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.

翻译：近年来，机器人在自主性领域取得了显著进展，同时机器人技术的应用范围也在不断扩大。然而，新的部署场景的出现给确保这些系统的安全运行带来了前所未有的挑战，而安全运行始终至关重要。尽管传统的基于模型的安全控制方法在泛化性和可扩展性方面存在困难，新兴的数据驱动方法往往缺乏充分理解的保证，可能导致不可预测的灾难性故障。下一代自主机器人的成功部署需要整合这两种范式的优势。本文综述了安全过滤器方法，强调了现有技术之间的重要联系，并提出了一个统一的技术框架来理解、比较和组合它们。这种新的统一视角揭示了看似不同的安全过滤器类别之间共享的模块化结构，并自然地为未来在更可扩展的合成、鲁棒的监控和高效的干预方面的进展指明了方向。