{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T06:17:07Z","timestamp":1790144227369,"version":"4.0.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,15]],"date-time":"2024-11-15T00:00:00Z","timestamp":1731628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100005046","name":"the National Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"crossref","award":["No.YQ2020E028"],"award-info":[{"award-number":["No.YQ2020E028"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-79292-4","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T20:39:25Z","timestamp":1731616765000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Learning agility and adaptive legged locomotion via curricular hindsight reinforcement learning"],"prefix":"10.1038","volume":"14","author":[{"given":"Sicen","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiming","family":"Pang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panju","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shihao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaojin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liquan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,15]]},"reference":[{"key":"79292_CR1","doi-asserted-by":"crossref","unstructured":"Gangapurwala, S., Campanaro, L. & Havoutis, I. Learning low-frequency motion control for robust and dynamic robot locomotion. In 2023 IEEE International Conference on Robotics and Automation (ICRA) 5085\u20135091 (IEEE, 2023).","DOI":"10.1109\/ICRA48891.2023.10160357"},{"key":"79292_CR2","doi-asserted-by":"crossref","unstructured":"Mitchell, A.\u00a0L. et al. Next steps: Learning a disentangled gait representation for versatile quadruped locomotion. In 2022 International Conference on Robotics and Automation (ICRA) 10564\u201310570 (IEEE, 2022).","DOI":"10.1109\/ICRA46639.2022.9811584"},{"key":"79292_CR3","doi-asserted-by":"publisher","first-page":"eabk2822","DOI":"10.1126\/scirobotics.abk2822","volume":"7","author":"T Miki","year":"2022","unstructured":"Miki, T. et al. Learning robust perceptive locomotion for quadrupedal robots in the wild. Sci. Robot. 7, eabk2822 (2022).","journal-title":"Sci. Robot."},{"key":"79292_CR4","doi-asserted-by":"crossref","unstructured":"Margolis, G.\u00a0B., Yang, G., Paigwar, K., Chen, T. & Agrawal, P. Rapid locomotion via reinforcement learning. arXiv:2205.02824 (2022).","DOI":"10.15607\/RSS.2022.XVIII.022"},{"key":"79292_CR5","doi-asserted-by":"crossref","unstructured":"Kumar, A., Fu, Z., Pathak, D. & Malik, J. Rma: Rapid motor adaptation for legged robots. arXiv:2107.04034 (2021).","DOI":"10.15607\/RSS.2021.XVII.011"},{"key":"79292_CR6","doi-asserted-by":"publisher","first-page":"eabc5986","DOI":"10.1126\/scirobotics.abc5986","volume":"5","author":"J Lee","year":"2020","unstructured":"Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V. & Hutter, M. Learning quadrupedal locomotion over challenging terrain. Sci. Robot. 5, eabc5986 (2020).","journal-title":"Sci. Robot."},{"key":"79292_CR7","doi-asserted-by":"crossref","unstructured":"Xie, Z., Ling, H.\u00a0Y., Kim, N.\u00a0H. & van de Panne, M. Allsteps: Curriculum-driven learning of stepping stone skills. In Computer Graphics Forum Vol.\u00a039 213\u2013224 (Wiley Online Library, 2020).","DOI":"10.1111\/cgf.14115"},{"key":"79292_CR8","doi-asserted-by":"crossref","unstructured":"Narvekar, S. & Stone, P. Learning curriculum policies for reinforcement learning. arXiv:1812.00285 (2018).","DOI":"10.24963\/ijcai.2017\/757"},{"key":"79292_CR9","unstructured":"Andrychowicz, M. et al. Hindsight experience replay. In Advances in Neural Information Processing Systems Vol. 30 (2017)."},{"key":"79292_CR10","doi-asserted-by":"crossref","unstructured":"Humphreys, J., Li, J., Wan, Y., Gao, H. & Zhou, C. Bio-inspired gait transitions for quadruped locomotion. In IEEE Robotics and Automation Letters (2023).","DOI":"10.1109\/LRA.2023.3300249"},{"key":"79292_CR11","doi-asserted-by":"crossref","unstructured":"Farshidian, F., Jelavic, E., Satapathy, A., Giftthaler, M. & Buchli, J. Real-time motion planning of legged robots: A model predictive control approach. In 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids) 577\u2013584 (IEEE, 2017).","DOI":"10.1109\/HUMANOIDS.2017.8246930"},{"key":"79292_CR12","doi-asserted-by":"publisher","first-page":"2509","DOI":"10.1109\/LCSYS.2022.3167795","volume":"6","author":"VR Kamidi","year":"2022","unstructured":"Kamidi, V. R., Kim, J., Fawcett, R. T., Ames, A. D. & Hamed, K. A. Distributed quadratic programming-based nonlinear controllers for periodic gaits on legged robots. IEEE Control Syst. Lett. 6, 2509\u20132514 (2022).","journal-title":"IEEE Control Syst. Lett."},{"key":"79292_CR13","doi-asserted-by":"publisher","first-page":"2148","DOI":"10.1109\/LRA.2019.2899664","volume":"4","author":"R Buchanan","year":"2019","unstructured":"Buchanan, R. et al. Walking posture adaptation for legged robot navigation in confined spaces. IEEE Robot. Autom. Lett. 4, 2148\u20132155 (2019).","journal-title":"IEEE Robot. Autom. Lett."},{"key":"79292_CR14","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1109\/MCS.2017.2786448","volume":"38","author":"EC Kerrigan","year":"2018","unstructured":"Kerrigan, E. C. Predictive control for linear and hybrid systems [bookshelf]. IEEE Control Syst. Mag. 38, 94\u201396 (2018).","journal-title":"IEEE Control Syst. Mag."},{"key":"79292_CR15","doi-asserted-by":"crossref","unstructured":"Di Carlo, J., Wensing, P.\u00a0M., Katz, B., Bledt, G. & Kim, S. Dynamic locomotion in the MIT cheetah 3 through convex model-predictive control. In 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) 1\u20139 (IEEE, 2018).","DOI":"10.1109\/IROS.2018.8594448"},{"key":"79292_CR16","doi-asserted-by":"crossref","unstructured":"Bledt, G. & Kim, S. Implementing regularized predictive control for simultaneous real-time footstep and ground reaction force optimization. In 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) 6316\u20136323 (IEEE, 2019).","DOI":"10.1109\/IROS40897.2019.8968031"},{"key":"79292_CR17","doi-asserted-by":"crossref","unstructured":"Mastalli, C. et al. Agile maneuvers in legged robots: A predictive control approach. arXiv:2203.07554 (2022).","DOI":"10.21203\/rs.3.rs-1870369\/v1"},{"key":"79292_CR18","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1109\/TRO.2022.3228390","volume":"39","author":"A Meduri","year":"2023","unstructured":"Meduri, A. et al. Biconmp: A nonlinear model predictive control framework for whole body motion planning. IEEE Trans. Robot. 39, 905\u2013922 (2023).","journal-title":"IEEE Trans. Robot."},{"key":"79292_CR19","doi-asserted-by":"publisher","first-page":"3013","DOI":"10.1109\/LRA.2019.2923967","volume":"4","author":"J Carius","year":"2019","unstructured":"Carius, J., Ranftl, R., Koltun, V. & Hutter, M. Trajectory optimization for legged robots with slipping motions. IEEE Robot. Autom. Lett. 4, 3013\u20133020 (2019).","journal-title":"IEEE Robot. Autom. Lett."},{"key":"79292_CR20","doi-asserted-by":"crossref","unstructured":"Wu, J., Xin, G., Qi, C. & Xue, Y. Learning robust and agile legged locomotion using adversarial motion priors. In IEEE Robotics and Automation Letters (2023).","DOI":"10.1109\/LRA.2023.3290509"},{"key":"79292_CR21","doi-asserted-by":"publisher","first-page":"eaau5872","DOI":"10.1126\/scirobotics.aau5872","volume":"4","author":"J Hwangbo","year":"2019","unstructured":"Hwangbo, J. et al. Learning agile and dynamic motor skills for legged robots. Sci. Robot. 4, eaau5872 (2019).","journal-title":"Sci. Robot."},{"key":"79292_CR22","doi-asserted-by":"crossref","unstructured":"Hoeller, D., Rudin, N., Sako, D. & Hutter, M. Anymal parkour: Learning agile navigation for quadrupedal robots. arXiv:2306.14874 (2023).","DOI":"10.1126\/scirobotics.adi7566"},{"key":"79292_CR23","doi-asserted-by":"publisher","first-page":"eade2256","DOI":"10.1126\/scirobotics.ade2256","volume":"8","author":"S Choi","year":"2023","unstructured":"Choi, S. et al. Learning quadrupedal locomotion on deformable terrain. Sci. Robot. 8, eade2256 (2023).","journal-title":"Sci. Robot."},{"key":"79292_CR24","doi-asserted-by":"publisher","first-page":"eabb2174","DOI":"10.1126\/scirobotics.abb2174","volume":"5","author":"C Yang","year":"2020","unstructured":"Yang, C., Yuan, K., Zhu, Q., Yu, W. & Li, Z. Multi-expert learning of adaptive legged locomotion. Sci. Robot. 5, eabb2174 (2020).","journal-title":"Sci. Robot."},{"key":"79292_CR25","doi-asserted-by":"crossref","unstructured":"Yang, C., Pu, C., Xin, G., Zhang, J. & Li, Z. Learning complex motor skills for legged robot fall recovery. In IEEE Robotics and Automation Letters (2023).","DOI":"10.1109\/LRA.2023.3281290"},{"key":"79292_CR26","unstructured":"Semini, C. et al. Design overview of the hydraulic quadruped robots. In The fourteenth Scandinavian International Conference on Fluid Power 20\u201322 (sn, 2015)."},{"key":"79292_CR27","unstructured":"St\u00fcckler, J., Schwenk, J. & Behnke, S. Getting back on two feet: Reliable standing-up routines for a humanoid robot. In IAS 676\u2013685 (Citeseer, 2006)."},{"key":"79292_CR28","doi-asserted-by":"crossref","unstructured":"Li, Z. et al. Fall prediction of legged robots based on energy state and its implication of balance augmentation: A study on the humanoid. In 2015 IEEE International Conference on Robotics and Automation (ICRA) 5094\u20135100 (IEEE, 2015).","DOI":"10.1109\/ICRA.2015.7139908"},{"key":"79292_CR29","doi-asserted-by":"crossref","unstructured":"Araki, K., Miwa, T., Shigemune, H., Hashimoto, S. & Sawada, H. Standing-up control of a fallen humanoid robot based on the ground-contacting state of the body. In IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society 3292\u20133297 (IEEE, 2018).","DOI":"10.1109\/IECON.2018.8591360"},{"key":"79292_CR30","doi-asserted-by":"crossref","unstructured":"Radulescu, A., Havoutis, I., Caldwell, D.\u00a0G. & Semini, C. Whole-body trajectory optimization for non-periodic dynamic motions on quadrupedal systems. In 2017 IEEE International Conference on Robotics and Automation (ICRA) 5302\u20135307 (IEEE, 2017).","DOI":"10.1109\/ICRA.2017.7989623"},{"key":"79292_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2185520.2185539","volume":"31","author":"I Mordatch","year":"2012","unstructured":"Mordatch, I., Todorov, E. & Popovi\u0107, Z. Discovery of complex behaviors through contact-invariant optimization. ACM Trans. Graph. ToG 31, 1\u20138 (2012).","journal-title":"ACM Trans. Graph. ToG"},{"key":"79292_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3528223.3530110","volume":"41","author":"XB Peng","year":"2022","unstructured":"Peng, X. B., Guo, Y., Halper, L., Levine, S. & Fidler, S. Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters. ACM Trans. Graph. TOG 41, 1\u201317 (2022).","journal-title":"ACM Trans. Graph. TOG"},{"key":"79292_CR33","doi-asserted-by":"publisher","first-page":"11045","DOI":"10.3390\/app131911045","volume":"13","author":"X Zhang","year":"2023","unstructured":"Zhang, X., Wu, Y., Wang, H., Iida, F. & Wang, L. Adaptive locomotion learning for quadruped robots by combining DRL with a cosine oscillator based rhythm controller. Appl. Sci. 13, 11045 (2023).","journal-title":"Appl. Sci."},{"key":"79292_CR34","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1109\/TCDS.2019.2896300","volume":"12","author":"J Nassour","year":"2019","unstructured":"Nassour, J., Hoa, T. D., Atoofi, P. & Hamker, F. Concrete action representation model: From neuroscience to robotics. IEEE Trans. Cognit. Dev. Syst. 12, 272\u2013284 (2019).","journal-title":"IEEE Trans. Cognit. Dev. Syst."},{"key":"79292_CR35","doi-asserted-by":"crossref","unstructured":"Li, G., Ijspeert, A. & Hayashibe, M. Ai-cpg: Adaptive imitated central pattern generators for bipedal locomotion learned through reinforced reflex neural networks. In IEEE Robotics and Automation Letters (2024).","DOI":"10.1109\/LRA.2024.3388842"},{"key":"79292_CR36","doi-asserted-by":"publisher","first-page":"jeb245784","DOI":"10.1242\/jeb.245784","volume":"226","author":"AJ Ijspeert","year":"2023","unstructured":"Ijspeert, A. J. & Daley, M. A. Integration of feedforward and feedback control in the neuromechanics of vertebrate locomotion: A review of experimental, simulation and robotic studies. J. Exp. Biol. 226, jeb245784 (2023).","journal-title":"J. Exp. Biol."},{"key":"79292_CR37","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Louradour, J., Collobert, R. & Weston, J. Curriculum learning. In Proceedings of the 26th Annual International Conference on Machine Learning 41\u201348 (2009).","DOI":"10.1145\/1553374.1553380"},{"key":"79292_CR38","unstructured":"Florensa, C., Held, D., Geng, X. & Abbeel, P. Automatic goal generation for reinforcement learning agents. In International Conference on Machine Learning 1515\u20131528 (PMLR, 2018)."},{"key":"79292_CR39","unstructured":"Graves, A., Bellemare, M.\u00a0G., Menick, J., Munos, R. & Kavukcuoglu, K. Automated curriculum learning for neural networks. In International Conference on Machine Learning 1311\u20131320 (PMLR, 2017)."},{"key":"79292_CR40","doi-asserted-by":"publisher","first-page":"4630","DOI":"10.1109\/LRA.2022.3151396","volume":"7","author":"G Ji","year":"2022","unstructured":"Ji, G., Mun, J., Kim, H. & Hwangbo, J. Concurrent training of a control policy and a state estimator for dynamic and robust legged locomotion. IEEE Robot. Autom. Lett. 7, 4630\u20134637 (2022).","journal-title":"IEEE Robot. Autom. Lett."},{"key":"79292_CR41","unstructured":"Rudin, N., Hoeller, D., Reist, P. & Hutter, M. Learning to walk in minutes using massively parallel deep reinforcement learning. In Conference on Robot Learning 91\u2013100 (PMLR, 2022)."},{"key":"79292_CR42","doi-asserted-by":"crossref","unstructured":"Nahrendra, I. M.\u00a0A., Yu, B. & Myung, H. Dreamwaq: Learning robust quadrupedal locomotion with implicit terrain imagination via deep reinforcement learning. In 2023 IEEE International Conference on Robotics and Automation (ICRA) 5078\u20135084 (IEEE, 2023).","DOI":"10.1109\/ICRA48891.2023.10161144"},{"key":"79292_CR43","unstructured":"Fang, M., Zhou, T., Du, Y., Han, L. & Zhang, Z. Curriculum-guided hindsight experience replay. In Advances in Neural Information Processing Systems Vol. 32 (2019)."},{"key":"79292_CR44","doi-asserted-by":"publisher","unstructured":"Nair, A., McGrew, B., Andrychowicz, M., Zaremba, W. & Abbeel, P. Overcoming exploration in reinforcement learning with demonstrations. In 2018 IEEE International Conference on Robotics and Automation (ICRA) 6292\u20136299. https:\/\/doi.org\/10.1109\/ICRA.2018.8463162 (2018).","DOI":"10.1109\/ICRA.2018.8463162"},{"key":"79292_CR45","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.neucom.2023.01.016","volume":"525","author":"C Han","year":"2023","unstructured":"Han, C. et al. Overfitting-avoiding goal-guided exploration for hard-exploration multi-goal reinforcement learning. Neurocomputing 525, 76\u201387 (2023).","journal-title":"Neurocomputing"},{"key":"79292_CR46","doi-asserted-by":"publisher","unstructured":"Li, B. et al. Acder: Augmented curiosity-driven experience replay. In 2020 IEEE International Conference on Robotics and Automation (ICRA), 4218\u20134224. https:\/\/doi.org\/10.1109\/ICRA40945.2020.9197421 (2020).","DOI":"10.1109\/ICRA40945.2020.9197421"},{"key":"79292_CR47","unstructured":"Coumans, E. & Bai, Y. Pybullet, a python module for physics simulation for games, robotics and machine learning. http:\/\/pybullet.org (2016\u20132021)."},{"key":"79292_CR48","doi-asserted-by":"crossref","unstructured":"Haarnoja, T. et al. Learning to walk via deep reinforcement learning. arXiv:1812.11103 (2018).","DOI":"10.15607\/RSS.2019.XV.011"},{"key":"79292_CR49","doi-asserted-by":"crossref","unstructured":"Chen, S., Zhang, B., Mueller, M.\u00a0W., Rai, A. & Sreenath, K. Learning torque control for quadrupedal locomotion. arXiv:2203.05194 (2022).","DOI":"10.1109\/Humanoids57100.2023.10375154"},{"key":"79292_CR50","unstructured":"Nair, V. & Hinton, G.\u00a0E. Rectified linear units improve restricted Boltzmann machines. In Icml (2010)."},{"key":"79292_CR51","unstructured":"Chen, X., Wang, C., Zhou, Z. & Ross, K.\u00a0W. Randomized ensembled double q-learning: Learning fast without a model. In International Conference on Learning Representations (2020)."},{"key":"79292_CR52","unstructured":"Ross, S., Gordon, G. & Bagnell, D. A reduction of imitation learning and structured prediction to no-regret online learning. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics 627\u2013635 (JMLR Workshop and Conference Proceedings, 2011)."},{"key":"79292_CR53","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE International Conference on Computer Vision 1026\u20131034 (2015).","DOI":"10.1109\/ICCV.2015.123"},{"key":"79292_CR54","unstructured":"Kingma, D.\u00a0P. & Ba, J. Adam: A method for stochastic optimization. arXiv:1412.6980 (2014)."},{"key":"79292_CR55","doi-asserted-by":"crossref","unstructured":"Tan, J. et al. Sim-to-real: Learning agile locomotion for quadruped robots. arXiv:1804.10332 (2018).","DOI":"10.15607\/RSS.2018.XIV.010"},{"key":"79292_CR56","unstructured":"Winter, D.\u00a0A. Biomechanics and Motor Control of Human Gait: Normal, Elderly and Pathological (1991)."},{"key":"79292_CR57","doi-asserted-by":"publisher","unstructured":"ihuhuhu\/chrl: v1.0.0. https:\/\/doi.org\/10.5281\/zenodo.13924712 (2024).","DOI":"10.5281\/zenodo.13924712"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-79292-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-79292-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-79292-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T21:04:02Z","timestamp":1731618242000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-79292-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,15]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["79292"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-79292-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3812430\/v1","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,15]]},"assertion":[{"value":"27 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The authors affirm that human research participants provided informed consent for publication of identifying information\/images in an online open-access publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"28089"}}