  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.
