SAFE-SMART: Safety Analysis and Formal Evaluation using STL Metrics for Autonomous RoboTs

2025-11-21

AI Summary

Learning-based black-box autonomous mobile robots struggle to satisfy dynamically evolving human safety requirements. Method: This paper proposes a regulator-driven, post-hoc safety assessment framework. Its core innovations include: (i) systematically modeling human safety requirements as Signal Temporal Logic (STL) specifications for the first time; (ii) introducing differentiable, quantitative safety metrics—Total Robustness Value (TRV) and Local Robustness Value (LRV); and (iii) enabling closed-loop model retraining via external trajectory verification and robustness feedback. Results: In virtual driving tasks, speeding and lane-deviation violations decreased by 177% and 1138%, respectively. In robot navigation experiments, sharp-turn evasive capability improved by 300%, and time-to-collision with obstacles reduced by 49%. Real-world robotic deployment validates both effectiveness and generalizability.

Technology Category

• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Intelligent Robots: State Estimation

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

We present a novel, regulator-driven approach for post hoc safety evaluation of learning-based, black-box autonomous mobile robots, ensuring ongoing compliance with evolving, human-defined safety rules. In our iterative workflow, human safety requirements are translated by regulators into Signal Temporal Logic (STL) specifications. Rollout traces from the black-box model are externally verified for compliance, yielding quantitative safety metrics, Total Robustness Value (TRV) and Largest Robustness Value (LRV), which measure average and worst-case specification adherence. These metrics inform targeted retraining and iterative improvement by model designers. We apply our method across two different applications: a virtual driving scenario and an autonomous mobile robot navigating a complex environment, and observe statistically significant improvements across both scenarios. In the virtual driving scenario, we see a 177% increase in traces adhering to the simulation speed limit, a 1138% increase in traces minimizing off-road driving, and a 16% increase in traces successfully reaching the goal within the time limit. In the autonomous navigation scenario, there is a 300% increase in traces avoiding sharp turns, a 200% increase in traces reaching the goal within the time limit, and a 49% increase in traces minimizing time spent near obstacles. Finally, we validate our approach on a TurtleBot3 robot in the real world, and demonstrate improved obstacle navigation with safety buffers.

Problem

• Post hoc safety evaluation of black-box autonomous mobile robots
• Ensuring compliance with human-defined safety rules using STL specifications
• Quantitative safety metrics for iterative improvement of robot behavior

Innovation

• Post hoc safety evaluation using STL specifications
• Quantitative metrics TRV and LRV measure compliance
• Iterative retraining based on formal verification results