{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T04:57:06Z","timestamp":1774673826020,"version":"3.50.1"},"reference-count":11,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T00:00:00Z","timestamp":1626825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Industrial Strategy Challenge Fund","award":["EP\/R026084\/1"],"award-info":[{"award-number":["EP\/R026084\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>For robotic systems involved in challenging environments, it is crucial to be able to identify faults as early as possible. In challenging environments, it is not always possible to explore all of the fault space, thus anomalous data can act as a broader surrogate, where an anomaly may represent a fault or a predecessor to a fault. This paper proposes a method for identifying anomalous data from a robot, whilst using minimal nominal data for training. A Monte Carlo ensemble sampled Variational AutoEncoder was utilised to determine nominal and anomalous data through reconstructing live data. This was tested on simulated anomalies of real data, demonstrating that the technique is capable of reliably identifying an anomaly without any previous knowledge of the system. With the proposed system, we obtained an F1-score of 0.85 through testing.<\/jats:p>","DOI":"10.3390\/robotics10030093","type":"journal-article","created":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T22:37:14Z","timestamp":1626993434000},"page":"93","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Variational AutoEncoder to Identify Anomalous Data in Robots"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8196-3842","authenticated-orcid":false,"given":"Luigi","family":"Pangione","sequence":"first","affiliation":[{"name":"Remote Applications in Challenging Environments (RACE), United Kingdom Atomic Energy Authority, Abingdon OX14 3DB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8431-4637","authenticated-orcid":false,"given":"Guy","family":"Burroughes","sequence":"additional","affiliation":[{"name":"Remote Applications in Challenging Environments (RACE), United Kingdom Atomic Energy Authority, Abingdon OX14 3DB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1076-906X","authenticated-orcid":false,"given":"Robert","family":"Skilton","sequence":"additional","affiliation":[{"name":"Remote Applications in Challenging Environments (RACE), United Kingdom Atomic Energy Authority, Abingdon OX14 3DB, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15:1","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv."},{"key":"ref_2","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lin, S., Clark, R., Birke, R., Sch\u00f6nborn, S., Trigoni, N., and Roberts, S. (2020, January 4\u20138). Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053558"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102920","DOI":"10.1016\/j.cviu.2020.102920","article-title":"Video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder","volume":"195","author":"Fan","year":"2020","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_5","first-page":"1","article-title":"Variational Autoencoder based Anomaly Detection using Reconstruction Probability","volume":"2","author":"An","year":"2015","journal-title":"Spec. Lect. IE"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xu, H., Feng, Y., Chen, J., Wang, Z., Qiao, H., Chen, W., Zhao, N., Li, Z., Bu, J., and Li, Z. (2018, January 23\u201325). Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications. Proceedings of the 2018 World Wide Web Conference on World Wide Web\u2014WWW \u201918, Lyon, France.","DOI":"10.1145\/3178876.3185996"},{"key":"ref_7","unstructured":"Pol, A.A., Berger, V., Cerminara, G., Germain, C., and Pierini, M. (2021, July 05). Anomaly Detection with Conditional Variational Autoencoders. CoRR, Available online: http:\/\/xxx.lanl.gov\/abs\/2010.05531."},{"key":"ref_8","unstructured":"(2020, May 21). Kinova Gen 3 Product Description. Available online: https:\/\/www.kinovarobotics.com\/en\/products\/gen3-robot."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tokatli, O., Das, P., Nath, R., Pangione, L., Altobelli, A., Burroughes, G., and Skilton, R. (2021). Robot Assisted Glovebox Teleoperation for Nuclear Industry. Robotics, 10.","DOI":"10.3390\/robotics10030085"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., and Ng, A.Y. (2009, January 12\u201317). ROS: An open-source Robot Operating System. Proceedings of the IEEE ICRA 2009, Kobe, Japan.","DOI":"10.1109\/MRA.2010.936956"},{"key":"ref_11","unstructured":"(2020, May 21). ROS: Robot Operating System. Available online: https:\/\/www.ros.org\/."}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/10\/3\/93\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:33:05Z","timestamp":1760164385000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/10\/3\/93"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,21]]},"references-count":11,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["robotics10030093"],"URL":"https:\/\/doi.org\/10.3390\/robotics10030093","relation":{},"ISSN":["2218-6581"],"issn-type":[{"value":"2218-6581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,21]]}}}