In Human–Robot Interaction, researchers typically utilize in-person studies to collect subjective perceptions of a robot. In addition, videos of interactions and interactive simulations (where participants control an avatar that interacts with a robot in a virtual world) have been used to quickly collect human feedback at scale. How would human perceptions of robots compare between these methodologies? To investigate this question, we conducted a 2 \({\times}\) 2 between-subjects study ( N \({=}\) 160), which evaluated the effect of the interaction environment (Real vs. Simulated environment) and participants’ interactivity during human-robot encounters (Interactive participation vs. Video observations) on perceptions about a robot (competence, discomfort, social presentation, and social information processing) for the task of navigating in concert with people. We also studied participants’ workload across the experimental conditions. Our results revealed a significant difference in the perceptions of the robot between the real environment and the simulated environment. Furthermore, our results showed differences in human perceptions when people watched a video of an encounter versus taking part in the encounter. Finally, we found that simulated interactions and videos of the simulated encounter resulted in a higher workload than real-world encounters and videos thereof. Our results suggest that findings from video and simulation methodologies may not always translate to real-world human–robot interactions. In order to allow practitioners to leverage learnings from this study and future researchers to expand our knowledge in this area, we provide guidelines for weighing the tradeoffs between different methodologies.

Despite the clear benefits that social connection offers to well-being, strangers in close physical proximity regularly ignore each other due to their tendency to underestimate the positive consequences of social connection. In a between-subjects study (N = 49 pairs, 98 participants), we investigated the effectiveness of a humanoid robot, a computer screen, and a poster at stimulating meaningful, face-to-face conversations between two strangers by posing progressively deeper questions. We found that the humanoid robot facilitator was able to elicit the greatest compliance with the deep conversation questions. Additionally, participants in conversations facilitated by either the humanoid robot or the computer screen reported greater happiness and connection to their conversation partner than those in conversations facilitated by a poster. These results suggest that technology-enabled conversation facilitators can be useful in breaking the ice between strangers, ultimately helping them develop closer connections through face-to-face conversations and thereby enhance their overall well-being.

We provide guidelines for planning, executing, analyzing, and reporting hypothesis-driven experiments in Human–Robot Interaction (HRI). The intended audience are researchers in the field of HRI who are not trained in empirical research but who are interested in conducting rigorous human-participant studies to support their research. Following the chronological order of research activities and grounded in updated research practices in psychological and behavioral sciences, this primer covers recommended methods and common pitfalls for defining research questions, identifying constructs and hypotheses, choosing appropriate study designs, operationalizing constructs as variables, planning and executing studies, sampling, choosing statistical tools for data analysis, and reporting results.

Anthropomorphic robots, or robots with human-like appearance features such as eyes, hands, or faces, have drawn considerable attention in recent years. To date, what makes a robot appear human-like has been driven by designers» and researchers» intuitions, because a systematic understanding of the range, variety, and relationships among constituent features of anthropomorphic robots is lacking. To fill this gap, we introduce the ABOT (Anthropomorphic roBOT) Database---a collection of 200 images of real-world robots with one or more human-like appearance features (http://www.abotdatabase.info). Harnessing this database, Study 1 uncovered four distinct appearance dimensions (i.e., bundles of features) that characterize a wide spectrum of anthropomorphic robots and Study 2 identified the dimensions and specific features that were most predictive of robots» perceived human-likeness. With data from both studies, we then created an online estimation tool to help researchers predict how human-like a new robot will be perceived given the presence of various appearance features. The present research sheds new light on what makes a robot look human, and makes publicly accessible a powerful new tool for future research on robots» human-likeness.

Visual perspective taking plays a fundamental role in both human-human interaction and human-robot interaction (HRI). In three experiments, we took a novel approach to the topic of visual perspective taking in HRI, examining whether, and under what conditions, people spontaneously take a robot's visual perspective. Using two different robot models, we found that specific behaviors performed by a robot-namely, object-directed gaze and goal-directed reaching-led many human viewers to take the robot's visual perspective, though slightly fewer than when the same behaviors were performed by a person. However, we found no difference in people's perspective-taking tendency toward robots that differed in their human-likeness. Also, reaching became an especially effective perspective-taking trigger when it was displayed in a video rather than in a photograph. Taken together, these findings suggest that certain nonverbal behaviors in robots are sufficient to trigger the mechanism of mental state attribution-visual perspective taking in particular-in human observers. Therefore, people's spontaneous perspective-taking tendencies should be taken into account when designing intuitive and effective human-centered robots.