文章背景与核心概要
工业面板操作需要严格遵守复杂的安全规范和操作手册。尽管现代基础模型为任务规划提供了强大的语义理解能力，但它们通常缺乏透明、可复现且可定位的机制来检测和修复违规约束。
为了填补这一空白，作者提出了 PanelShield，这是一个专为手册引导的工业面板操作而设计的可验证闭环安全规划框架。核心亮点包括采用双重形式化验证、靶向错误修复机制以及多级长视界基准测试，在仿真和真实世界机器人实验中表现出卓越的性能，将违规率显著降低至 2.7%，总延迟仅为 4.1秒。
PanelShield: Verifiable Closed-Loop Safe Planning for Robotic Industrial Panel Operation
PanelShield: Verifiable Closed-Loop Safe Planning for Robotic Industrial Panel Operation
Authors: Guipeng Xin, Jiahe Xu, Chenhui Wan, Jie Liu, Youmin Hu, Zhongxu Hu
Primary Subject: Robotics (cs.RO), Artificial Intelligence (cs.AI)
arXiv ID: arXiv:2608.28305 | Submitted: August 28, 2026
Links: View PDF | TeX Source | DOI
Authors: Guipeng Xin, Jiahe Xu, Chenhui Wan, Jie Liu, Youmin Hu, Zhongxu Hu
Primary Subject: Robotics (cs.RO), Artificial Intelligence (cs.AI)
arXiv ID:arXiv:2608.28305| Submitted: August 28, 2026
Links: View PDF | TeX Source | DOI
Summary
Summary
Industrial panel operations demand strict adherence to complex safety regulations and operation manuals. While modern foundation models offer robust semantic understanding for task planning, they typically lack transparent, reproducible, and localizable mechanisms to detect and repair constraint violations.
工业面板操作需要严格遵守复杂的安全规范和操作手册。虽然现代基础模型为任务规划提供了强大的语义理解能力，但它们通常缺乏透明、可复现且可定位的机制来检测和修复违规约束。
To bridge this gap, the authors propose PanelShield, a verifiable closed-loop safety planning framework designed for manual-guided industrial panel operations. Key highlights include: * Dual Formal Verification: Employs Linear Temporal Logic (LTL) and a Safety Finite State Machine (FSM) to ensure cross-step temporal correctness and local transition legality. * Targeted Error Repair: Outputs structured counterexamples pinpointing the earliest violating step and root cause, allowing precise plan repair and re-verification. * Benchmarking & Validation: Evaluated via a newly built multi-level long-horizon planning benchmark across three representative industrial device panels, demonstrating superior performance in both simulation and real-world robotic experiments. * Performance: Achieved a significantly reduced violation rate of 2.7% with a total latency of 4.1 seconds, proving end-to-end feasibility and balancing flexibility, safety, and auditability.
为了填补这一空白，作者提出了 PanelShield，这是一个专为手册引导的工业面板操作而设计的可验证闭环安全规划框架。核心亮点包括： * 双重形式化验证： 采用线性时序逻辑（LTL）和一个安全有限状态机（FSM），以确保跨步骤的时间正确性和局部转换的合法性。 * 靶向错误修复： 输出结构化反例，精准定位最早违规步骤及根本原因，从而实现精确的计划修复和重新验证。 * 基准测试与验证： 通过跨越三个代表性工业设备面板的新建多级长视界规划基准进行评估，在仿真和真实世界机器人实验中均展示出卓越的性能。 * 性能表现： 将违规率显著降低至 2.7%，总延迟为 4.1秒，证明了端到端的普适可行性，并在灵活性、安全性和可审计性之间取得了平衡。
Document Metadata
Document Metadata
| Metadata Field | Details |
|---|---|
| Primary Subject | Robotics (cs.RO) |
| Secondary Subject | Artificial Intelligence (cs.AI) |
| Cite As | arXiv:2608.28305 [cs.RO] |
| License | Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)  View License |
| Associated Tools | Bibliographic Explorer, Connected Papers, Litmaps, scite.ai, alphaXiv, CatalyzeX, DagsHub, Hugging Face, ScienceCast |
元数据字段 详情 主要学科 机器人学 ( cs.RO)次要学科 人工智能 ( cs.AI)引用格式 arXiv:2608.28305 [cs.RO]许可协议 知识共享 署名-非商业性使用-禁止演绎 4.0 国际 ( CC BY-NC-ND 4.0)
关联工具 Bibliographic Explorer, Connected Papers, Litmaps, scite.ai, alphaXiv, CatalyzeX, DagsHub, Hugging Face, ScienceCast