Mobile robots carrying food in restaurants are here. What service behavior norms do people expect them to follow? This paper evaluates robot waiter algorithms and service parameters for a robot serving two participants at a simulated cocktail event, varying body-storming inspired context variables such as: "hunger level" and "relationship to each other," robot delivery algorithms (lead, follow, ambient), and participant pose (standing, seated). In the within-subjects design, pairs of people were given a series of context prompts, and told to participate as felt natural. Output variables included whether they took food and post-trial survey ratings of the robot. The results show a positive correlation between food taking (or feelings of obligation to take food) and human OR robot initiative, relative to a mixed-ambient algorithm with no explicit leader. The robot waiter that initiates is the clearest and most noticeable. There were also some challenges: people in conversation would sometimes forget or delay calls for cupcakes, ambient robot motion was hardest to notice, and bringing food one person ordered to the other was unforgivable. When in doubt, go to the middle. Finally, participants enjoyed the robot spinning, describing it as a dessert tray which attracted their eyes to the robot.

In this work, a controlled user study explores four Cartesian spatial trajectories (high, low, direct, and from the side arcs), with three unique objects (flower, dagger, water). In mathematics, the Cartesian planes of horizontal, vertical, and forward/back are widely used for path-planning, much like any animal seeking to traverse the surface of the earth. Gravity naturally pulls objects down, and extra force must be exerted to push them upwards. In Laban notation, a dance annotation language, actors and dancers are similarly taught to think of table, door, and wheel planes (direct Cartesian correlates). What is the impact of these cardinal directions on interpretation of robot handovers? In these results, we find that direct paths, water, and flowers are generally seen positively, with daggers and high paths as least friendly. There is also a statistically significant interaction between path and object on robot predictability ratings. In terms of nuance, we also find that elegance is best predicted by path shape. These findings expand prior features of mobile robot expression to arcing planes, finding the up/down degree of freedom to significantly impact human interpretation and experience. Future work can continue such investigations grounded in real world tasks such as assistive robotics (passing a blanket or tissue), or service (retrieving a ticket for a bus).

Spatial formations can give many social cues, such as illustrating a group of people are having a conversation (social affiliation), or that they are trying to move swiftly through a space (functional goal). This work explored how people perceive varied robots formations while navigating through a space and approaching people. Evaluation occurred across four different geometric formations: wedge, v-shape, vertical line, and horizontal line (Fig 3). Two studies were conducted: the first being an exploratory study of three robots navigating through a public space, and the second being a controlled user study of the same robots approaching humans in different formations. Results showed that triangle shapes were generally received more positively than lines, with wedge being the viewed as harmless, polite, welcoming, and encouraging the human to join the robot group, whereas horizontal line was seen as threatening and unwelcoming. From a path planning perspective, v-shape and wedge were also more robust to controller variance. Results from this work show that formation impacts how people perceive robots, and as a result may impact task success. Future researchers can use these results to inform their behavior design for multi-robot groups to increase task success and desired communication effects.

In a world with increasing numbers of robots operating in everyday human spaces, the employees at this robotics company are pioneers, with intelligent point-to-point path planning and autonomous transport operations in 150+ factory and warehouse locations in North America. At the time of research, this robotics company consisted of 250 employees. Unlike other industry models, their robots are designed to operate with people in mixed human-machine spaces, yet no HRI style evaluations had previously been run with their robots. As early observers of how factory workers and transport robot interact, across varied job roles ranging from technology design to customer relations, this work sought to leverage employee knowledge and experiences to identify opportunities for improving the communication capabilities of the robots, resulting in the addition of several robot state communications to their initial software set leveraging both employee- and social robotics literature- sourced ideas for communicating with lights. To achieve this a social robotics researcher spent a summer onsite at the robotics company, getting to know their software stack and culture. Her research activities included: (1) a company-wide survey relative to the robot’s light, sound, and motion communications was sent out and analyzed, (2) the development of three new light sets (car-like, sweeping, heartbeat) and five overall states (blocked, at goal, turning, idle), and (3) a user study evaluating the developed light sets relative to the current robot default light patterns, all significantly improving the overall legibility of the targeted robot state communications: at goal, blocked, turning, and idle. Our initial findings advance knowledge in which style of light patterns is best for different communication states, showing that eye-catching lights are best for high urgency states, such as blocked, and subtle lights are best for low urgency states, such as idle. Finally, the latest software release for this robot has deployed a subset of these light patterns to all of their currently operating client sites, i.e., anyone who updates their robots to the latest release will benefit from these research results. This deployment sets the ground for future researchers exploring how end-users at different sites have responded to the new, more communicative light patterns.

Robot feeding is a new but growing application, with the work thus far mostly focusing on mechanical aspects and the ability to acquire food, rather than socially-inspired behaviors or human-centric evaluations. As a precursor to evaluating the ways in which robot arms could expressively offer food to a person in robot-feeding applications, this study explores socially-inspired variables and how they intersect with feeding tools for a distracted participant. The results illustrate the human impact of robot approach path in terms of likelihood to take the food and robot attributions. Specifically, this work evaluates participant reactions to a mobile robot robot approaching a human to offer food with varied expressive pathways and utensils, implementing six different approaches, each a combination of delivery tool and approach path (N=5, within subjects, one week data collection period). Future designers of robots that feed humans may be interested to know that our participants often attributed direct approaches with aggressiveness and indirect approaches with confusion, but found semi-direct approaches to be the most helpful, perhaps because they were seen as polite but intentionally clear. Moreover, utensil choice can emphasize the path's directionality and the robot's perceived aggressiveness. The results indicate that future work on robot arms would benefit from considering the social attributes of how the robot is perceived as, depending on the desired effect, certain approach styles and implements may be recommended over others.

Even as autonomous capabilities improve, many robot manipulation tasks require human(s)-in-the-loop to resolve high-level problems in uncertain environments or ambiguous situations. Prior work in highly autonomous applications tends to use interfaces with few human interface modalities, poten-tially missing out on the benefits that multimodal interfaces have demonstrated in lower-level operation. In this work, we demonstrate a system with a multimodal interface for a controlling a robot at a high level of autonomy. This example highlights how multiple modalities could enable redundant and robust interactions, increased situational awareness, and compact representations of complex commands, such as how to grasp an object.

This paper evaluates a robot that distributed hand-sanitizer over an eight month period (October 2020-June 2021) in public places on the Oregon State University campus. During COVID times, many robots have been deployed in public places as social distancing enforcers, food delivery robots, UV-sanitation robots and more, but few studies have assessed the social situations of these robots. Using the context of robot distributing hand sanitizer, this work explores the benefits that social robots may provide to encouraging healthy human activities, as well as ways in which street-performance inspired approaches and a bit of humor might improve the quality and experience of functional human-robot interactions. After gaining human-in-the-loop deployment experience with a customized interface to enable both planned and improvized responses to human bystanders, we run two sub-studies. In the first, we compare the performance of the robot (moving or still) relative to a traditional hand sanitizer dispenser stick ($\mathrm{N}=2048$, 3 week data collection period). In the second, we evaluate how varied utterance strategies further impact the interaction results ($\mathrm{N}=185$, 2 week data collection period). The robot dramatically outperforms the stick dispenser across all tracked behavioral variables, cuing high levels of positive social engagement. This work finds the utterance design is more complex socially, and offer insights to future robot designers about how to integrate helpful and playful speech into service robot interactions. Finally, across both sub-studies, the work shows that people in groups are more likely to engage with the robot and each other, as well as sanitize their hands.

Imbuing furniture with robot properties reduces the physical labor and time needed for arranging spaces, such as homes, classrooms, and offices. Outsourcing labor tasks to robot furniture requires users’ involvement with functional user interfaces. We performed a user study on multi-robot furniture and added additional features based on the study results. The study involved 12 participants rearranging multiple non-robotic and robotic chairs (ChairBots). Results from the video and interview analysis revealed five high-level features missing in the original ChairBot: dual screen-based user interface, the ability to save and to set arrangements, the ability to move in multi-robot formations, the ability to snap to angles/gridlines, and higher movement precision. The improved system allows users to control multiple furniture robots, both locally and remotely. Such improvement sets the baseline functionalities of robot furniture arrangement systems while extending the potential utilization of established robotic chairs.

This paper proposes a street-style study method to conduct human-robot interaction studies in-the-wild where the robots conduct their own experiments by recruiting their audience, conducting the study and gathering data. This street-style study method was implemented using multi-robot comedy as the platform deployed at an arts and technology festival to validate the concept. Twelve robot comedy shows occurred over seven hours with two robots on stage, who queried the audience during and at the end of each show. The multi-robot aspect enabled the robots to act out interactions relative each other, oneself and the audience. The final street-style study method evolved from pilots at a local farmer’s market, with hardware designed for portability and easy replication. The robots conducted their own human experiments in that they queried the audience after displaying experimentally balanced episodes of relational humor, with permutations of who was the ‘butt of the joke.’ Our study results explore the relational humor of the two robots and the audience, asking the audience to agree or disagree with particular perspectives. Delivered as part of the show, the robots invite the audience to vote via a show of hands. ANOVA analyses of the percent-agreement results find that (1) audiences were generally positive about all aspects of the show unless both robots were being negative, and (2) audiences were more ready to protect the robot comedian’s ego than their own, strongly supporting the statement that the robot was doing a good job.New Abstract

Robots sometimes face hardware and algorithmic challenges that exceed their capabilities, e.g., an armless robot pressing an elevator button. Previous work suggests that rather than augmenting the robot capabilities, sometimes robots can simply ask for help. A central contribution of this paper is the discovery of how people's helping behaviors vary within local microcultures, i.e., shared patterns of behaviors and norms linked to local atmospheric conditions and situations. Our methods combine techniques from both social robotics research and ethnography to investigate how people's helping behaviors toward robots vary across six cafes on a single college campus. We deploy a simple robot to request help ordering items, analyzing the 268 interaction instances to find significant variations in both help and care behaviors toward the robot. Microcultural and situational factors influence this help, motivating the inclusion of cultural criteria into the behavioral predictions of human-robot interaction systems.

This article is an ethnographic exploration of robot anthropomorphism at a robotics company. It draws on a 10-month participatory ethnography among a robotics company, an anthropologist, and a social robotics research lab. In contrast to psychological methods, this anthropological participatory ethnography integrates all stakeholders’ insights, offering holistic understandings of robots’ in situ operations throughout the fieldwork, data-sharing, interviews, and analysis. In particular, this article unravels employee social constructions of the company’s self-driving factory transport vehicles, “the robots.” These robots are deployed across a variety of warehouses and factories in North America. Our results involve an assessment of six teams at the robotics company’s headquarters: those testing robots, those developing their hardware and software, and those working with customers. We unpack trends of anthropomorphism for each of these teams and across the company.

The success of rising service robots will rely largely on their ability to persuade people to use their services. Simple scenarios in which a robot conveys information to a human could be enhanced given a deeper understanding of persuasion in the context of human robot interaction. These robots can further increase their utility with moving around and being responsive to people. Robot furniture is an upcoming area of social robotics where the furniture itself acts as the minimal social robot. These robots have already shown success in interacting via non-verbal behaviors, however, previous work has seldom considered the persuasive capability of their behavior except in [1] [2].

This paper explores peoples attitudes about a service robot using customer data in conversation. In particular, how can robots understand privacy expectations in social grey-areas like cafes, which are both open to the public and used for private meetings? To answer this question, we introduce the Theater Method, which allows a participant to experience a “violation” of their privacy rather than have their actual privacy be violated. Using Python to generate 288 scripts that fully explored our research variables, we ran a large-scale online study ( N=4608). To validate our results and ask more in-depth questions, we also ran an in-person follow-up ( N=20). The experiments explored social & data-inspired variables such as data source, the positive or negative use of that data, and whom the robot verbally addressed, all of which significantly predicted participants' social attitudes towards the robot's politeness, consideration, appropriateness, and respect of privacy. Body language analysis and cafe-related conversation were the lowest risk, but even more extreme data channels are potentially okay when used for positive purposes.

Over the summer of 2018, CHARISMA Robotics Laboratory at Oregon State University invited a Theater Artist to collaborate on two interdisciplinary robot theater productions using ChairBots and human performers. Both productions shared in a three-week development period, the same development team and performing robots, and culminated in live performances. This paper acts as a companion to the video documentation of these productions, addressing the novelty and contributions, both technical and creative, of dancing with robot furniture.

This paper consists of a failure analysis of two robot performance productions. Both productions included three-week rehearsal periods, and culminated in live performances including both robots and humans. To develop these productions, a theater artist collaborated with a robotics lab to develop, (1) a narrative dance performed live on stage and (2) an improvisational performance in a public space. While the interdisciplinary team did not set out to explore robot failures, failures played an ever-present role during the eighteen rehearsals and two live performances. This paper details strategies for addressing, planning for, and rehearsing responses to robot failures on stage, both technical and choreographic. In addition to scaffolding future robot theater performances, we discuss how these strategies apply to other customer- and audience-facing robots, including sponsor demos. The on-stage exploration of robot chairs and human performers also suggests that humans can conceptualize minimal robots as both characters and props, moving fluidly from one to the other. We hope these strategies ensure that future audiences will want the robot shows to go on, as well as expand ideas about the types of robots that can be cast in future human-robot productions.

Robot furniture is a growing area of robotics research, as people easily anthropomorphize these simple robots and they fit in easily to many human environments. Could they also be of service in recruiting people to play chess? Prior work has found motion gestures to aid in persuasion, but this work has mostly occurred in in-lab studies and has not yet been applied to robot furniture. This paper assessed the efficacy of four motion strategies in persuading passerbyers to participate in a ChairBot Chess Tournament, which consisted of a table with a chessboard and two ChairBots - one for the white team, and another for the black team. The study occurred over a six-week period, seeking passersby to play chess in the atrium of our Computer Science building for an hour each Friday. Forward-Back motion was the most effective strategy in getting people to come to the table and play chess, while Spinning was the worst. Overall, people found the ChairBots to be friendly and somewhat dog-like. In-the-wild studies are challenging, but produce data that is highly likely to be replicable in future versions of the system. The results also support the potential of future robots to recruit participants to activities that they might already enjoy.

Improvisational actors are adept at creative exploration within a set of boundaries. These boundaries come from each scene having “games” that establish the rules of-play. In this paper, we introduce a game that allows an expressive motion robot to collaboratively develop a narrative with an improviser. When testing this game on eight improv performers, our team explored two research questions: (1) Can a simple robot be a creative partner to a human improviser, and (2) Can improvisers expand our understanding of robot expressive motion? After conducting 16 scenes and 40 motion demonstrations, we found that performers viewed our robot as a supportive teammate who positively inspired the scene’s direction. The improvisers also provided insightful perspectives on robot motion, which led us to create a movement categorization scheme based on their various interpretations. We discuss our lessons learned, show the benefits of merging social robotics with improvisational theater, and hope this will encourage further exploration of this cross-disciplinary intersection.

How could a rearranging chair convince you to let it by? This paper explores how robotic chairs might negotiate passage in shared spaces with people, using motion as an expressive cue. The user study evaluates the efficacy of three gestures at convincing a busy participant to let it by. This within-participants study consisted of three subsequent trials, in which a person is completing a puzzle on a standing desk and a robotic chair approaches to squeeze by. The measure was whether participants moved out of the robot's way or not. People deferred to the robot in slightly less than half the trials as they were engaged in the activity. The main finding, however, is that over-communication cues more blocking behaviors, perhaps because it is annoying or because people want chairs to know their place (socially speaking). The Forward-Back gesture that was most effective at negotiating passage in the first trail was least effective in the second and third trial. The more subtle Pause and the slightly loud but less-aggressive Side-to-Side gesture, were much more likely to be deferred to in later trials, but not a single participant deferred to them in the first trial. The results demonstrate that the Forward-Back gesture was the clearest way to communicate the robot's intent, however, they also give evidence that there is a communicative trade-off between clarity and politeness, particularly when direct communication has an association with aggression. The takeaway for robot design is: be informative initially, but avoid over-communicating later.

Dancers are human Expressive Motion experts and could theoretically help robots communicate their state to people, e.g., rushed, confused, curious. The problem is twofold: first, dancers are trained in human-motion whereas many robots are non-anthropomorphic, and second, most dancers are not programmers. This is where the present interface is useful: the robot demos a batch of motions, in person, and the dancer, who knows expressive motion when she sees it, rates each path's success at communicating a particular state. Using an evolutionary algorithm, the interface - where feedback is recorded on the robot's screen and motion is demonstrated via the robot - calculates a new batch of motions that explore variations of the top-rated paths from the previous generation. This approach addresses the challenges of visualizing the expressive potential of non-anthropomorphic robots, while also ensuring path characteristics are reproducible via the robot's motion controller. The purpose of the interface is to help a non-expert negotiate a high-dimensional space of robot motion expression. Thus, it also has interactive functionality enabling users to freeze a feature value they like, or reset all features to begin again. To illustrate the system, this paper includes the results of two dancers designing motions for an omni-directional mobile robot, showing convergence with every generation. In reality, motion designers may have many authoring styles - exploring multiple solutions before honing in, or being satisfied easily versus getting each detail exactly right. By combining human-in-the-loop machine learning with direct authoring, we create a kinetic conversation between the robot and the dancer, and gain the ability to model knowledge from complementary fields.

Expressive motion refers to movements that help convey an agent's attitude towards its task or environment. People frequently use expressive motion to indicate internal states such as emotion, confidence, and engagement. Robots can also exhibit expressive motion, and studies have shown that people can legibly interpret such expressive motion. Mimicry involves imitating the behaviors of others, and has been shown to increase rapport between people. The research question addressed in this study is how robots mimicking the expressive motion of children affects their interaction with dancing robots. The paper presents our approach to generating and characterizing expressive motion, based on the Laban Efforts System and the results of the study, which provides both significant and suggestive evidence to support that such mimicry has positive effects on the children's behaviors.

This tutorial is a hands-on introduction to human-centered design topics and practices for human-robot interaction. It is intended for researchers with a variety of backgrounds, particularly those with little or no prior experience in design. In the morning, participants will learn about user needs and needfinding, as ways to understand the stakeholders in research outcomes, guide the selection of participants, and as possible measures of success. We then focus on design sketching, including ways to represent objects, people and their interactions through storyboards. Design sketching is not intended to be art, rather a way to develop and build upon ideas with oneself, and quickly communicate with colleagues. In the afternoon, participants will use the tools and materials, and learn techniques for lightweight physical prototyping and improvisation. Participants will build a small paper robot (not actuated) of their own design, to practice puppeteering, explore bodily movement and prototype interactions.

Functional robots are an increasing presence in shared human-machine environments. Humans efficiently parse motion expressions, gaining an immediate impression of an agent's current action and state. Past work has shown that motion can effectively reveal a robot's current task objective to bystanders and collaborators, however, the layering of expression on pre-existing robot task motions has yet to be explored. Rather than showing us what the robot is doing, these layered motion characteristics leverage the how of the task motions to convey additional robot attitudes, e.g., confidence, adherence to deadline or flexibility of attention. To lay the foundations for this objective, we adapt the Laban Efforts, a system from dance and acting training in use for over 50 years. We operationalize features representing the four Laban Efforts (Time, Space, Weight, and Flow) to the movements of a 2-DOF Nao head and a 4-DOF Keepon robot during simple dance and look-for-someone behaviors. Using online survey, we collect 1028 motion ratings for 72 robot motion videos depicting contrasting Effort motion examples. We achieve statistically significant legibility results for all four Effort implementations. Even without human degrees of freedom, we find that robot motion patterns can convey complex expressions to people.

Expressive motion can situate a robot's attitude in its task motions, illustrating real-time reactions. Inspired by acting movement training, we construct path shape features that layer expression into a mobile robot's motion traversal. Our video-study results show that simple variations of path shape and orientation can influence human perceptions of a robot's task, focus, and confidence. We further find that sequencing path features is a useful way to create expressions that are pinpointed in time without requiring changes in velocity. Our quantitative features represent the Laban Space Effort: using path shape and orientation along the path to communicate the direct or indirect attitude of the robot toward its target destination (acting vocabulary italicized). These features illustrate expressive or stylistic aspects of the robot's inner state, filling a gap in the pre-existing literature that has mostly focused on task legibility. Our future work will evaluate temporal and spatial robot motion features in explicit interaction contexts.

In our experiment, two autonomously moving costumed robots visit 256 offices during a `reverse' trick-or-treating task close to Halloween. Our behavioral data supports the idea that people interpret a robot's non-verbal cues, as the robots' costuming and baskets of candy seem to have communicated an implicit offer of candy. In fact, one third of our detection instances occurred during robot transit, i.e., while the robots were making no verbal offer. We find that candy accessibility dominates any social influence of robot orientation and that robot speed influences both whether people will interrupt a robot in transit (slow more interruptible) and whether they will respond to its verbal offer (fast more salient).

There is a saying that 95% of communication is body language, but few robot systems today make effective use of that ubiquitous channel. Motion is an essential area of social communication that will enable robots and people to collaborate naturally, develop rapport, and seamlessly share environments. The proposed work presents a principled set of motion features based on the Laban Effort system, a widespread and extensively tested acting ontology for the dynamics of “how” we enact motion. The features allow us to analyze and, in future work, generate expressive motion using position (x, y) and orientation (theta). We formulate representative features for each Effort and parameterize them on expressive motion sample trajectories collected from experts in robotics and theater. We then produce classifiers for different “manners” of moving and assess the quality of results by comparing them to the humans labeling the same set of paths on Amazon Mechanical Turk. Results indicate that the machine analysis (41.7% match between intended and classified manner) achieves similar accuracy overall compared to a human benchmark (41.2% match). We conclude that these motion features perform well for analyzing expression in low degree of freedom systems and could be used to help design more effectively expressive mobile robots.

In this paper we analyze joint attention between a robot that presents features of its surroundings and its human audience. In a statistical analysis of hand-coded video data, we find that the robot's physical indications lead to a greater attentional coherence between robot and humans than do its verbal indications.We also find that aspects of how the tour group participants look at robot-indicated objects, including when they look and how long they look, can provide statistically significant correlations with their self-reported engagement scores of the presentations. Higher engagement would suggest a greater degree of interest in, and attention to, the material presented. These findings will seed future gaze tracking systems that will enable robots to estimate listeners' state. By tracking audience gaze, our goal is to enable robots to cater the type of content and manner of its presentation to the preferences or educational goals of a particular crowd, e.g. in a tour guide, classroom or entertainment setting.

In this video, real-life acting professor Matthew Gray tutors Data the Robot (a Nao model) to improve his expression of emotion via Chekhov's Psychological Gestures. Though the video narrative is fictional and the robot actions pre-programmed, the aim of the dramatization is to introduce an acting methodology that social robots could use to leverage full body affect expressions.

As an early behavioral study of what non-verbal features a robot tourguide could use to analyze a crowd, personalize an interaction and/or maintain high levels of engagement, we analyze participant gaze statistics in response to a robot tour guide's deictic gestures. There were thirty-seven participants overall split into nine groups of three to five people each. In groups with the lowest engagement levels aggregate gaze responses in response to the robot deictic gesture involved the fewest total glance shifts, least time spent looking at indicated object and no intra-participant gaze. Our diverse participants had overlapping engagement ratings within their group, and we found that a robot that tracks group rather than individual analytics could capture less noisy and often stronger trends relating gaze features to self-reported engagement scores. Thus we have found indications that aggregate group analysis captures more salient and accurate assessments of overall humans-robot interactions, even with lower resolution features.

This paper describes the hardware and algorithms for a realtime social touch gesture recognition system. Early experiments involve a sensate bear test-rig with full body touch sensing, sensor visualization and gesture recognition capabilities. Algorithms are based on real humans interacting with a plush bear. In developing a preliminary gesture library with thirteen symbolic gestures and eight touch subtypes, we have taken the first steps toward a robotic touch API, showing that the huggable robot behavior system will be able to stream currently active sensors to detect regional social gestures and local sub-gestures in realtime. The system demonstrates the infrastructure to detect three types of touching: social touch, local touch, and sensor-level touch.