key":"ref_7","unstructured":"Zheng, J., Li, J., Wang, Z., Liu, D., Kang, X., Feng, Y., Zheng, Y., Zou, J., Chen, Y., and Zeng, J. (2025). X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, Y., Ding, P., Li, L., Cui, C., Ge, Z., Tong, X., Song, W., Zhao, H., Zhao, W., and Hou, P. (2025). VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model. arXiv.","DOI":"10.1609\/aaai.v40i22.38931"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Pumacay, W., Singh, I., Duan, J., Krishna, R., Thomason, J., and Fox, D. (2024, January 15\u201319). THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation. Proceedings of the Robotics: Science and Systems 2024, Delft, The Netherlands.","DOI":"10.15607\/RSS.2024.XX.133"},{"key":"ref_10","unstructured":"Fei, S., Wang, S., Shi, J., Dai, Z., Cai, J., Qian, P., Li, J., He, X., Zhang, S., and Fei, Z. (2025). LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models. arXiv."},{"key":"ref_11","unstructured":"Wang, G., Zhang, C., Liu, Q., Zhang, J., Cai, J., Liu, J., and Liu, X. (2026). LIBERO-X: Robustness Litmus for Vision-Language-Action Models. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, B., Zhu, Y., Gao, C., Feng, Y., Liu, Q., Zhu, Y., and Stone, P. (2023). LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning. arXiv.","DOI":"10.52202\/075280-1939"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Das, M., Hussain, Z., and Nawaz, M. (2026). Latency-Aware Benchmarking of Large Language Models for Natural-Language Robot Navigation in ROS 2. Sensors, 26.","DOI":"10.3390\/s26020608"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Battiston, G., Regnier, R., and Galibert, O. (2023). Evaluation Protocol for Analogue Intelligent Medical Radars: Towards a Systematic Approach Based on Theory and a State of the Art. Sensors, 23.","DOI":"10.3390\/s23063036"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ramasubramanian, A.K., Kazasidis, M., Fay, B., and Papakostas, N. (2024). On the Evaluation of Diverse Vision Systems towards Detecting Human Pose in Collaborative Robot Applications. Sensors, 24.","DOI":"10.3390\/s24020578"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, Y., Cui, X., Fan, S., Wang, Q., Liu, Y., Sun, Y., and Wang, G. (2024). Dynamic Validation of Calibration Accuracy and Structural Robustness of a Multi-Sensor Mobile Robot. Sensors, 24.","DOI":"10.3390\/s24123896"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Martinez-Martin, E., and del Pobil, A.P. (2019). Vision for Robust Robot Manipulation. Sensors, 19.","DOI":"10.3390\/s19071648"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7327","DOI":"10.1109\/LRA.2022.3180108","article-title":"CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Tasks","volume":"7","author":"Mees","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3019","DOI":"10.1109\/LRA.2020.2974707","article-title":"RLBench: The Robot Learning Benchmark and Learning Environment","volume":"5","author":"James","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Luo, J., Xu, C., Liu, F., Tan, L., Lin, Z., Wu, J., Abbeel, P., and Levine, S. (2025). FMB: A Functional Manipulation Benchmark for Generalizable Robotic Learning. Int. J. Robot. Res., 44.","DOI":"10.1177\/02783649241276017"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Guruprasad, P., Sikka, H., Song, J., Wang, Y., and Liang, P.P. (2024). Benchmarking Vision, Language, & Action Models on Robotic Learning Tasks. arXiv.","DOI":"10.20944\/preprints202411.0494.v1"},{"key":"ref_22","unstructured":"Zhou, X., Xu, Y., Tie, G., Chen, Y., Zhang, G., Chu, D., Zhou, P., and Sun, L. (2025). LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization. arXiv."},{"key":"ref_23","unstructured":"Gu, Q., Ju, Y., Sun, S., Gilitschenski, I., Nishimura, H., Itkina, M., and Shkurti, F. (2025). SAFE: Multitask Failure Detection for Vision-Language-Action Models. arXiv."},{"key":"ref_24","unstructured":"Lin, Z., Duan, J., Fang, H., Fox, D., Krishna, R., Tan, C., and Wen, B. (2025). FailSafe: Reasoning and Recovery from Failures in Vision-Language-Action Models. arXiv."},{"key":"ref_25","unstructured":"Norton, A., and Flynn, B. (2024). Towards Using Multiple Iterated, Reproduced, and Replicated Experiments with Robots (MIRRER) for Evaluation and Benchmarking. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1109\/LRA.2020.2964164","article-title":"Benchmarking Protocols for Evaluating Grasp Strength, Grasp Cycle Time, Finger Strength, and Finger Repeatability of Robot End-Effectors","volume":"5","author":"Falco","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_27","unstructured":"Ross, S., Gordon, G.J., and Bagnell, J.A. (2011, January 11\u201313). A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Belkhale, S., Cui, Y., and Sadigh, D. (2023). Data Quality in Imitation Learning. arXiv.","DOI":"10.52202\/075280-3524"},{"key":"ref_29","unstructured":"De Ruiter, A. (2025). Advanced Control Strategies for Space Systems: Integration of Model Predictive Control and Neural Networks. Advanced Control Strategies for Multi-Robot Space Systems, IntechOpen."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/15\/4757\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T08:19:36Z","timestamp":1785140376000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/15\/4757"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,27]]},"references-count":29,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["s26154757"],"URL":"https:\/\/doi.org\/10.3390\/s26154757","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,27]]}}}