Interval POMDP Shielding for Imperfect-Perception Agents
Summary: arXiv:2604.20728v1 Announce Type: new
Abstract
Autonomous systems that rely on learned perception can make unsafe decisions when sensor readings are misclassified. In this study, we explore shielding for this context: given a proposed action, a shield blocks actions that could violate safety protocols. We focus on the prevalent scenario where system dynamics are known, but perception uncertainty must be estimated from limited labeled data. Utilizing this data, we develop confidence intervals for the probabilities of various perception outcomes and model the system as a finite Interval Partially Observable Markov Decision Process (POMDP) with discrete states and actions.
Key Findings
- We propose an innovative algorithm that computes a conservative set of beliefs over the underlying state, ensuring consistency with the observations obtained thus far.
- This method allows for the construction of a runtime shield, which comes with a finite-horizon guarantee. This means that with high probability over the training data, if the true perception uncertainty rates fall within the learned intervals, then every action permitted by the shield meets a specified lower bound on safety.
- Our experiments conducted on four different case studies demonstrate that the proposed shielding approach, along with its derived variants, significantly enhances the safety of the system compared to existing state-of-the-art baselines.
Introduction
The growing reliance on autonomous systems in critical applications raises concerns about safety, particularly in scenarios where perception may be imperfect. Mislabeled sensor data can lead to unsafe actions, emphasizing the need for robust shielding mechanisms that can intelligently filter actions based on uncertain perceptions.
Proposed Methodology
Our approach begins with the assumption that the dynamics of the system are understood, but the uncertainties in perception must be quantified. We leverage finite labeled data to create confidence intervals that provide a statistical basis for estimating perception outcomes. These intervals are crucial for modeling the decision-making process as a finite Interval POMDP.
Runtime Shielding
One of the significant contributions of this research is the development of a runtime shielding algorithm that operates under the aforementioned POMDP framework. The algorithm calculates a set of beliefs that align with past observations, thereby allowing the system to make informed decisions about which actions to permit.
Experimental Evaluation
To validate our approach, we conducted experiments across four distinct case studies. The results indicate that our shielding method not only maintains safety standards but also outperforms several contemporary approaches in terms of reliability and effectiveness.
Conclusion
In conclusion, our work on interval POMDP shielding offers a promising solution for enhancing the safety of autonomous systems facing imperfect perception challenges. By utilizing confidence intervals derived from limited data, we provide a framework that ensures safety during decision-making processes. Future research will aim to further refine these methods and explore their applicability across a broader range of autonomous applications.