VerifyLLM: LLM-Based Pre-Execution Task Plan Verification for Robots
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915374080983040 |
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| author | Grigorev, Danil S. Kovalev, Alexey K. Panov, Aleksandr I. |
| author_facet | Grigorev, Danil S. Kovalev, Alexey K. Panov, Aleksandr I. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05118 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VerifyLLM: LLM-Based Pre-Execution Task Plan Verification for Robots Grigorev, Danil S. Kovalev, Alexey K. Panov, Aleksandr I. Robotics Artificial Intelligence 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. |
| title | VerifyLLM: LLM-Based Pre-Execution Task Plan Verification for Robots |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2507.05118 |