The emotional expression of body movement, which is an aspect of emotional communication between humans, has not been considered enough in the field of human–robot interactions (HRIs). This paper explores human perceptions of the emotional expressions of humanoid robot body movements to study the emotional design of the bodily expressions of robots and the characteristics of the human perception of these emotional body movements. Six categories of emotional behaviors, including happiness, anger, sadness, surprise, fear, and disgust, were designed by imitating human emotional body movements, and they were implemented on a Yanshee robot. A total of 135 participants were recruited for questionnaires and eye-tracking measurements. Statistical methods, including K-means clustering, repeated analysis of variance (ANOVA), Friedman’s ANOVA, and Spearman’s correlation test, were used to analyze the data. According to the statistical results of emotional categories, intensities, and arousals perceived by humans, a guide to grading the designed robot’s bodily expressions of emotion is created. By combining this guide with certain objective analyses, such as fixation and trajectory of eye movements, the characteristics of human perception, including the perceived differences between happiness and negative emotions and the trends of eye movements for different emotional categories, are described. This study not only illustrates subjective and objective evidence that humans can perceive robot bodily expressions of emotions through only vision but also provides helpful guidance for designing appropriate emotional bodily expressions in HRIs.

The interaction patterns are constantly changing as the rapid development in technology. The non-contact interaction system using gestures is becoming important in human-computer interaction nowadays. This paper illustrates a design framework of a non-contact interaction system using gestures, which fully considers the approach to fit non-contact gestures into the system. The corresponding user requirements were investigated by surveys, and the universal design guide of non-contact gestures was summarized by statistical analysis. A case study was illustrated for key points of the developed design framework. The scheme, including interaction architecture, gesture definition, and user interface design, was described. The rapid prototyping was built. Evaluations, including eye-tracking measurements and tests of system usability, were implemented and analyzed by statistical methods to present suggestions of iterative design. The feasibility of the developed framework was verified. By statistical methods, the relationship between the interaction usability and the user’s perception for information was discussed, and the user’s perception for the display of dynamic and static gestures was analyzed. The results provide a clear approach to satisfy user’s preferences and improve the interaction quality for non-contact interaction systems using gestures, and furnish empirical grounding for sustainably devising similar interaction systems.

Interaction errors are hard to avoid in the process of human–robot interaction (HRI). User emotions toward interaction errors could further affect the user’s attitudes to robots and experiences of HRI and so on. In this regard, the present study explores the effects of different factors on user emotions when interaction errors occur in HRI. There is sparse research directly studying this perspective. In so doing, three factors, including robot feedback, passive and active contexts, and previous user emotions, were considered. Two stages of online surveys with 465 participants were implemented to explore attitudes to robots and the self-reporting of emotions in active and passive HRI. Then, a Yanshee robot was selected as the experimental platform, and 61 participants were recruited for a real human–robot empirical study based on the two surveys. According to the results of statistical analysis, we conclude some design guides can cope with scenarios of interaction errors. For example, feedback and previous emotions have impacts on user emotions after encountering interaction errors, but contexts do not. There are no interactive effects between the three factors. The approach to reduce negative emotions in the cases of interaction errors in HRI, such as providing irrelevant feedback and so on, is also illustrated in the contributions.

Since service robots serving as salespersons are expected to be deployed efficiently and sustainably in retail environments, this paper explores the impacts of their interaction cues on customer experiences within small-scale self-service shops. The corresponding customer experiences are discussed in terms of fluency, comfort and likability. We analyzed customers’ shopping behaviors and designed fourteen body gestures for the robots, giving them the ability to select appropriate movements for different stages in shopping. Two experimental scenarios with and without robots were designed. For the scenario involving robots, eight cases with distinct interaction cues were implemented. Participants were recruited to measure their experiences, and statistical methods including repeated-measures ANOVA, regression analysis, etc., were used to analyze the data. The results indicate that robots solely reliant on voice interaction are unable to significantly enhance the fluency, comfort and likability effects experienced by customers. Combining a robot’s voice with the ability to imitate a human salesperson’s body movements is a feasible way to truly improve these customer experiences, and a robot’s body movements can positively influence these customer experiences in human–robot interactions (HRIs) while the use of colored light cannot. We also compiled design strategies for robot interaction cues from the perspectives of cost and controllable design. Furthermore, the relationships between fluency, comfort and likability were discussed, thereby providing meaningful insights for HRIs aimed at enhancing customer experiences.