{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T15:01:44Z","timestamp":1781622104397,"version":"3.54.5"},"reference-count":122,"publisher":"Annual Reviews","issue":"1","license":[{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,10]]},"abstract":"<jats:p>Recent years have seen significant progress in the realm of robot autonomy, accompanied by the expanding reach of robotic technologies. However, the emergence of new deployment domains brings unprecedented challenges in ensuring safe operation of these systems, which remains as crucial as ever. While traditional model-based safe control methods struggle with generalizability and scalability, emerging data-driven approaches tend to lack well-understood guarantees, which can result in unpredictable catastrophic failures. Successful deployment of the next generation of autonomous robots will require integrating the strengths of both paradigms. This article provides a review of safety filter approaches, highlighting important connections between existing techniques and proposing a unified technical framework to understand, compare, and combine them. The new unified view exposes a shared modular structure across a range of seemingly disparate safety filter classes and naturally suggests directions for future progress toward more scalable synthesis, robust monitoring, and efficient intervention.<\/jats:p>","DOI":"10.1146\/annurev-control-071723-102940","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T15:36:37Z","timestamp":1707752197000},"page":"47-72","source":"Crossref","is-referenced-by-count":82,"title":["The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems"],"prefix":"10.1146","volume":"7","author":[{"given":"Kai-Chieh","family":"Hsu","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Princeton University, Princeton, New Jersey, USA; email: kaichieh@princeton.edu, haiminh@princeton.edu, jfisac@princeton.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haimin","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Princeton University, Princeton, New Jersey, USA; email: kaichieh@princeton.edu, haiminh@princeton.edu, jfisac@princeton.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaime F.","family":"Fisac","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Princeton University, Princeton, New Jersey, USA; email: kaichieh@princeton.edu, haiminh@princeton.edu, jfisac@princeton.edu"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"22","reference":[{"key":"B1","doi-asserted-by":"crossref","first-page":"6271","DOI":"10.1109\/CDC.2014.7040372","article-title":"Control barrier function based quadratic programs with application to adaptive cruise control","volume-title":"53rd IEEE Conference on Decision and Control","year":"2014"},{"key":"B2","doi-asserted-by":"crossref","first-page":"109597","DOI":"10.1016\/j.automatica.2021.109597","article-title":"A predictive safety filter for learning-based control of constrained nonlinear dynamical systems","volume":"129","year":"2021","journal-title":"Automatica"},{"key":"B3","first-page":"2242","article-title":"Hamilton-Jacobi reachability: a brief overview and recent advances","volume-title":"2017 IEEE 56th Annual Conference on Decision and Control","year":"2017"},{"key":"B4","first-page":"3717","article-title":"Learning control barrier functions from expert demonstrations","volume-title":"2020 59th IEEE Conference on Decision and Control","year":"2020"},{"key":"B5","first-page":"90","article-title":"ISAACS: iterative soft adversarial actor-critic for safety","volume-title":"Proceedings of the 5th Annual Learning for Dynamics and Control Conference","year":"2023"},{"key":"B6","first-page":"1817","article-title":"DeepReach: a deep learning approach to high-dimensional reachability","volume-title":"2021 IEEE International Conference on Robotics and Automation","year":"2021"},{"key":"B7","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1146\/annurev-control-060117-104941","article-title":"Hamilton\u2013Jacobi reachability: some recent theoretical advances and applications in unmanned airspace management","volume":"1","year":"2018","journal-title":"Annu. 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