# Efficient Dynamic Shielding for Parametric Safety Specifications

arXiv: https://arxiv.org/abs/2505.22104 · HTML: https://mlsanity.com/paper/2505.22104/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 53 references that are in the map.
- First posted on arXiv 28 May 2025.

## Builds on (most cited first, 30 of 32)

- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks](https://mlsanity.com/paper/1702.01135/) (2017)
- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [Verisig: verifying safety properties of hybrid systems with neural network controllers](https://mlsanity.com/paper/1811.01828/) (2018)
- [Safe Multi-Agent Reinforcement Learning via Shielding](https://mlsanity.com/paper/2101.11196/) (2021)
- [Safe Reinforcement Learning via Shielding under Partial Observability](https://mlsanity.com/paper/2204.00755/) (2022)
- [Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics](https://mlsanity.com/paper/1910.10885/) (2019)
- [Shield Synthesis: Runtime Enforcement for Reactive Systems](https://mlsanity.com/paper/1501.02573/) (2015)
- [Safe Reinforcement Learning via Probabilistic Logic Shields](https://mlsanity.com/paper/2303.03226/) (2023)
- [Towards Scalable Verification of Deep Reinforcement Learning](https://mlsanity.com/paper/2105.11931/) (2021)
- [Funnel Libraries for Real-Time Robust Feedback Motion Planning](https://mlsanity.com/paper/1601.04037/) (2016)
- [Online Shielding for Reinforcement Learning](https://mlsanity.com/paper/2212.01861/) (2022)
- [Adaptive Shielding under Uncertainty](https://mlsanity.com/paper/2010.03842/) (2020)
- [Dynamic Model Predictive Shielding for Provably Safe Reinforcement Learning](https://mlsanity.com/paper/2405.13863/) (2024)
- [TEMPEST -- Synthesis Tool for Reactive Systems and Shields in Probabilistic Environments](https://mlsanity.com/paper/2105.12588/) (2021)
- [Verification-Guided Shielding for Deep Reinforcement Learning](https://mlsanity.com/paper/2406.06507/) (2024)
- [SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction](https://mlsanity.com/paper/2110.00843/) (2021)
- [Shield Synthesis for LTL Modulo Theories](https://mlsanity.com/paper/2406.04184/) (2024)
- [Dynamic Shielding for Reinforcement Learning in Black-Box Environments](https://mlsanity.com/paper/2207.13446/) (2022)
- [Shield Synthesis for Real: Enforcing Safety in Cyber-Physical Systems](https://mlsanity.com/paper/1908.05402/) (2019)
- [Planning, Fast and Slow: A Framework for Adaptive Real-Time Safe Trajectory Planning](https://mlsanity.com/paper/1710.04731/) (2017)
- [Discounting in LTL](https://mlsanity.com/paper/1406.4249/) (2014)
- [Feedback Refinement Relations for the Synthesis of Symbolic Controllers](https://mlsanity.com/paper/1503.03715/) (2015)
- [EVA-Planner: Environmental Adaptive Quadrotor Planning](https://mlsanity.com/paper/2011.04246/) (2020)
- [Adaptive Shielding via Parametric Safety Proofs](https://mlsanity.com/paper/2502.18879/) (2025)
- [Forward LTLf Synthesis: DPLL At Work](https://mlsanity.com/paper/2302.13825/) (2023)
- [Context-triggered Abstraction-based Control Design](https://mlsanity.com/paper/2305.03399/) (2023)
- [Efficient Shield Synthesis via State-Space Transformation](https://mlsanity.com/paper/2407.19911/) (2024)
- [Synthesizing Permissive Winning Strategy Templates for Parity Games](https://mlsanity.com/paper/2305.14026/) (2023)
- [Safety Shielding under Delayed Observation](https://mlsanity.com/paper/2307.02164/) (2023)

## Abstract (arXiv)

Shielding has emerged as a promising approach for ensuring safety of AI-controlled autonomous systems. The algorithmic goal is to compute a shield, which is a runtime safety enforcement tool that needs to monitor and intervene the AI controller's actions if safety could be compromised otherwise. Traditional shields are designed statically for a specific safety requirement. Therefore, if the safety requirement changes at runtime due to changing operating conditions, the shield needs to be recomputed from scratch, causing delays that could be fatal. We introduce dynamic shields for parametric safety specifications, which are succinctly represented sets of all possible safety specifications that may be encountered at runtime. Our dynamic shields are statically designed for a given safety parameter set, and are able to dynamically adapt as the true safety specification (permissible by the parameters) is revealed at runtime. The main algorithmic novelty lies in the dynamic adaptation procedure, which is a simple and fast algorithm that utilizes known features of standard safety shields, like maximal permissiveness. We report experimental results for a robot navigation problem in unknown territories, where the safety specification evolves as new obstacles are discovered at runtime. In our experiments, the dynamic shields took a few minutes for their offline design, and took between a fraction of a second and a few seconds for online adaptation at each step, whereas the brute-force online recomputation approach was up to 5 times slower.