למידת פוליצי נוירלית בטוחה ורובוסטה עם וריפיקציה סטטיסטית להפעלה בסימולציה-למציאות ברובוטיקה

תקציר מקורי באנגליתarXiv:2608.06481v2 Announce Type: replace-cross Abstract: Synthesizing safe and robust neural controllers in simulation for reliable sim-to-real deployment remains a critical challenge in robotics. Existing learning-based methods typically lack safety and performance guarantees over an explicitly defined operating region, while post-training verification techniques provide no mechanism to refine controllers when safety violations are detected. To bridge this gap, we propose a curriculum-driven framework that tightly integrates scenario-based Evolution Strategy with Statistical Model Checking-based verification in a closed-loop procedure. Starting from a candidate region, our approach co-optimizes policy performance while progressively enlarging its safe operating boundaries. Upon terminati