A Taxonomy to Structure and Analyze Human–Robot Interaction

• Published in: International journal of social robotics Vol. 13; no. 4; pp. 833 - 849
• Main Authors: Onnasch, Linda, Roesler, Eileen
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.07.2021; Springer Nature B.V
• Subjects: Control; Engineering; Graphical representations; Mechatronics; Robotics; Robots; Survey; Taxonomy; Robot morphology; Human–robot interaction; Taxonomy; Collaboration; HRI framework; Collaborative robots
• ISSN: 1875-4791; 1875-4805
• DOI: 10.1007/s12369-020-00666-5

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.