How Can LLMs and Knowledge Graphs Contribute to Robot Safety? A Few-Shot Learning Approach

• URL: http://arxiv.org/abs/2412.11387v1
• Date: Mon, 16 Dec 2024 02:28:34 GMT
• Title: How Can LLMs and Knowledge Graphs Contribute to Robot Safety? A Few-Shot Learning Approach
• Authors: Abdulrahman Althobaiti, Angel Ayala, JingYing Gao, Ali Almutairi, Mohammad Deghat, Imran Razzak, Francisco Cruz,
• Abstract summary: Large Language Models (LLMs) are transforming the robotics domain by enabling robots to comprehend and execute natural language instructions. This paper outlines a safety layer that verifies the code generated by ChatGPT before executing it to control a drone in a simulated environment.
• Score: 8.15784886699733
• License:
• Abstract: Large Language Models (LLMs) are transforming the robotics domain by enabling robots to comprehend and execute natural language instructions. The cornerstone benefits of LLM include processing textual data from technical manuals, instructions, academic papers, and user queries based on the knowledge provided. However, deploying LLM-generated code in robotic systems without safety verification poses significant risks. This paper outlines a safety layer that verifies the code generated by ChatGPT before executing it to control a drone in a simulated environment. The safety layer consists of a fine-tuned GPT-4o model using Few-Shot learning, supported by knowledge graph prompting (KGP). Our approach improves the safety and compliance of robotic actions, ensuring that they adhere to the regulations of drone operations.

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