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The livestock industry faces several challenges, including labor-intensive management, the threat of predators and environmental sustainability concerns. Therefore, this paper explores the integration of quadruped robots in extensive livestock farming as a novel application of field robotics. The SELF-AIR project, an acronym for Supporting Extensive Livestock Farming with the use of Autonomous Intelligent Robots, exemplifies this innovative approach. Through advanced sensors, artificial intelligence, and autonomous navigation systems, these robots exhibit remarkable capabilities in navigating diverse terrains, monitoring large herds, and aiding in various farming tasks. This work provides insight into the SELF-AIR project, presenting the lessons learned., Comment: 5 pages, 3 figures

This paper presents an improved system based on our prior work, designed to create explanations for autonomous robot actions during Human-Robot Interaction (HRI). Previously, we developed a system that used Large Language Models (LLMs) to interpret logs and produce natural language explanations. In this study, we expand our approach by incorporating Vision-Language Models (VLMs), enabling the system to analyze textual logs with the added context of visual input. This method allows for generating explanations that combine data from the robot's logs and the images it captures. We tested this enhanced system on a basic navigation task where the robot needs to avoid a human obstacle. The findings from this preliminary study indicate that adding visual interpretation improves our system's explanations by precisely identifying obstacles and increasing the accuracy of the explanations provided., Comment: 5 pages, 4 figures. This paper is a preprint of an article submitted to the Robot Trust for Symbiotic Societies (RTSS) workshop (ICRA 2024)

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., Comment: 26 pages, 15 Figures, 11 Tables. This paper is a preprint of an article submitted to the International Journal of Social Robotics