ROSAnnotator: A Web Application for ROSBag Data Analysis in Human-Robot Interaction
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913646026686464 |
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| author | Zhang, Yan Li, Haoqi Tabatabaei, Ramtin Johal, Wafa |
| author_facet | Zhang, Yan Li, Haoqi Tabatabaei, Ramtin Johal, Wafa |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07051 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ROSAnnotator: A Web Application for ROSBag Data Analysis in Human-Robot Interaction Zhang, Yan Li, Haoqi Tabatabaei, Ramtin Johal, Wafa Robotics Human-Computer Interaction 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 |
| title | ROSAnnotator: A Web Application for ROSBag Data Analysis in Human-Robot Interaction |
| topic | Robotics Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.07051 |