  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 towards more scalable synthesis, robust
monitoring, and efficient intervention.
