# Explaining Autonomy: Enhancing Human-Robot Interaction through Explanation Generation with Large Language Models

arXiv: https://arxiv.org/abs/2402.04206 · HTML: https://mlsanity.com/paper/2402.04206/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2024.

## Built on by (most cited first, 4 of 4)

- [Enhancing Robot Explanation Capabilities through Vision-Language Models: a Preliminary Study by Interpreting Visual Inputs for Improved Human-Robot Interaction](https://mlsanity.com/paper/2404.09705/) (2024)
- [Robot Behavior Personalization from Sparse User Feedback](https://mlsanity.com/paper/2410.19219/) (2024)
- [Personalised Explanations in Long-term Human-Robot Interactions](https://mlsanity.com/paper/2507.03049/) (2025)
- [What Questions Should Robots Be Able to Answer? A Dataset of User Questions for Explainable Robotics](https://mlsanity.com/paper/2510.16435/) (2025)

## Abstract (arXiv)

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.