arXiv:2505.14689v1 Announce Type: new
Abstract: This paper presents a novel dynamic post-shielding framework that enforces the full class of $omega$-regular correctness properties over pre-computed probabilistic policies. This constitutes a paradigm shift from the predominant setting of safety-shielding — i.e., ensuring that nothing bad ever happens — to a shielding process that additionally enforces liveness — i.e., ensures that something good eventually happens. At the core, our method uses Strategy-Template-based Adaptive Runtime Shields (STARs), which leverage permissive strategy templates to enable post-shielding with minimal interference. As its main feature, STARs introduce a mechanism to dynamically control interference, allowing a tunable enforcement parameter to balance formal obligations and task-specific behavior at runtime. This allows to trigger more aggressive enforcement when needed, while allowing for optimized policy choices otherwise. In addition, STARs support runtime adaptation to changing specifications or actuator failures, making them especially suited for cyber-physical applications. We evaluate STARs on a mobile robot benchmark to demonstrate their controllable interference when enforcing (incrementally updated) $omega$-regular correctness properties over learned probabilistic policies.
Expert Commentary on Dynamic Post-Shielding Framework
The concept of a dynamic post-shielding framework that enforces both safety and liveness properties over pre-computed probabilistic policies represents a significant advancement in the field of autonomous systems and robotics. Traditionally, safety-shielding has been the primary focus, ensuring that systems never enter into undesirable states. However, this new framework expands beyond safety to include liveness properties, guaranteeing that the system eventually reaches desired states or goals.
The use of Strategy-Template-based Adaptive Runtime Shields (STARs) is a key innovation in this framework. By leveraging permissive strategy templates, STARs are able to enforce post-shielding with minimal interference, allowing for a better balance between formal correctness guarantees and task-specific behaviors. The ability to dynamically control interference with a tunable enforcement parameter is particularly noteworthy, as it provides flexibility in how aggressively the system enforces correctness properties based on the current situation.
Furthermore, the support for runtime adaptation in STARs is a crucial feature, especially in cyber-physical applications where specifications may change or components may fail. The ability to dynamically adjust to these changes ensures the continued reliability and effectiveness of the system over time.
The evaluation of STARs on a mobile robot benchmark underscores the practical applicability of this framework. By demonstrating controllable interference when enforcing $omega$-regular correctness properties over learned probabilistic policies, the study showcases the effectiveness of STARs in real-world scenarios.
Overall, the multi-disciplinary nature of this research, combining concepts from control theory, formal methods, and robotics, highlights the importance of integrating diverse expertise to push the boundaries of autonomous systems and ensure their safety and reliability in complex environments.