{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:15:33Z","timestamp":1790702133806,"version":"4.1.0"},"reference-count":363,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T00:00:00Z","timestamp":1765411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Robot. AI"],"abstract":"<jats:p>\n                    For autonomous mobile robots to operate effectively in human environments, navigation must extend beyond obstacle avoidance to incorporate social awareness. Safe and fluid interaction in shared spaces requires the ability to interpret human motion and adapt to social norms\u2014an area that is being reshaped by advances in learning-based methods. This review examines recent progress in learning-based social navigation methods that deal with the complexities of human-robot coexistence. We introduce a taxonomy of navigation methods and analyze core system components, including realistic training environments and objectives that promote socially compliant behavior. We conduct a comprehensive benchmark of existing frameworks in challenging crowd scenarios, showing their advantages and shortcomings, while providing critical insights into the architectural choices that impact performance. We find that many learning-based approaches outperform model-based methods in realistic coordination scenarios such as navigating doorways. A key highlight is the end-to-end models, which achieve strong performance by directly planning from raw sensor input, enabling more efficient and adaptive navigation. This review also maps current trends and outlines ongoing challenges, offering a strategic roadmap for future research. We emphasize the need for models that accurately anticipate human movement, training environments that realistically simulate crowded spaces, and evaluation methods that capture real-world complexity. Advancing these areas will help overcome current limitations and move social navigation systems closer to safe, reliable deployment in everyday environments. Additional resources are available at:\n                    <jats:ext-link>https:\/\/socialnavigation.github.io<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.3389\/frobt.2025.1658643","type":"journal-article","created":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T11:04:39Z","timestamp":1765451079000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Social robot navigation: a review and benchmarking of learning-based methods"],"prefix":"10.3389","volume":"12","author":[{"given":"Rashid","family":"Alyassi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cesar","family":"Cadena","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Riener","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diego","family":"Paez-Granados","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,12,11]]},"reference":[{"key":"B1","first-page":"22","article-title":"Constrained policy optimization","volume-title":"International Conference on Machine Learning","author":"Achiam","year":"2017"},{"key":"B2","first-page":"961","article-title":"Social lstm: human trajectory prediction in crowded spaces","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Alahi","year":"2016"},{"key":"B3","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/978-3-642-32723-0_15","article-title":"Optimal reciprocal collision avoidance for multiple non-holonomic robots","volume-title":"Distributed autonomous robotic systems: the 10th international symposium","author":"Alonso-Mora","year":"2013"},{"key":"B4","first-page":"1","article-title":"Autonomous Mobile robot navigation for complicated environments by switching multiple control policies","volume-title":"IECON 2022\u201348th annual conference of the IEEE industrial electronics Society","author":"Amano","year":"2022"},{"key":"B5","article-title":"Bevbert: multimodal map pre-training for language-guided navigation","author":"An","year":"2022","journal-title":"arXiv Prepr. arXiv:2212.04385"},{"key":"B6","first-page":"3674","article-title":"Vision-and-language navigation: interpreting visually-grounded navigation instructions in real environments","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Anderson","year":""},{"key":"B7","article-title":"On evaluation of embodied navigation agents","author":"Anderson","year":""},{"key":"B8","first-page":"671","article-title":"Sim-to-real transfer for vision-and-language navigation","volume-title":"Conference on robot learning","author":"Anderson","year":"2021"},{"key":"B9","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"International Conference on Machine Learning","author":"Arjovsky","year":"2017"},{"key":"B10","article-title":"Mengeros: a crowd simulation tool for autonomous robot navigation","volume-title":"AAAI fall symposium series","author":"Aroor","year":"2017"},{"key":"B11","doi-asserted-by":"crossref","first-page":"3402","DOI":"10.1109\/ROBOT.2007.363998","article-title":"Using boosted features for the detection of people in 2d range data","volume-title":"Proceedings 2007 IEEE international conference on robotics and automation","author":"Arras","year":"2007"},{"key":"B12","article-title":"Sit dataset: socially interactive pedestrian trajectory dataset for social navigation robots","volume":"36","author":"Bae","year":"2024","journal-title":"Adv. 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