Favorable assessments of social robots are addressed in several research and development attempts because positive attitudes and intentions towards technology are regarded as a necessary prerequisite for usage. To predict a favorable evaluation, it is inevitable to understand the appraisal process and determine crucial variables that affect the evaluative and behavioral consequences of HRI. Robotic morphology has been identified as one of these variables. In the present work, we expand previous work by demonstrating that capability attributions associated with robots’ morphological features explain variations in evaluations. Based on two large picture-based online studies (Study 1, n = 673; Study 2, n = 586) we show that robots with similar morphological features (e.g., robots with arms and grippers) can be clustered along their assigned capabilities, and that these capabilities (e.g., to manipulate objects) explain evaluations of the robots in terms of acceptance and social attributes (i.e., warmth, competence, and discomfort). We discuss whether these initial assessments are relevant to live interactions and how our results can inform robot design.

This study investigates whether the informative content of negative robot feedback influences perceived adequacy of feedback and willingness to improve decisions made during an allocation task. For this purpose, 153 subjects received feedback from a robotic co-worker after an allocation task, which provided either process-level information, task-level information, self-regulation information, or person-specific feedback. As the results of this study indicate, negative robot feedback that is related to the task and contains information about the current progress and potentials for improvement is perceived as more appropriate and leads to a higher willingness to improve than feedback which is neither informative nor specific regarding the handled task.

A pre-registered conceptual video-based replication of a laboratory experiment was conducted to test whether the impact of a robot’s non-functional touch to a human can be studied from observation (online). Therefore, n=92 participants watched either a video recording of the same human–robot interaction with or without touch. The interpretation, evaluation, and emotional as well as behavioral responses were collected by means of an online-survey. The results show that the observation of touch affects observers’ emotional state: Contrary to what was hypothesized, observers felt significantly better when no touch was visible and they evaluated the robot’s touch as inappropriate. The findings are compared to results from a laboratory experiment to raise awareness for the different perspectives involved in observing and experiencing touch.

Social robots are increasingly applied to support children’s learning, but how a robot can foster (or may hinder) learning is still not fully clear. One technique used by teachers is scaffolding, temporarily assisting learners to achieve new skills or levels of understanding they would not reach on their own. We ask if and how a social robot can be utilized to scaffold second-language learning of children at kindergarten age (4--7 years). Specifically, we explore an adapt-and-explain scaffolding strategy in which a robot acts as a peer-like tutor who dynamically adapts its behavior or the learning tasks to the cognitive and affective state of the child, and provides verbal explanations of these adaptations. An evaluation study with 40 children shows that children benefit from the learning adaptation and that the explanations have a positive effect especially for slower learners. Further, in 76% of all cases the robot managed to “re-engage” children who started to disengage from the learning interaction, helping them to achieve an overall higher learning gain. These findings demonstrate that a social robot equipped with suitable scaffolding mechanisms can increase engagement and learning, especially when being adaptive to the individual behavior and states of a child learner.

This video illustrates the large-scale experiment of the L2TOR project that will be presented at the HRI 2019 conference. The experiment aimed to investigate how 192 Dutch 5-year-old children could learn 34 English words from a NAO robot in 7 lessons. The experiment compared 4 conditions: 1) robot using iconic gestures, 2) robot without iconic gestures, 3) tablet only, and 4) a control group. The results revealed that children could learn more English words in all experimental conditions compared to the control group. The three experimental conditions did not show any significant differences regarding the learning outcomes.

We present a large-scale study of a series of seven lessons designed to help young children learn English vocabulary as a foreign language using a social robot. The experiment was designed to investigate 1) the effectiveness of a social robot teaching children new words over the course of multiple interactions (supported by a tablet), 2) the added benefit of a robot's iconic gestures on word learning and retention, and 3) the effect of learning from a robot tutor accompanied by a tablet versus learning from a tablet application alone. For reasons of transparency, the research questions, hypotheses and methods were preregistered. With a sample size of 194 children, our study was statistically well-powered. Our findings demonstrate that children are able to acquire and retain English vocabulary words taught by a robot tutor to a similar extent as when they are taught by a tablet application. In addition, we found no beneficial effect of a robot's iconic gestures on learning gains.

We propose a new theoretical framework assuming that embodiment effects in HAI and HRI are mediated by users' perceptions of an artificial entity's body-related capabilities. To enable the application of our framework to foster more theoretical-driven research, we developed a new self-report measurement that assesses bodilyrelated perceptions of the embodiment and corporeality - which we reveal as not being a binary characteristic of artificial entities. For the development and validation of the new scale we conducted two surveys and one video-based experiment. Exploratory factor analysis reveal a four-factorial solution with good reliability (Study 2, n = 442), which was confirmed via confirmatory factor analysis (Study 3, n = 260). In addition, we present first insights into the explanatory power of the scale: We reveal that humans? perceptions of an artificial entity's capabilities vary between virtual and physical embodiments, and that the evaluation of the artificial counterpart can be explained through the perceived capabilities. Practical applications and future research lines are discussed.