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.

Robots will eventually perform norm-regulated roles in society (e.g. caregiving), but how will people apply moral norms and judgments to robots? By answering such questions, researchers can inform engineering decisions while also probing the scope of moral cognition. In previous work, we compared people's moral judgments about human and robot agents' behavior in moral dilemmas. We found that robots, compared with humans, were more commonly expected to sacrifice one person for the good of many, and they were blamed more than humans when they refrained from that decision. Thus, people seem to have somewhat different normative expectations of robots than of humans. In the current project we analyzed in detail the justifications people provide for three types of moral judgments (permissibility, wrongness, and blame) of robot and human agents. We found that people's moral judgments of both agents relied on the same conceptual and justificatory foundation: consequences and prohibitions undergirded wrongness judgments; attributions of mental agency undergirded blame judgments. For researchers, this means that people extend moral cognition to nonhuman agents. For designers, this means that robots with credible cognitive capacities will be considered moral agents but perhaps regulated by different moral norms.

Moral norms play an essential role in regulating human interaction. With the growing sophistication and proliferation of robots, it is important to understand how ordinary people apply moral norms to robot agents and make moral judgments about their behavior. We report the first comparison of people's moral judgments (of permissibility, wrongness, and blame) about human and robot agents. Two online experiments (total N = 316) found that robots, compared with human agents, were more strongly expected to take an action that sacrifices one person for the good of many (a "utilitarian" choice), and they were blamed more than their human counterparts when they did not make that choice. Though the utilitarian sacrifice was generally seen as permissible for human agents, they were blamed more for choosing this option than for doing nothing. These results provide a first step toward a new field of Moral HRI, which is well placed to help guide the design of social robots.