ROVER: 規制者駆動によるブラックボックスロボット方策のロバスト時相検証
ROVER: Regulator-Driven Robust Temporal Verification of Black-Box Robot Policies
ブラックボックスなロボット方策の実行トレースを、優先度付きSTL仕様に基づき規制者インザループで評価し、再学習を導く検証手法を提案。仮想レースと移動ロボットで満足度を平均43.8%改善し、TurtleBot3で27%向上を実証。
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著者: Kristy Sakano, Jianyu An, Dinesh Manocha, Huan Xu
分類: cs.RO
原文アブストラクト
We present a novel, regulator-driven approach for the temporal verification of black-box autonomous robot policies, inspired by real-world certification processes where regulators often evaluate observable behavior without access to model internals. Central to our method is a regulator-in-the-loop approach that evaluates execution traces from black-box policies against temporal safety requirements. These requirements, expressed as prioritized Signal Temporal Logic (STL) specifications, characterize behavior changes over time and encode domain knowledge into the verification process. We use Total Robustness Value (TRV) and Largest Robustness Value (LRV) to quantify average performance and worst-case adherence, and introduce Average Violation Robustness Value (AVRV) to measure average specification violation. Together, these metrics guide targeted retraining and iterative model improvement. Our approach accommodates diverse temporal safety requirements (e.g., lane-keeping, delayed acceleration, and turn smoothness), capturing persistence, sequencing, and response across two distinct domains (virtual racing game and mobile robot navigation). Across six STL specifications in both scenarios, regulator-guided retraining increased satisfaction rates by an average of 43.8%, with consistent improvement in average performance (TRV) and reduced violation severity (LRV) in half of the specifications. Finally, real-world validation on a TurtleBot3 robot demonstrates a 27% improvement in smooth-navigation satisfaction, yielding smoother paths and stronger compliance with STL-defined temporal safety requirements.
