期刊
INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS
卷 13, 期 4, 页码 833-849出版社
SPRINGER
DOI: 10.1007/s12369-020-00666-5
关键词
Human-robot interaction; Taxonomy; Collaborative robots; HRI framework; Robot morphology; Collaboration
类别
资金
- Projekt DEAL
- Federal Institute for Occupational Safety and Health (BAuA) [F2369]
- HFC Human-Factors-Consult GmbH
Robotic systems are a core technology shaping our future, changing private and professional life by working alongside humans in various domains. To address the issue of comparability and generalizability caused by the diversity in robots and interaction scenarios, researchers have developed a new HRI taxonomy that enables structured comparisons and guides future research activities.
Robotic systems are one of the core technologies that will shape our future. Robots already change our private and professional life by working together with humans in various domains. Evoked by this increasing trend, great variability exists in terms of robots and interaction scenarios. This has boosted research regarding shaping factors of human-robot interaction (HRI). Nevertheless, this variety hinders the comparability and the generalizability of insights. What is needed for efficient research is a structured approach that allows the analysis of superordinate attributes, making previous HRI research comparable, revealing research gaps and thus guiding future research activities. Based on the review of previous HRI frameworks we developed a new HRI taxonomy that (1) takes into account the human, the robot, the interaction and the context of the HRI, (2) is applicable to various HRI scenarios and (3) provides predefined categories to enable structured comparisons of different HRI scenarios. A graphical representation of the taxonomy, including all possible classifications, eases the application to specific HRI scenarios. To demonstrate the use and value of this taxonomy, it is applied to different studies in HRI in order to identify possible reasons for contrasting results. The exemplified applications of the taxonomy underline its value as a basis for reviews and meta-analyses. Moreover, the taxonomy offers a framework for future HRI research as it offers guidance for systematic variations of distinctive variables in HRI.
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