In order to calibrate children’s trust in robots toward appropriate levels in the interaction, reliable trust measures are necessary. Current trust measures are not suitable for measuring children’s trust in a real-time manner. While speech from adult speakers has proven to contain information on their trust, this paper presents a first exploration investigating whether these results hold up in the context of a child-robot interaction. Fifty-eight conversations between children and robots were recorded (N=29), evoking high and low trust moments in the interaction. Correlation tests showed no (strong) predictors of children’s trust in their speech. Limitations and possibilities of how to advance the investigations of an automatic trust measure for child-robot interaction are discussed.

In the current state-of-the-art, robots are bound to make errors in a human-robot interaction (HRI). Trust is one of the important concepts in HRI that is often lowered by these errors. Fortunately, research has shown there are strategies that can help rebuild trust. An apology made by the robot is one of those strategies. However, apologies can take different forms. We designed a study in which Nao first built trust with the users, then violated that trust by making a speech recognition error, and then tried to restore it by either an apology with display of remorse, without remorse, or no apology at all. The results showed expected trends; an apology with remorse in most cases rebuilt trust the strongest. Although the effect of the type of apology on the trusting beliefs were not significant, the effect on the trusting behaviours was found to be just significant. Suggestions for future research include repeating the study without its current limitations (small sample size, offline) and investigating the accuracy of the portrayed remorse by the robot.

To get a better understanding of people's natural responses to humanlike robots outside the lab, we analyzed commentary on online videos depicting robots of different humanlikeness and gender. We built on previous work, which compared online video commentary of moderately and highly humanlike robots with respect to valence, uncanny valley, threats, and objectification. Additionally, we took into account the robot's gender, its appearance, its societal impact, the attribution of mental states, and how people attribute human stereotypes to robots. The results are mostly in line with previous work. Overall, the findings indicate that moderately humanlike robot design may be preferable over highly humanlike robot design because it is less associated with negative attitudes and perceptions. Robot designers should therefore be cautious when designing highly humanlike and gendered robots.

Human-robot interaction (HRI) research aims to design natural interactions between humans and robots. Intonation, a social signaling function in human speech investigated thoroughly in linguistics, has not yet been studied in HRI. This study investigates the effect of robot speech intonation in four conditions (no intonation, focus intonation, end-of-utterance intonation, or combined intonation) on conversational naturalness, social engagement, and people's humanlike perception of the robot collecting objective and subjective data of participant conversations (n = 120). Our results showed that humanlike intonation partially improved subjective naturalness but not observed fluency, and that intonation partially improved social engagement but did not affect humanlike perceptions of the robot. Given that our results mainly differed from our hypotheses based on human speech intonation, we discuss the implications and provide suggestions for future research to further investigate conversational naturalness in robot speech intonation.

The more people commute by bicycle, the higher is the number of cyclists using their smartphones while cycling and compromising traffic safety. We have designed, implemented and evaluated two prototypes for smartphone control devices that do not require the cyclists to remove their hands from the handlebars—the three-button device Tribike and the rotation-controlled Brotate. The devices were the result of a user-centred design process where we identified the key features needed for a on-bike smartphone control device. We evaluated the devices in a biking exercise with 19 participants, where users completed a series of common smartphone tasks. The study showed that Brotate allowed for significantly more lateral control of the bicycle and both devices reduced the cognitive load required to use the smartphone. Our work contributes insights into designing interfaces for cycling.