通过视觉-语言模型增强机器人解释能力：一项通过解读视觉输入改善人机交互的初步研究
Enhancing Robot Explanation Capabilities through Vision-Language Models: a Preliminary Study by Interpreting Visual Inputs for Improved Human-Robot Interaction
AI总结：
本研究针对人机交互中机器人动作解释的需求，在原有基于大语言模型的日志解释系统基础上引入视觉-语言模型，结合日志与图像数据生成解释，经导航避障任务验证可提升障碍物识别精度与解释准确性。
摘要：
本文提出了一种基于我们先前工作的改进系统，旨在为人机交互（HRI）过程中的自主机器人动作生成解释。此前，我们开发了一个利用大语言模型（LLMs）解读日志并生成自然语言解释的系统。在本研究中，我们通过引入视觉-语言模型（VLMs）拓展了研究方法，使系统能够结合视觉输入的额外上下文分析文本日志。该方法可生成融合机器人日志数据与其捕获图像数据的解释。我们在一项基础导航任务上测试了这个增强系统，该任务中机器人需要避开人类障碍物。这项初步研究的结果表明，加入视觉解读功能可精准识别障碍物，提升所提供解释的准确性，从而改善我们系统的解释效果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
