Design space taxonomy and investigation of gesture-based transparency cues in human-robot interaction

Although gesture-based explanations for robotic behavior seem promising for enhancing the transparency of robotic systems, careful design is crucial to their success. Based on a pilot laboratory study ( N = 42) that highlights the need for a systematic framework for gesture-based design of transparent robotic systems, we propose an initial 12-dimensional design space taxonomy, clustered in three levels (communicational, spatial, and motor), to develop gesture-based robotic explanations. To explore the design space taxonomy, we used it to create five gesture-based explanations for robotic behavior deviating from the users' goal (i.e., a robot arm in a smart kitchen environment serving a different type of soda can than the one ordered and indicating that the ordered can was empty by showing it to users, pointing at the lid, shaking it, or (hinting at) disposing of it). We investigated the effect of these gestures on users' understanding, perception of technology transparency, and further interaction-related variables in a video-based between-subjects user study ( N = 235), including a control condition without a gesture-based explanation. Results showed that the show gesture tended to be most effective at creating understanding and transparency perception, while other gestures were ineffective, and the shake gesture even reduced facets of perceived technology transparency compared to the control condition. The findings point to gestures as a potential means of making robot actions understandable, while at the same time highlighting the design challenges due to still insufficient user understanding of the robot's behavior across conditions and a lack of significant differences compared to the control condition. Moreover, as improvements in understanding were not correspondingly reflected in increased transparency perceptions, our studies indicate that these two outcomes may yield different results and that both should be considered separately in explanatory robot design. Nevertheless, the results suggest that direct gestures in users' proximity with explicit evidence presentation might be most promising. This highlights the relevance of our design space taxonomy for systematically exploring robotic gesture designs and investigating their effectiveness in specific use contexts.