This study investigates how different forms of operator embodiment (avatar robots, telepresence robots, and direct in-person interaction) affect people’s willingness and ability to approach and initiate social interactions with strangers in public spaces. Participants were tasked with distributing information to pedestrians in a shopping mall across the three embodiment conditions. We examined behavioral performance (e.g., frequency of approaches, hesitation time), self-reported anxiety, fear of talking to strangers, and post-interaction reflections. Results showed that participants using robot-mediated embodiment (avatar and telepresence robots) exhibited greater social engagement and reported less hesitation time, lower state anxiety, and reduced fear of talking to strangers compared to the in-person condition. Notably, the avatar robot condition was associated with a greater reduction in fear of talking to strangers than the telepresence condition. Our interview results suggest that the reduction may be attributed to the anonymity provided by the avatar, which decreases self-consciousness and social pressure. Additionally, many participants noted that pedestrians showed heightened interest in the robots, which made initiating interactions easier. Telepresence robots also offered psychological relief by removing the need for physical presence, although some participants were more self-aware due to their visible face and voice. While most participants reported feeling nervous in face-to-face interactions, some appreciated the physical freedom and focused delivery enabled by in-person engagement. These findings suggest that teleoperated robots can mitigate psychological barriers in socially demanding tasks, and that embodiment plays a role in shaping operator experience and social outcomes.

We define authoritative presence as letting people experience authority through human-made technology in sensory or non-sensory ways. Our goal is to design a robot that creates the impression of possessing capabilities worthy of respect as a source of authority, thereby enhancing compliance without attributing that authority to external sources, such as a specific person or organization. We hypothesized that strategies commonly used in the Wizard-of-Oz method could help manipulate authoritative presence as it strives to ensure that the robot is not perceived as having additional abilities beyond those introduced by the manipulations. Wizards typically need to maintain the robot’s functionality and abilities at an appropriate level to minimize unwanted influence on participants’ perceptions and interactions. By interviewing HRI researchers who have wizarded, we summarized their usual strategies and implemented the opposite behaviors in a robot to investigate if this would contribute to authoritative presence. Based on the findings, we designed four behaviors that include (1) let the robot have an open-ended conversation with people, (2) randomize the robot’s reaction delay timing, (3) let the robot move with inconsistent velocity, and (4) let the robot perceive people’s status without looking at them. To evaluate the impact of these behaviors, we conducted a video-based online experiment with 942 participants, using a between-subjects design. The experiment aimed to determine whether the behaviors conveying authoritative presence would make people perceive the robot as having more authority and increase their likelihood of complying with its requests. A mediation analysis indicated that despite a decrease in perceived authority, the imply authoritative presence condition had a positive effect on participant compliance. Our study formally introduces the concept of authoritative presence, providing a proof-of-concept for how robots can create authoritative presence through specific behaviors. This work lays the groundwork for future research on authority and robotics.

We explore if social strategies can be used more effectively by social or traditional interfaces to repair trust in shared autonomy robot teleoperation. Shared autonomy systems help robots and operators collaborate, but when trust in these systems is broken, it does not recover easily. Social human–robot interaction suggests that social robots can rebuild trust through social strategies. This study explores if these strategies can also be used by teleoperated robots and further compares if these social trust repair strategies are more effective for an anthropomorphized interface (more similar to social robots) or a traditional log and read-out style interface design when used with a non-social, teleoperated robot. We find that positive social perception of an interface can improve the effectiveness of social trust repair strategies, and that while social interfaces can repair trust better than traditional interfaces, poor social perception of that social interface may lead to worse trust overall. Our results suggest social interfaces for teleoperation robots may act as a way to leverage findings in social robotics in teleoperated robots, hold implications for social interfaces, and call for additional investigation into appropriate social behavior and design across software agents, social robots, and teleoperated robots.

We investigate two approaches to designing neurotic behavior – the inability to deal with a stressful situation – and if such behavior can encourage more care shown towards a domestic robot. Robots are generally designed to be polite and selfless when working in daily situations, striving to be prosocial whenever possible. While negative behaviors such as neuroticism may initially seem undesirable, they serve a purpose in human-human interaction and may still be usable by robots in a positive way, such as communicating frustration, doubt, or anger. In our experiment, participants do an unrelated task as a Roomba cleans the room. In a 2x2 (valence and escalation) experiment on neurotic behavior design, the robot collides with objects while the participant is doing their task and displays a positive or negative valanced sound, and escalates (getting quicker or longer) or does not escalate that behavior. Our results showcase that designing neurotic behaviors is not simple, with escalation being a more effective control of perceived neuroticism, and that robot personality can indeed influence people, unprompted, to check on and interact with the robot. There was also no qualitative indication that this was perceived negatively by any of our participants. Our results demonstrate how neurotic behavior, stereotypically undesirable, can be useful in promoting positive interaction and prompts further investigation into non prosocial robot behaviors.

We investigate how gender stereotypes in authority influence the perceptions and behavior of avatar robots operators and their interlocutors. Gender stereotypes, which typically place men in more authoritative positions than women, are present in not only inter-human but also human-robot interaction. As avatar robots become more integrated into our lives and serve for diverse usages, they may be utilized in positions where they require authority. We study how avatar robot gender and operator gender affect expressions and perception of gender stereotypes in a customer service scenario with 41 pairs of participants. Operators controlled binary gendered avatar robots one at a time, acting as shopkeepers that had to assert authority over customers behaving improperly. The operators perceived their authority to be higher with male avatar robots compared to female ones, regardless of operator gender. We did not detect an effect on customer's perception of the shopkeeper's authority. While less than half of operators and customers perceived authority for reasons related to traditional gender stereotypes, others observed behaviors that did not align with stereotypes. Avatar embodiment may also help operators assert authority safely due to being physically hidden from the customers.

Most social robot behaviors in human-robot interaction are designed to be polite, but there is little research about how or when a robot could be impolite, and if that may ever be beneficial. We explore the potential benefits and tradeoffs of different politeness levels for human-robot interaction in an exercise context. We designed impolite and polite phrases for a robot exercise trainer and conducted a 24-person experiment where people squat in front of the robot as it uses (im)polite phrases to encourage them. We found participants exercised harder and felt competitive with the impolite robot, while the polite robot was found to be friendly, but sometimes uncompelling and disingenuous. Our work provides evidence that human-robot interaction should continue to aim for more nuanced and complex models of communication.

Robots are increasingly becoming present in workplaces and social scenarios, where people are interacting and completing tasks with them. In this paper, we explore the importance of the physical seating arrangement or placement of the robot in relation to the human. We designed a novel collaborative human-robot task for exploring how human-robot seating arrangement may influence interaction and the person's perceptions of a robot, and present the results from a pilot and two formal experiments (total 72 participants). Drawing from proxemics literature, we compared a person sitting next to a robot to being across a table from it or beside it, with a corner between them. Our results highlight a range of impacts on participant attitudes toward the robot, as well as their behavior and interaction with it. In particular, seating arrangement impacted participant preferences for the robot, and their use of aggression or condescension during disagreements with it. Our results highlight the importance of seating arrangements for collaborative human-robot teams.

We investigate if an on-screen agent that reacts to a teleoperator's driving performance (e.g., by showing fear during poor driving) can influence teleoperation. Serving as a kind of virtual passenger, we explore if and how this agent's reactions may impact teleoperation. Our design concept is to create an emotional response in the operator (e.g., to feel bad for the agent), with the ultimate goal of shaping driving behavior (e.g., to slow down to calm the agent). We designed and implemented two proof-of-concept agent personas that react differently to operator driving. By conducting an initial proof-of-concept study comparing our agents to a base case, we were able to observe the impact of our agent personas on operator experience, perception of the robot, and driving behavior. While our results failed to find compelling evidence of changed teleoperator behavior, we did demonstrate that emotional on-screen agents can alter teleoperator emotion. Our initial results support the plausibility of passenger agents for impacting teleoperation, and highlight potential for more targeted, ongoing work in applying social techniques to teleoperation interfaces.

Priming is the influence of external stimuli on a person's behavior and thoughts, where the effect is related to some quality of that stimuli. In this work, we explore three methods of priming teleoperator's expectations about robot capability and investigate how this may impact driving behavior and perceptions of the robot. We tested priming impressions of robot ability by the stiffness of the robot controller, additionally describing the changes in stiffness verbally to operators and simply describing the robots' abilities on paper and verbally. Our results found that all priming methods affected the perception of the robot including its speed, weight, or overall safety. Further, we confirmed that some priming methods lowered operator collisions by over 40%. Thus, interface and product designers should consider priming as a tool to leverage to improve operator performance and perception of the robot.

We investigate robots using infrasound, low-frequency vibrational energy at or near the human hearing threshold, as an interaction tool for working with people. Research in psychology suggests that the presence of infrasound can impact a person's emotional state and mood, even when the person is not acutely aware of the infrasound. Although often not noticed, infrasound is commonly present in many situations including factories, airports, or near motor vehicles. Further, a robot itself can produce infrasound. Thus, we examine if infrasound may impact how people interpret a robot's social communication: if the presence of infrasound makes a robot seem more or less happy, energetic, etc., as a result of impacting a person's mood. We present the results from a series of experiments that investigate how people rate a social robot's emotionally-charged gestures, and how varied levels and sources of infrasound impact these ratings. Our results show that infrasound does have a psychological effect on the person's perception of the robot's behaviors, supporting this as a technique that a robot can use as part of its interaction design toolkit. We further provide a comparison of infrasound generation methods.

Perceptions of a technology can shape the way the technology is used and adopted. Thus, in teleoperation, it is important to understand how a teleoperator's perceptions of a robot can be shaped, and whether those perceptions can impact how people drive robots. Priming, evoking activity in a person by exposing them to learned stimuli, is one way of shaping someone's perception. We investigate priming an operator's impression of a robot's physical capabilities in order to impact their perception of the robot and teleoperation behavior; that is, we examine if we can change operator driving behavior simply by making them believe that a robot is dangerous or safe, fast or slow, etc., without actually changing robot capability. Our results show that priming (with no change to robot behavior or capability) can impact operator perception of the robot, their teleoperation experience, and in some cases may impact teleoperation performance.

We designed and evaluated a series of teleoperation interface techniques that aim to draw operator attention while mitigating negative effects of interruption. Monitoring live teleoperation video feeds, for example to search for survivors in search and rescue, can be cognitively taxing, particularly for operators driving multiple robots or monitoring multiple cameras. To reduce workload, emerging computer vision techniques can automatically identify and indicate (cue) salient points of potential interest for the operator. However, it is not clear how to cue such points to a preoccupied operator -- whether cues would be distracting and a hindrance to operators -- and how the design of the cue may impact operator cognitive load, attention drawn, and primary task performance. In this paper, we detail our iterative design process for creating a range of visual attention-grabbing cues that are grounded in psychological literature on human attention, and two formal evaluations that measure attention-grabbing capability and impact on operator performance. Our results show that visually cueing on-screen points of interest does not distract operators, that operators perform poorly without the cues, and detail how particular cue design parameters impact operator cognitive load and task performance. Specifically, full-screen cues can lower cognitive load, but can increase response time; animated cues may improve accuracy, but increase cognitive load. Finally, from this design process we provide tested, and theoretically grounded cues for attention drawing in teleoperation.

We investigated the effects of changing the tele-operation feel of operating a robot by modifying its speed and acceleration profiles, and found that reducing a robot's maximum speed by half can reduce collisions by 32%, while only increasing navigation task time by 10%. Teleoperated robots are increasingly popular for enabling people to remotely attend meetings, explore dangerous areas, or view tourist destinations. As these robots are being designed to work in crowded areas with people, obstacles, or even unpredictable debris, interfaces that support piloting them in a safe and controlled manner are important for successful teleoperation. We investigate modifying a teleoperated robot's speed and acceleration profiles on an operator remotely navigating through an obstacle course. Our results indicate that lower maximum speeds result in lower operator workload, fewer collisions, and are only slightly slower than other profiles with a higher maximum speed. Our results raise questions about how robot designers should think about physical robot capability design and default driving software settings, the robot control interface, and the relation of robot speed to control.

Robot teleoperation, such as for search and rescue, uses multiple specialized cameras (e.g., wide environmental and sharp narrow views) to aid in task awareness. Simple display techniques, such as tiling, require ongoing mental mapping between the views; cameras that pan or tilt exacerbate the problem as the inter-view relationship changes. The detail-in-context technique bypasses this mental mapping requirement by providing a single integrated feed showing all cameras, with detail overlaid within the context. However, how this can be adapted to for robot teleoperation with multiple pan-and-tilt cameras has not yet been demonstrated. We present Monocle, an interactive detail-in-context teleoperation interface that integrates a pan-and-tilt narrow-angle first-person view into a wide-angle behind-robot view; operators can move the Monocle around a scene to obtain more resolution when and where needed. Evaluation results demonstrate Monocle's feasibility and show that it can help operators complete search and rescue tasks more effectively in comparison to simple solutions.

We present the results of a pilot study that investigates if and how people judge the trustworthiness of a robot during social Human-Robot Interaction (sHRI). Current research in sHRI has observed that people tend to interact with robots socially. However, results from neuroscience suggests people use different cognitive mechanisms interacting with robots than they do with humans, leading to a debate about whether people truly perceive robots as social entities. Our paper focuses on one aspect of this debate, by examining trustworthiness between people and robots using behavioral economics' ‘Trust Game’ scenario. Our pilot study replicates a trust game scenario, where a person invests money with a robot trustee in hopes they will receive a larger sum (trusting the robot to give more back), then gets a chance to invest once more. Our qualitative analysis of investing behavior and interviews with participants suggests that people may follow a human-robot (h-r) trust model that is quite similar to the human-human trust model. Our results also suggest a possible resolution to the sHRI and Neuroscience debate: people try to interact socially with robots, but due to lack of common social cues, they draw from social experience, or create new experiences by actively exploring the robot behavior.

We are interested in the social implications of working alongside robots. In this paper we look at a humanoid robot quality inspector, acting alongside workers in an assembly line. This setting is viable in small scale assembly lines where human assembly workers provide flexible, rapid assembly. A robotic quality inspector could enhance the quality assurance process, but places the robot in a position of relative seniority to the assembly workers. We present the results of an initial in-lab pilot study designed with our industry collaborators. In our pilot, a humanoid robot visually inspected participants' assembled products in a shared workspace and provided critiques that follow simple models of robotic social feedback. Our findings suggest that people's opinions of the robot (trust, impression of intelligence, etc.) changed based on the robot's social behaviors while it is judging the participant's work. Additionally, people rated the robot more negatively if they disagreed with the robot's opinions of their work, regardless of the robot social behavior and the value of its critique.

This work proposes the concept of shared presence, where we enable a user to "become" a co-located humanoid robot while still being able to use their real body to complete tasks. The user controls the robot and sees with its vision and sensors, while still maintaining awareness and use of their real body for tasks other than controlling the robot. This shared presence can be used to accomplish tasks that are difficult for one person alone, for example, a robot manipulating a circuit board for easier soldering by the user, lifting and manipulating heavy or unwieldy objects together, or generally having the robot conduct and complete secondary tasks while the user focuses on the primary tasks. If people are able to overcome the cognitive difficulty of maintaining presence for both themselves and a nearby remote entity, tasks that typically require the use of two people could simply require one person assisted by a humanoid robot that they control. In this work, we explore some of the challenges of creating such a system, propose research questions for shared presence, and present our initial implementation that can enable shared presence. We believe shared presence opens up a new research direction that can be applied to many fields, including manufacturing, home-assistant robotics, and education.

The creation of interactive computer-controlled characters in interactive media is a challenging and multi-faceted task requiring the skills and effort of professionals from many fields. This work addresses authoring the interactive aspect of these characters' behaviors -- how characters act automatically in response to a dynamic user-controlled character. We present PaintBoard, a system that enables users to prototype and test discrete, real-time, interactive movements in a 2D grid environment simply by digitally painting a storyboard. We designed and developed a novel authoring technique for creating behaviors (painting storyboards) and a novel algorithm based on machine-learning, that analyzes a storyboard to create a behavior that works beyond situations provided in the input storyboard. We conducted two exploratory studies that grounded the prototype design, and present the results of a proof-of-concept workshop with game developers. Finally, we performed a comparison of machine learning algorithms' performance on our storyboard data.

Applying visual analytics to large software systems can help users comprehend the wealth of information produced by source repository mining. One concept of interest is the co-evolution of test code with source code, or how source and test files develop together over time. For example, understanding how the testing pace compares to the development pace can help test managers gauge the effectiveness of their testing strategy. A useful concept that has yet to be effectively incorporated into a co-evolution visualization is co-change. Co-change is a quantity that identifies correlations between software artifacts, and we propose using this to organize our visualization in order to enrich the analysis of co-evolution. In this paper, we create, implement, and study an interactive visual analytics tool that displays source and test file changes over time (co-evolution) while grouping files that change together (co-change). Our new technique improves the analyst's ability to infer information about the software development process and its relationship to testing. We discuss the development of our system and the results of a small pilot study with three participants. Our findings show that our visualization can lead to inferences that are not easily made using other techniques alone.

We present a robot augmented with an ambient display that communicates using a multi-color halo. We use this robot in a public café-style setting where people vote on which colors the robot will display: we ask people to select a color which "best represents their mood". People can vote from a mobile device (e.g., smart phone or laptop) through a web interface. Thus, the robot's display is an abstract aggregate of the current mood of the room. Our research investigates how a robot with an ambient display may integrate into a space. For example, how will the robot alter how people use or perceive the environment, or how people will interact with the robot itself? In this paper we describe our initial prototype, an iRobot Roomba augmented with lights, and highlight the research questions driving our exploration, including initial study design.