The presentation of a robot's capability and identity directly influences a human collaborator's perception and implicit trust in the robot. Unlike humans, a physical robot can simultaneously present different identities and have them reside and control different parts of the robot. This paper presents a novel study that investigates how users perceive a robot where different robot control domains (head and gripper) are presented as independent robots. We conducted a mixed design study where participants experienced one of three presentations: a single robot, two agents with shared full control (co-embodiment), or two agents with split control across robot control domains (split-embodiment). Participants underwent three distinct tasks -- a mundane data entry task where the robot provides motivational support, an individual sorting task with isolated robot failures, and a collaborative arrangement task where the robot causes a failure that directly affects the human participant. Participants perceived the robot as residing in the different control domains and were able to associate robot failure with different identities. This work signals how future robots can leverage different embodiment configurations to obtain the benefit of multiple robots within a single body.

With more robots being deployed in the world, users will likely interact with multiple robots sequentially when receiving services. In this paper, we describe an exploratory field study in which unsuspecting participants experienced a "person transfer" – a scenario in which they first interacted with one stationary robot before another mobile robot joined to complete the interaction. In our 7-hour study spanning 4 days, we recorded 18 instances of person transfers with 40+ individuals. We also interviewed 11 participants after the interaction to further understand their experience. We used the recorded video and interview data to extract interesting insights about in-the-field sequential human-robot interaction, such as mobile robot handovers, trust in person transfer, and the importance of the robots’ positions. Our findings expose pitfalls and present important factors to consider when designing sequential human-robot interaction.

Close-proximity human-robot interactions can be improved through the optimization of task-centric factors or by prioritizing the user experience. Prior work has often explored these factors individually. In this paper, we conducted a within-subject study with 18 participants that compared a multi-objective robot motion adaptation method (CoMOTO) against methods that optimize distance from the user (UserAvoidant) or task performance (ShortestPath) in a close-proximity human-robot interaction task. In the task, the robot and participants worked on different tasks in an overlapping workspace. We show that while CoMOTO trajectories took a longer time, they caused significantly fewer interruptions compared to ShortestPath and generated shorter trajectories than UserAvoidant. CoMOTO was also perceived as significantly more intelligent, more trustworthy, and preferred by an overwhelming majority of the participants.

In this paper, we explored how a mobile robot should join an existing multimodal interaction between a person and a stationary robot. We developed three different strategies (Circular, Line, and Improper) and an interactive system to put the strategies in practice. Circular (all interactants stand in a circle) and Line (two robots stand in a line facing a person) were based on existing human group spatial arrangements, whereas Improper (second robot stands far from the person and stationary robot) explored how a bad arrangement might change a user’s position and perception. We also investigated how scenarios with different tasks influenced human positions at different stages of the interaction. We conducted a 3x4 mixed design, in-person user study and found that participants in the Improper condition repositioned themselves to decrease the distance differences between interactants. We also conducted an exploratory analysis on the spatial data to better understand how user actions and social cues, such as gaze and vocalizations, changed spatial behaviors.

Mobile robots have been shown to be helpful in guiding users in complex indoor spaces. While these robots can assist all types of users, current implementations often rely on users visually rendezvousing with the robot, which may be a challenge for people with visual impairments. This paper describes a proof of concept for a robotic system that addresses this kind of short-range rendezvous for users with visual impairments. We propose to use a lattice graph-based Anytime Repairing A* (ARA*) planner as a global planner to discourage the robot from turning in place at its goal position, making its path more human-like and safer. We also interviewed an Orientation & Mobility (O&M) Specialist for their thoughts on our planner. They observed that our planner produces less obtrusive trajectories to the user than the ROS default global planner and recommended that our system should allow the robot to approach the person from the side as opposed to the front as it currently does. In the future, we plan to test our system with users in-person to better validate our assumptions and find additional pain points.

In the not-so-distant future, people in service experiences are likely to interact with more than a single intelligent system, often sequentially, including different robots and devices. However, there has been sparse work exploring the characteristics of transferring people from one intelligent system to another. This paper aims to create a context-independent taxonomy to differentiate and categorize the transfer of users across robots, devices, and human staff in service interactions. We conducted two sets of design workshops where participants generated scenarios of human-multi-robot interactions and existing person transfers. Using the outcomes of both workshops, we analyzed scenarios and constructed a taxonomy for person transfers with 4-dimensions: Rationale, Type, Design, and Information Shared. We showcase different ways to utilize the taxonomy, and, through it, we discuss the trade-offs and design considerations in the implementation of person transfers.

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.

Interactions with multiple conversational agents and social robots are becoming increasingly common. This raises new design challenges: Should agents and robots be modeled after humans, presenting their entity (i.e., social presence) as bound to a single body, or should they take advantage of non-human capabilities, such as moving their social presence from body to body across service touchpoints and contexts? We conducted a User Enactments study in which participants interacted with agents that had one social presence per body, that could re-embody (move their social presence from body to body), and that could co-embody (move their social presence into a body that already contains another). Reactions showed that participants felt comfortable with re-embodying agents, who created more seamless and efficient experiences. Yet situations that required expertise or concentration raised concerns about non-human behaviors. We report on our insights regarding collaboration and coordination with several agents in multi-step interactions.

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.

We describe an exploration of how kiosk-type stationary robots might provide navigation instructions for blind people. Inspired by a technique used by Orientation & Mobility experts in which a route is traced out on a person's palm, we developed five methods that supplement verbal instructions with physical movements. We explored the usability, strengths, and limitations of each of our methods in two exploratory studies with blind participants. One method, in which the robot used its entire arm to create path gestures while participants held its gripper, was preferred by 5 out of 8 blind participants and performed comparably on a recall task as a verbal-only instruction method. A closer approximation of the original palm method failed. We analyzed interview data to understand the reasons behind the failures and successes. We discuss the lessons learned from our studies about instruction methods, how robots in public settings can be useful for blind people, and the challenges of deploying such systems in public.

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 × 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.

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.

We explored whether a robot can leverage social influences to motivate nearby bystanders to intervene and defend them from human abuse. We designed a between-subjects study where 48 participants took part in a memorization task and observed a confederate mistreating a robot both verbally and physically. The robot was either empathetic towards the participant»s performance in the task or indifferent. When the robot was mistreated, it ignored the abuse, shut down in response to it, or reacted emotionally. We found that the majority of the participants intervened to help the robot after it was abused. Interventions happened for a wide range of reasons. Interestingly, the empathetic robot increased the proportion of participants that self-reported intervening in comparison to the indifferent robot, but more participants moved the robot as a response to abuse in the latter case. The participants also perceived the robot being verbally mistreated more and reported higher levels of personal distress when the robot briefly shut down after abuse in comparison to when it reacted emotionally or did not react at all.

We demonstrate a system of adding an additional human-robot communication channel using a spherical robot through kinesthetic haptic signals. While not designed for this type of interaction, we show that the robot could be used to generate kinesthetic haptic signals through asymmetric rotations. These rotations generate pseudo-rotations that are detectable in a user's palm and can convey both rotational and directional information with proper training. We present two user studies that explore potential types of distinguishable haptic signals. While some participants were able to rapidly identify the different signals, other participants expressed the need for more training. We also describe use cases we believe would benefit from such interaction and general guidelines for spherical haptic devices.

Gaze aversion-the intentional redirection away from the face of an interlocutor-is an important nonverbal cue that serves a number of conversational functions, including signaling cognitive effort, regulating a conversation's intimacy level, and managing the conversational floor. In prior work, we developed a model of how gaze aversions are employed in conversation to perform these functions. In this paper, we extend the model to apply to conversational robots, enabling them to achieve some of these functions in conversations with people. We present a system that addresses the challenges of adapting human gaze aversion movements to a robot with very different affordances, such as a lack of articulated eyes. This system, implemented on the NAO platform, autonomously generates and combines three distinct types of robot head movements with different purposes: face-tracking movements to engage in mutual gaze, idle head motion to increase lifelikeness, and purposeful gaze aversions to achieve conversational functions. The results of a human-robot interaction study with 30 participants show that gaze aversions implemented with our approach are perceived as intentional, and robots can use gaze aversions to appear more thoughtful and effectively manage the conversational floor.