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In a future where many robot assistants support human endeavors, interactions with multiple robots either simultaneously or sequentially will occur. This paper highlights an initial exploration into one type of sequential interaction, which we call "transfers'' between multiple service robots. We defined the act of transferring between service robots and further decomposed it into five stages. Our research was informed by a design workshop investigating usage of multiple service robots. We also identified open design and research questions on this topic. 

Many robot applications being explored involve robots leading humans during navigation. Developing effective robots for this task requires a way for robots to understand and model a human's following behavior. In this paper, we present results from a user study of how humans follow a guide robot in the halls of an office building. We then present a data-driven Markovian model of this following behavior, and demonstrate its generalizability across time interval and trajectory length. Finally, we integrate the model into a global planner and run a simulation experiment to investigate the benefits of coupled human-robot planning. Our results suggest that the proposed model effectively predicts how humans follow a robot, and that the coupled planner, while taking longer, leads the human significantly closer to the target position. 

People take to social media to share their thoughts, joys, and sorrows. A recent popular trend has been to support and mourn people and pets that have died as well as other objects that have suffered catastrophic damage. As several popular robots have been discontinued, including the Opportunity Rover, Jibo, and Kuri, we are interested in how language used to mourn these robots compares to that to mourn people, animals, and other objects. We performed a study in which we asked participants to categorize deidentified Twitter reactions as referencing the death of a person, an animal, a robot, or another object. Most reactions were labeled as being about humans, which suggests that people use similar language to describe feelings for animate and inanimate entities. We used a natural language toolkit to analyze language from a larger set of tweets. A majority of tweets about Opportunity included second-person ("you") and gendered third-person pronouns (she/he versus it), but terms like "R.I.P" were reserved almost exclusively for humans and animals. Our findings suggest that people verbally mourn robots similarly to living things, but reserve some language for people. 

Human-robot interactions that involve multiple robots are becoming common. It is crucial to understand how multiple robots should transfer information and transition users between them. To investigate this, we designed a 3 x 3 mixed design study in which participants took part in a navigation task. Participants interacted with a stationary robot who summoned a functional (not explicitly social) mobile robot to guide them. Each participant experienced the three types of robot-robot interaction: representative (the stationary robot spoke to the participant on behalf of the mobile robot), direct (the stationary robot delivered the request to the mobile robot in a straightforward manner), and social (the stationary robot delivered the request to the mobile robot in a social manner). Each participant witnessed only one type of robot-robot communication: silent (the robots covertly communicated), explicit (the robots acknowledged that they were communicating), or reciting (the stationary robot said the request aloud). Our results show that it is possible to instill socialness in and improve likability of a functional robot by having a social robot interact socially with it. We also found that covertly exchanging information is less desirable than reciting information aloud. 

The inevitable increase in real-world robot applications will, consequently, lead to more opportunities for robots to have observable failures. Although previous work has explored interaction during robot failure and discussed hypothetical danger, little is known about human reactions to actual robot behaviors involving property damage or bodily harm. An additional, largely unexplored complication is the possible influence of social characteristics in robot design. In this work, we sought to explore these issues through an in-person study with a real robot capable of inducing perceived property damage and personal harm. Participants observed a robot packing groceries and had opportunities to react to and assist the robot in multiple failure cases. Prior exposure to damage and threat failures decreased assistance rates from approximately 81% to 60%, with variations due to robot facial expressions and other factors. Qualitative data was then analyzed to identify interaction design needs and opportunities for failing robots.