让机器人交互数据标注更高效，支持多模态自动与手动标注。
ROSAnnotator: A Web Application for ROSBag Data Analysis in Human-Robot Interaction
- 基于网页的多模态大模型工具，集成视频、音频、文字标注功能。
- 支持实时统计分析，提升人机交互数据处理效率。
- 开放接口可扩展自定义消息格式，适合研究者快速上手。
人机交互（HRI）是融合定量与定性方法的跨学科领域。尽管机器人操作系统（ROS）中的ROSBag文件格式能高效采集真实机器人实验中时间同步的多模态数据，但缺乏将定性编码与分析功能整合到ROSBag的专用工具。为此，我们开发了ROSAnnotator——一个基于网页的多模态大语言模型（LLM）应用，支持对视频、音频及转录文本的自动与手动标注，并提供开放接口以接入自定义ROS消息和分析工具。通过使用ROSAnnotator，研究者可简化定性分析流程，构建更连贯的数据分析工作流，并快速获取标注的统计摘要，显著提升人机交互数据研究的整体效率。
原文摘要 · Abstract (English)
Human-robot interaction (HRI) is an interdisciplinary field that utilises both quantitative and qualitative methods. While ROSBags, a file format within the Robot Operating System (ROS), offer an efficient means of collecting temporally synched multimodal data in empirical studies with real robots, there is a lack of tools specifically designed to integrate qualitative coding and analysis functions with ROSBags. To address this gap, we developed ROSAnnotator, a web-based application that incorporates a multimodal Large Language Model (LLM) to support both manual and automated annotation of ROSBag data. ROSAnnotator currently facilitates video, audio, and transcription annotations and provides an open interface for custom ROS messages and tools. By using ROSAnnotator, researchers can streamline the qualitative analysis process, create a more cohesive analysis pipeline, and quickly access statistical summaries of annotations, thereby enhancing the overall efficiency of HRI data analysis. https://github.com/CHRI-Lab/ROSAnnotator
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