用大模型自动验证机器人任务计划，提前发现逻辑漏洞
VerifyLLM: LLM-Based Pre-Execution Task Plan Verification for Robots
- 将自然语言指令转为时序逻辑，用大模型分析动作序列合理性
- 在多种家庭任务数据集上验证，有效识别计划中的潜在缺陷
- 适合开发智能机器人系统的研究者和工程师参考
在机器人领域，确保任务规划的可靠性和高效性是一项关键挑战。执行前验证高层任务计划能显著减少错误，提升系统整体性能。本文提出一种基于大语言模型（LLM）的架构，用于在仿真或真实环境中执行前自动验证高层任务计划。该方法包含两个关键步骤：首先将自然语言指令转换为线性时序逻辑（LTL），随后对动作序列进行综合分析。模块利用大模型的推理能力评估计划的逻辑一致性，并识别潜在缺失。在不同复杂度的数据集上进行严格测试，证明该模块在家庭任务场景中具有广泛适用性。本工作提升了任务规划的可靠性与效率，回应了自主系统中稳健预执行验证的关键需求。代码已公开于 https://verifyllm.github.io。
原文摘要 · Abstract (English)
In the field of robotics, researchers face a critical challenge in ensuring reliable and efficient task planning. Verifying high-level task plans before execution significantly reduces errors and enhance the overall performance of these systems. In this paper, we propose an architecture for automatically verifying high-level task plans before their execution in simulator or real-world environments. Leveraging Large Language Models (LLMs), our approach consists of two key steps: first, the conversion of natural language instructions into Linear Temporal Logic (LTL), followed by a comprehensive analysis of action sequences. The module uses the reasoning capabilities of the LLM to evaluate logical coherence and identify potential gaps in the plan. Rigorous testing on datasets of varying complexity demonstrates the broad applicability of the module to household tasks. We contribute to improving the reliability and efficiency of task planning and addresses the critical need for robust pre-execution verification in autonomous systems. The code is available at https://verifyllm.github.io.
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