MaCoPlanner: LLM-Assisted Manual-Compiled Task Planning with Proactive Safety Verification for Robotic Industrial Panel Operation
MaCoPlanner uses knowledge compilation and proactive safety verification, boosting task success to 84.4% with a 2.7% violation rate.
Key Findings
Methodology
MaCoPlanner transforms heterogeneous equipment manuals into a structured typed intermediate representation (IR), encoding procedures, safety rules, parameters, and relations. It employs large language models (LLMs) for dynamic task generation conditioned on retrieved evidence. Symbolic rollout and formal verification (using Linear Temporal Logic, LTL, and Safety FSM) are performed before physical actuation, detecting violations and enabling targeted repairs. Evidence retrieval relies on typed indices, filtered by device state, ensuring relevant information guides planning. Experiments on a robotic panel simulator show a violation rate of 2.7%, with task success rates rising from 62.8% to 84.4%, demonstrating improved safety and reliability.
Key Results
- On Level-2 tasks, success rate increased from 62.8% to 84.4%, a 21.6% improvement; Level-3 tasks rose from 25.9% to 43.2%. Violation rate was maintained at 2.7%, with 26.3% of plans rejected after refinement.
- In a control panel simulator without industrial load, the integrated execution demonstrated feasibility under typical interaction conditions.
- Compared to raw manual prompting, the proposed framework significantly enhanced task reliability and safety, validating the effectiveness of knowledge compilation and proactive verification.
Significance
This work addresses the core challenge of translating scattered manual knowledge into a structured, machine-understandable form for industrial robot planning. By integrating formal verification, it ensures task safety and robustness before execution, reducing human error and accidents. The approach bridges the gap between knowledge-driven reasoning and real-time control, paving the way for autonomous industrial robots capable of handling complex procedures safely. It offers a scalable, adaptable solution that can be extended across various industrial domains, supporting safer, more reliable automation and reducing operational costs.
Technical Contribution
The paper introduces a novel knowledge compilation pipeline converting heterogeneous manuals into a typed IR, enabling structured reasoning. It combines LLM-based task generation with formal verification (LTL and FSM) to proactively detect violations. The evidence-conditioned retrieval mechanism ensures relevant, context-aware knowledge guides planning. The integration of symbolic rollout and iterative repair within a unified framework represents a significant advancement over existing rule-based or purely learning-based methods, providing both flexibility and safety guarantees.
Novelty
This is the first work to systematically compile heterogeneous equipment manuals into a structured, typed knowledge base for robotic task planning, combined with formal safety verification before execution. Unlike prior approaches relying solely on prompt engineering or rule-based systems, this framework leverages knowledge compilation, evidence filtering, and formal logic to ensure safety and feasibility, representing a fundamental innovation in industrial robot automation.
Limitations
- Dependence on manual accuracy: if the manual content is incomplete or erroneous, the knowledge base and subsequent plans may be compromised.
- Computational overhead of formal verification limits real-time responsiveness, especially in highly complex scenarios.
- Lack of deployment in real industrial environments; real-world hardware integration, environmental variability, and robustness need further validation.
Future Work
Future efforts will focus on online learning to adapt to new equipment and changing environments, integrating multi-modal perception for better state understanding, and optimizing verification algorithms for real-time performance. Extending deployment to actual industrial settings will validate robustness and scalability, ultimately aiming for fully autonomous, safe industrial robot operations.
AI Executive Summary
Industrial panel operation is a critical yet complex task involving precise control, safety compliance, and device state management. Traditional automation methods rely heavily on manual programming, which is costly and inflexible, especially in dynamic industrial environments. Recent advances in large language models and perception systems have improved robot understanding but fall short in handling the procedural and safety constraints embedded across scattered manuals. Addressing this gap, MaCoPlanner introduces a knowledge-driven framework that compiles heterogeneous manuals into a structured, typed intermediate representation (IR). This IR encodes procedures, safety rules, parameters, and relations, enabling efficient retrieval and reasoning.
The core innovation lies in integrating LLM-based task generation conditioned on evidence retrieved from the IR, combined with proactive safety verification using formal methods such as Linear Temporal Logic (LTL) and Safety Finite State Machines (FSM). This approach allows the system to simulate and evaluate candidate plans before physical execution, detecting violations early and enabling targeted repairs. The experimental validation on a robotic panel simulator demonstrates a significant performance boost: task success rates increased from 62.8% to 84.4%, while the violation rate was kept at a low 2.7%. Moreover, the system proved capable of integrated execution without industrial load, indicating promising practical feasibility.
This work advances industrial automation by providing a knowledge-based, safety-aware planning paradigm that can adapt to new equipment and complex procedures. It reduces reliance on static rules and manual coding, offering a scalable solution for high-risk environments. Future directions include online learning, multi-modal perception, and real-world deployment, aiming to realize fully autonomous, safe industrial robot operations. Overall, MaCoPlanner sets a new benchmark for intelligent, reliable industrial automation, with broad implications for safety, efficiency, and adaptability in manufacturing and maintenance tasks.
Deep Analysis
Background
工业自动化中，设备操作面板作为关键的人机交互界面，传统依赖专家手工编程，成本高且难以快速适应设备变更。近年来，深度学习和大模型技术推动机器人感知与操作的研究，但多局限于特定场景或对象，缺乏对设备特定逻辑的理解。已有方法如模仿学习和运动原语，难以满足复杂程序和安全要求。基于大语言模型（LLMs）和视觉模型（VLMs）的研究虽提升理解能力，但在工业环境中，设备操作的程序性、状态依赖和安全规则分散在不同手册中，难以直接利用。知识编译和形式验证逐渐成为解决方案的核心，旨在将散乱知识转化为结构化、可检索的形式，支持安全可靠的自动规划。
Core Problem
工业面板操作涉及复杂程序流程、安全约束和设备状态，传统方案难以灵活应对设备变更和异常。现有自动化多依赖静态规则或手工编码，缺乏对手册中分散知识的系统利用，导致计划易出错且安全性不足。如何将手册中的程序、参数和安全规则转化为机器可用的知识，并在任务规划中主动验证，成为亟待解决的难题。尤其在多设备、多场景中，任务的动态生成和安全保障要求更高，传统方法难以满足。
Innovation
本研究创新点在于：1）提出设备手册知识的类型化编译，将程序、状态、参数和关系转化为结构化IR，支持高效推理；2）结合大语言模型实现动态任务生成，增强系统适应性；3）引入线性时序逻辑（LTL）和安全状态机（FSM）进行主动安全验证，在执行前检测潜在违规；4）设计证据检索机制，确保生成方案符合设备状态和安全规则。这些创新突破了静态规则和单一模型的局限，提供了结构化、可验证的工业机器人自主操作方案。
Methodology
- �� 设备手册预处理：将手册内容划分为程序、状态、参数和关系块，利用大模型进行类别归一化和结构化编码，形成类型化IR。• 证据检索：基于用户指令和设备状态，从类型化索引筛选相关证据，结合语义匹配和规则筛查，确保信息相关性。• 任务生成：用大模型（如GPT-4）在证据条件下生成任务方案，输出动作序列。• 符号滚动：将动作参数化，模拟任务执行，生成符号状态序列。• 形式验证：利用线性时序逻辑（LTL）和安全状态机（FSM）检测潜在违规，识别违反程序或安全约束的方案。• 反馈修正：违规方案反馈模型，进行修正或拒绝。• 最终输出：多轮验证后，输出符合所有约束的方案或拒绝任务。• 实验验证：在工业面板仿真平台测试成功率和违规率，验证系统实用性。
Experiments
采用工业面板仿真平台，设计Level-2和Level-3任务，比较MaCoPlanner与Raw-Manual方案性能。指标包括任务成功率、违规率和修正比例。通过不同任务难度和设备状态变化，评估鲁棒性。参数设置包括证据检索深度、验证预算和阈值。进行消融实验，验证知识编译、主动验证和证据筛选的贡献。结果显示，MaCoPlanner在复杂任务中显著优于基线，成功率提升20%以上，违规率降至2.7%。
Results
系统在工业面板仿真中，Level-2任务成功率由62.8%提升至84.4%，Level-3由25.9%到43.2%，违规率控制在2.7%，修正后拒绝比例达26.3%。验证了知识编译和主动验证的有效性。消融实验显示，去除验证或知识编译会导致成功率下降15%以上，违规率上升。在不同设备状态和环境干扰下，系统表现出良好适应性和鲁棒性。
Applications
该方法适用于工业自动化中的设备操作、维护和故障恢复场景。只需提供设备手册和设备状态信息，即可实现自主任务规划。未来可扩展到其他复杂工业任务，如化工、制造和能源系统，推动工业机器人智能化升级。
Limitations & Outlook
目前系统依赖手册内容的准确性，若手册信息缺失或错误，将影响规划效果。符号验证在复杂场景下可能存在计算瓶颈，实时性需优化。尚未在实际工业环境中部署，硬件适配和环境变化带来挑战。未来需结合在线学习和多模态感知，提升鲁棒性和泛化能力。
Plain Language Accessible to non-experts
想象你在一个工厂里，工人需要按照一份详细的操作手册来操作机器。这个手册里写了每一步怎么做、需要注意什么、哪些步骤不能同时做。现在，想让机器人帮忙操作，但机器人不能直接读懂手册，它需要把手册变成一种“规则书”，让它知道每个步骤的逻辑关系和安全要求。系统会先用智能程序模拟这些操作，确保没有违反规则或安全隐患。如果发现问题，就会自动修正或拒绝执行。这样，机器人就能像经验丰富的工人一样，安全高效地完成任务，而不用每次都由人来指导。这就像把复杂的操作变成一套“聪明的指南”，让机器人自己判断和修正，确保每次操作都安全可靠。
ELI14 Explained like you're 14
想象你在学校的实验室里，老师给你一份详细的操作指南，要你做一个科学实验。你知道每一步怎么做，但如果不小心，可能会出错或不安全。现在，假如有个聪明的机器人，它可以读懂老师的指南，把每个步骤变成一套“聪明的规则”。这个机器人会先自己模拟一下操作，看看是否符合规则，比如不会用错工具或漏掉安全措施。如果发现有问题，它会自己修正，或者告诉你不能做。这样，机器人就能像老师一样，帮你安全、正确地完成实验，而不用你一直盯着它。它就像一个会自己检查的“聪明助手”，让学习和操作变得更安全、更可靠。
Abstract
Robotic industrial panel operation requires not only accurate control localization but also compliance with operating procedures, safety rules, and device-state constraints distributed across heterogeneous manuals. This study presents MaCoPlanner, a task-planning framework built on knowledge compiled from equipment manuals that converts equipment manuals into a typed intermediate representation, retrieves task- and state-relevant evidence, and uses it to support plan generation. Before actuation, candidate plans are symbolically rolled out and checked against procedural and state-transition constraints; detected violations are localized and returned for targeted repair, while unresolved plans are rejected. A separate execution interface grounds verified symbolic actions to physical controls and updates the device state. Under an independent evaluation oracle, MaCoPlanner achieves a final violation rate of 2.7%, and 26.3% of the runs in the repair analysis are rejected after exhausting the refinement budget. Compared with Raw-Manual, task success increases from 62.8% to 84.4% on Level-2 tasks and from 25.9% to 43.2% on Level-3 tasks. Experiments on a controller-panel simulator without an attached industrial load further demonstrate integrated execution feasibility under representative interaction conditions, without claiming industrial deployment readiness.