- The paper introduces conformal prediction as a novel approach to deliver formal, probabilistic safety guarantees for learning-enabled autonomous systems.
- It develops statistical abstractions through reachability analysis and temporal logic specifications to enable both open-loop and closed-loop control under uncertainty.
- It presents online verification algorithms using trajectory predictors to estimate future states, addressing challenges like distribution shifts in dynamic environments.
Formal Verification and Control with Conformal Prediction: Practical Safety Guarantees for Autonomous Systems
The paper focuses on the design and application of formal verification and control algorithms for autonomous systems using conformal prediction (CP). CP is a statistical tool for uncertainty quantification known for its simplicity, generality, and efficiency. It is particularly relevant for learning-enabled autonomous systems (LEASs), which incorporate complex learning-enabled components (LECs) that pose challenges for existing model-based verification techniques.
Overview of Conformal Prediction
Conformal prediction offers a distribution-free approach to uncertainty quantification, which does not assume any specific distributions for learned models or data. It generates prediction regions with formal probabilistic guarantees, making it suitable for various applications, including control and formal verification of autonomous systems. CP constructs valid prediction intervals based on nonconformity scores derived from the data, offering both marginal and calibration conditional coverage guarantees.
Applications in LEAS Verification and Control
The paper is organized to achieve several goals. Initially, it offers an accessible introduction to CP, highlighting its use in verifying LECs by analyzing input-output properties like neural networks. CP’s utility is demonstrated through both reachability analysis and the verification of more general properties expressed in logical formalisms.
Control Design with Statistical Abstractions
In the field of control design, the paper addresses the challenge of ensuring safety in dynamic and uncertain environments. The authors propose the use of CP to build statistical abstractions of environment models, which can then be integrated into control algorithms. Two fundamental approaches are discussed for constructing statistical abstractions: a naive approach that combines multiple prediction intervals and a more sophisticated single nonconformity score approach which provides tighter prediction regions. These abstractions are then used to design both open-loop and closed-loop control algorithms with probabilistic safety guarantees.
Moreover, the paper explores designing controllers constrained by temporal logic specifications. It elucidates both qualitative satisfaction checking and quantitative robustness guarantees for temporal logic specifications. The practical utility of these methods is demonstrated through tasks like robot navigation in dynamic environments and control synthesis for complex temporal specifications.
State Estimation and Perception
The authors also extend CP to state estimation and perception, which are critical for partially observable systems. They introduce the notion of statistical perceptual abstractions that account for sensor noise and the inherent uncertainty in perception algorithms. These abstractions facilitate safe control and verification in the presence of perceptual uncertainty. The paper suggests techniques for calibrating such abstractions using both conformal prediction and other statistical tools.
Online and Predictive Verification
Transitioning to online settings, the paper proposes predictive online verification methods. It emphasizes leveraging trajectory predictors to estimate future states and their uncertainties, thus enabling real-time verification. Two algorithms are developed: an accurate but less interpretable method and an interpretable method that constructs statistical abstractions for future states. Both approaches leverage CP’s ability to provide valid prediction regions to offer probabilistic performance guarantees during system operation. Additionally, robust versions of these algorithms are discussed to handle distribution shifts resulting from the sim2real gap or evolving operational conditions.
Practical Implications and Future Directions
The convergence of formal methods with CP has significant implications for the development of autonomous systems. Practically, these techniques enable the deployment of learning-enabled systems with rigorous safety and performance guarantees. Theoretically, they push forward the integration of statistical methods into formal verification and control, opening avenues for dealing with complex, high-dimensional, and uncertain systems.
Future research should focus on several open problems identified by the authors. Key areas include handling more sophisticated and dynamic interactions in multi-agent environments, improving the efficiency and accuracy of statistical and perceptual abstractions, and ensuring recursive feasibility in control algorithms. Additionally, the development of robust CP techniques that can handle larger distribution shifts and the creation of more comprehensive and user-friendly toolboxes for deploying these methods in real-world systems are crucial for advancing the state of verifiable autonomy.
In summary, the paper bridges the gap between statistical learning and formal verification/control, leveraging conformal prediction to ensure safety and performance in complex autonomous systems. Its contributions outline a clear pathway forward for research and practical application in this intersecting domain.