This paper introduces a system designed to generate explanations for the actions performed by an autonomous robot in Human-Robot Interaction (HRI). Explainability in robotics, encapsulated within the concept of an eXplainable Autonomous Robot (XAR), is a growing research area. The work described in this paper aims to take advantage of the capabilities of Large Language Models (LLMs) in performing natural language processing tasks. This study focuses on the possibility of generating explanations using such models in combination with a Retrieval Augmented Generation (RAG) method to interpret data gathered from the logs of autonomous systems. In addition, this work also presents a formalization of the proposed explanation system. It has been evaluated through a navigation test from the European Robotics League (ERL), a Europe-wide social robotics competition. Regarding the obtained results, a validation questionnaire has been conducted to measure the quality of the explanations from the perspective of technical users. The results obtained during the experiment highlight the potential utility of LLMs in achieving explanatory capabilities in robots.

翻译：本文介绍了一个旨在为人机交互（HRI）中自主机器人执行的动作生成解释的系统。机器人领域的可解释性，体现在可解释自主机器人（XAR）这一概念中，是一个新兴的研究方向。本文所述工作旨在利用大型语言模型（LLMs）在自然语言处理任务中的能力。本研究聚焦于结合检索增强生成（RAG）方法使用这类模型生成解释的可能性，以解读从自主系统日志中收集的数据。此外，本文还提出了所提议解释系统的形式化定义。该系统已通过欧洲机器人联赛（ERL）的导航测试进行评估，该联赛是一项欧洲范围内的社交机器人竞赛。针对所获结果，我们开展了一项验证性问卷调查，从技术用户的角度衡量解释的质量。实验结果表明，LLMs在使机器人具备解释能力方面具有潜在效用。