Interaction Studies - Volume 20, Issue 3, 2019

Social robots progressively enter children’s lives, but little is known about children’s acceptance of social robots and its antecedents. To fill this research gap, this narrative review surveyed 34 articles on child-robot interaction published between 2000 and 2017. We focused on robot, user, and interaction characteristics as potential antecedents of children’s intentional and behavioral social robot acceptance. In general, children readily accept robots. However, we found that social, adaptive robot behavior, children’s sex and age, as well as frequency of the interaction seem to affect acceptance. Additionally, we found various theoretical and methodological shortcomings in the field. The review concludes with recommendations and directions for future research on children’s acceptance of social robots.

Researchers have reported that companion robots have had positive effects on older adults with depression. However, there has been little quantitative analysis on the relationship between robot design and depression. To address this, we surveyed 191 older adults and investigated the impact of age, gender and depression level on design preferences for companion robots. We focused on toy-sized companion robots and evaluated three design elements: type, weight and material. The findings show that baby-type robots were the most preferred by older adults. They favoured the lightest weights and microfibre materials, regardless of the independent variables. Moreover, robot weight preferences varied significantly with the level of depression. Highly depressed older adults disliked heavy robots. These preliminary findings suggest that companion robots need to be designed with careful consideration of their physical characteristics and potential psychological effects.

Trust is a key dimension of human-robot interaction (HRI), and has often been studied in the HRI community. A common challenge arises from the difficulty of assessing trust levels in ecologically invalid environments: we present in this paper two independent laboratory studies, totalling 160 participants, where we investigate the impact of different types of errors on resulting trust, using both behavioural and subjective measures of trust. While we found a (weak) general effect of errors on reported and observed level of trust, no significant differences between the type of errors were found in either of our studies. We discuss this negative result in light of our experimental protocols, and argue for the community to move towards alternative methodologies to assess trust.

Using artificial emotions helps in making human-robot interaction more personalised, natural, and so more likeable. In the case of humanoid robots with constrained facial expression, the literature concentrates on the expression of emotions by using other nonverbal interaction channels. When using multi-modal communication, indeed, it is important to understand the effect of the combination of such non-verbal cues, while the majority of the works addressed only the role of single channels in the human recognition performance. Here, we present an attempt to analyse the effect of the combination of different animations expressing the same emotion or different ones. Results show that when an emotion is successfully expressed using a single channel, the combination of this channel with other animations, that may have lower recognition rates, appears to be less communicative.

Social robots as companions play an increasingly important role in our everyday life. However, reaching the full potential of social robots and the interaction between humans and robots requires permanent collection and processing of personal data of users, e.g. video and audio data for image and speech recognition. In order to foster user acceptance, trust and to address legal requirements as the General Data Protection Regulation of the EU, privacy needs to be integrated in the design process of social robots. The Privacy by Design approach by Cavoukian indicates the relevance of a privacy-respecting development and outlines seven abstract principle.

In this paper two methods as a hands-on guideline to fulfill the principles are presented and discussed in the content of the Privacy by Design approach. Privacy risks of a typical robot scenario are identified, analyzed and solutions are proposed on the basis of the seven types of privacy and the privacy protection goals.

This paper outlines the methodology and experiments associated with the reshaping of human intentions based on robot movements within Human-Robot Interactions (HRIs). Although studies on estimating human intentions are well studied in the literature, reshaping intentions through robot-initiated interactions is a new significant branching in the field of HRI. In this paper, we analyze how estimated human intentions can intentionally change through cooperation with mobile robots in real Human-Robot environments. This paper proposes an intention-reshaping system that includes either the Observable Operator Models (OOMs) or Hidden Markov Models (HMMs) to estimate human intention and decide which moves a robot should perform to reshape previously estimated human intentions into desired ones. At the low level, the system needs to track the locations of all mobile agents using cameras. We test our system on videos taken in a real HRI environment that has been developed as our experimental setup. The results show that OOMs are faster than HMMs and both models give correct decisions for testing sequences.