Safe and Robust Neural Policy Learning with Statistical Verification for Sim-to-Real Deployment in Robotics
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 ...
| Authors | Riccardo Curcio, Hongpeng Cao, Marco Caccamo |
|---|---|
| Primary category | cs.RO |
| Published | 6 August 2026 |
| Last updated at source | 2 October 2026 |
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