ROSAnnotator: A Web Application for ROSBag Data Analysis in Human-Robot Interaction

TL;DR

ROSAnnotator is a web-based tool that integrates multimodal data annotation with ROSBags, enhancing qualitative analysis in human-robot interaction research through automation and streamlined workflows.

Contribution

It introduces a novel web application that combines qualitative annotation capabilities with ROSBags, including automated annotation via a large language model, filling a gap in HRI data analysis tools.

Findings

Supports manual and automated multimodal annotations

Enables quick statistical summaries of annotations

Provides an open interface for custom ROS messages

Abstract

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