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

Understand

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.