PanelShield: Verifiable Closed-Loop Safe Planning for Robotic Industrial Panel Operation

2026-08-28

AI Summary

为解决工业面板操作中的安全性和合规性问题，提出PanelShield框架，通过生成参数化动作序列并结合LTL和Safety FSM进行验证，以确保操作的安全性和合法性。

Abstract

Industrial panel operation is knowledge-intensive and safety-critical. Beyond control recognition and action generation, execution must satisfy constraints in operation manuals and safety regulations. While foundation-model-based planners show strong semantic capability, they typically lack computable, localizable, and reproducible mechanisms for violation detection and repair. To address this, we propose PanelShield, a verifiable closed-loop safety planning framework for manual-guided industrial panel operation. The framework generates parameterized action primitive sequences from task-relevant manual evidence and applies dual formal verification with LTL and a Safety FSM to enforce cross-step temporal correctness and local transition legality. When violations occur, it outputs a structured counterexample with the earliest violating step and cause, enabling targeted repair and re-verification. We build a multi-level long-horizon planning benchmark covering three representative industrial device panels, and evaluate the framework in simulation and real-world robotic experiments. Results show that PanelShield improves complex safety-constrained task performance over foundation-model-only planning baselines while reducing the violation rate to 2.7%, with 4.1 s total latency. Real-world experiments demonstrate end-toend feasibility. Overall, PanelShield offers a verifiable approach to robotic panel operation that balances flexibility, safety, and auditability.

Problem

• Industrial Panel Operation
• Safety Regulations
• Violation Detection
• Reproducibility
• Semantic Capability

Innovation

• verifiable closed-loop safety planning
• dual formal verification
• LTL and Safety FSM
• targeted repair and re-verification