The deployment of autonomous robots in various domains has raised significant concerns about their trustworthiness and accountability. This study explores the potential of Large Language Models (LLMs) in analyzing ROS 2 logs generated by autonomous robots and proposes a framework for log analysis that categorizes log files into different aspects. The study evaluates the performance of three different language models in answering questions related to StartUp, Warning, and PDDL logs. The results suggest that GPT 4, a transformer-based model, outperforms other models, however, their verbosity is not enough to answer why or how questions for all kinds of actors involved in the interaction.

翻译：自主机器人在各领域的部署引发了对其可信度和问责性的重大关切。本研究探索了大语言模型（LLMs）在分析自主机器人生成的ROS 2日志方面的潜力，并提出了一种日志分析框架，该框架将日志文件按不同方面进行分类。研究评估了三种不同语言模型在回答与StartUp、Warning及PDDL日志相关的问题时的性能。结果表明，基于Transformer架构的GPT 4模型优于其他模型，但其冗长性不足以回答涉及交互中所有类型行为体的“为什么”或“如何”类问题。