Recent advancements in robot capabilities have enabled them to interact with people in various human-social environments (HSEs). In many of these environments, the perception of the robot often depends on its capabilities, e.g., task competency, language fluency, etc. To enable fluent human-robot interaction (HRI) in HSEs, it is crucial to understand the impact of these capabilities on the perception of the robot. Although many works have investigated the effects of various robot capabilities on the robot’s perception separately, in this paper, we present a large-scale HRI study (n = 60) to investigate the combined impact of both language fluency and task competency on the perception of a robot. The results suggest that while language fluency may play a more significant role than task competency in the perception of the verbal competency of a robot, both language fluency and task competency contribute to the perception of the intelligence and reliability of the robot. The results also indicate that task competency may play a more significant role than language fluency in the perception of meeting expectations and being a good teammate. The findings of this study highlight the relationship between language fluency and task competency in the context of social HRI and will enable the development of more intelligent robots in the future.

Social robots are being deployed to interact with people in various scenarios, where they are expected to in-corporate human-like conversational strategies to achieve flu-ency in interactions. For example, current robots are designed to perform advanced communication strategies (i.e., personal anecdotes, explanations, and apologies) to recover from task failure. However, these tactics are not always sufficient for failure recovery as they can be lengthy and insufficient for encouraging future interactions. In human-human interactions, people often use humor as a low-risk and engaging method for managing failures. Thus, the successful execution of advanced, human-like humor could enable robots to recover from task failures more efficiently. In this paper, we present a human-robot interaction study exploring how a robot's utilization of various human-like humor types (i.e., affiliative, aggressive, self-enhancing, and self-defeating) are perceived by human teammate (n = 32) and an external observer of the interaction (n = 256). Additionally, we have explored the effects of performance, humor type, perspective, and previous experience with robots on the participants' perceptions of warmth, competence, and the robot as a teammate. Our results indicate that dyadic participants rated the successful robot to be more competent and a better teammate than the bystander participants. Additionally, the results indicate that participants with less experience with robots found the successful robot to be more competent than participants with high levels of experience. These findings will enable the human-robot interaction community to develop more engaging robots for fluent interactive experiences in the future.