Programming social robots is challenging for novice robot programmers due to required expertise in planning, interaction design, and programming. While large language models (LLMs) hold significant promise through code generation from natural-language descriptions, they can obscure critical elements of programming and supplant designer intent, eventually resulting in over-reliance instead of developing programming skills. In this paper, we explore how LLM-based social-robot-programming tools can support novice robot programmers through a Research through Design (RtD) process. We designed and prototyped Robo-Blocks, a block-based programming environment that leverages LLMs to offer novice robot programmers generative scaffolding through structured narratives that connect high-level ideas to executable robot behaviors. Through deployment with novices, we discovered emerging user personas and usage patterns for generative scaffolding and showed how this scaffolding shapes end-user design and programming strategies. We present design insights for the effective use of generative scaffolding and its integration into the practice of social-robot programming.

Personalization is crucial for effective learning, yet online learning, designed for widespread availability and open access, lacks personalized guidance. Recent advancements in large language models (LLMs) offer opportunities to bridge this gap. We explore how LLM-driven tools may be designed to support personalized and adaptive learning and examine how they shape user experience and learning outcomes. We iteratively designed LearnMate2 to support online learning by providing personalized study plans, real-time contextual assistance, and adaptive learning activities. A preliminary study (n = 24) assessed the effectiveness and usability of LearnMate2 and informed refinements in our system, which we then evaluated (n = 16) against a combination of a state-of-the-art online learning platform and an LLM for learning support. Results indicate that LearnMate2 advances AI pedagogy by improving both learning outcomes and user experience compared to existing online learning and support tools. This work advances our understanding of the design space of personalized, AI-driven educational tools and their potential impact on user experience.

Educational robots are increasingly adopted to support children’s learning through interactive and personalized learning. Social interaction remains a crucial mechanism for effective learning, yet teacher and parental involvement in out-of-class learning activities is minimal or limited to supervisory roles. To support students with socially and intellectually meaningful learning experiences and to augment teachers’ pedagogical strategies outside of a classroom environment, we aim to explore the design of a learning companion robot by building a better understanding of the use cases for robot-assisted homework. In this paper, we report on findings from in-home technology probe studies with 10 students (aged 10–12), which revealed student expectations surrounding what support needs to be delivered by the robot and how it should be delivered. We discuss the themes of our findings and their implications for future design of social robots for homework assistance.

The sense of family connectedness may support positive outcomes including individual well-being, resilience, and healthy family functioning. However, as technologies advance, they often replace human-human interactions instead of nurturing them. In this work, we investigate how robot-facilitated communication tools might instead create new opportunities for family connection. We conducted two studies with families with children aged 5-12. We first explored the design space through in-home technology probe sessions with six families. These probes inspired us to explore two key interaction design dimensions: the robot’s behavior strategy (passive, reactive, proactive) and the mode of communication (synchronous, asynchronous). We then conducted a laboratory study with 20 families to examine how the two dimensions shaped parent-child interaction and connection. Our findings characterize how parents and children appropriated robot-mediated exchanges, the tensions they experienced around initiative, timing, and privacy, and the opportunities they envisioned for supporting everyday connectedness.

Group interactions are essential to social functioning, yet effective engagement relies on the ability to recognize and interpret visual cues, making such engagement a significant challenge for blind people. In this paper, we investigate how a mobile robot can support group interactions for blind people. We used the scenario of a guided tour with mixed-visual groups involving blind and sighted visitors. Based on insights from an interview study with blind people (n = 5) and museum experts (n = 5), we designed and prototyped a robotic system that supported blind visitors to join group tours. We conducted a field study in a science museum where each blind participant (n = 8) joined a group tour with one guide and two sighted participants (n = 8). Findings indicated users’ sense of safety from the robot’s navigational support, concerns in the group participation, and preferences for obtaining environmental information. We present design implications for future robotic systems to support blind people’s mixed-visual group participation.

In large-scale classrooms, students often struggle to ask questions due to limited instructor attention and social pressure. Based on findings from a formative study with 24 students and 12 instructors, we designed AskNow, an LLM-powered system that enables students to ask questions and receive real-time, context-aware responses grounded in the ongoing lecture and that allows instructors to view students’ questions collectively. We deployed AskNow in three university computer science courses for a week and tested with 117 students. To evaluate AskNow ’s responses, each instructor rated the perceived correctness and satisfaction of 100 randomly sampled AskNow -generated responses. In addition, we conducted interviews with 24 students and the three instructors to understand their experience with AskNow. We found that AskNow significantly reduced students’ perceived time to resolve confusion. Instructors rated AskNow’s responses as highly accurate and satisfactory. Instructor and student feedback provided insights into the role of such systems in supporting real-time learning in large lecture settings.

Advances in AI have enabled robots to engage in flexible, multi-turn dialogue. Yet many scenarios require robots to utilize additional modalities that complement speech to convey complex information. Robot screens can meet this need by supporting parallel processing, quick verification, and richer representations. As robots are integrated into an increasing number of scenarios with complex communication requirements, there is a need to systematically examine how screens may be used and designed to complement and augment verbal communication. In this paper, through an analysis of 357 commercial and research robots, we outline an initial design space of robot screens. Building on this design space, we present findings from two studies: a co-design study (n = 12) that explored user preferences for screen designs and derived a set of design guidelines; and an online pilot study (n = 89) that integrated these guidelines into screen designs and evaluated how these designs shaped user perceptions of robot communication. Our contributions include a database of screen-equipped robots; the design space of and guidelines for informational robot screens; and an empirical understanding of user perceptions on the use of robot screens.

This paper examines the community-driven sustenance of Moxie, a social robot that faced discontinuation when its parent company failed to secure funding. Through interviews and investigative digital ethnography, we study how users performed extensive articulation work to transition from corporate support to an open-source platform. Our findings reveal that invisible labor and value negotiation were central to Moxie's continued operation, simultaneously opening access for some users while excluding others. These processes also fundamentally reshaped the robot's desirability and meaning within the user community. This work demonstrates how socio-technical infrastructures, articulation work, and value renegotiation can sustain robots beyond their commercial lifecycles, while revealing the uneven distribution of both labor and access in community-driven technology repair and maintenance.

End-user robot programming grants users the flexibility to re-task robots in situ, yet it remains challenging for novices due to the need for specialized robotics knowledge. Large Language Models (LLMs) hold the potential to lower the barrier to robot programming by enabling task specification through natural language. However, current LLM-based approaches generate opaque, "black-box" code that is difficult to verify or debug, creating tangible safety and reliability risks in physical systems. We present RoboCritics, an approach that augments LLM-based robot programming with expert-informed motion-level critics. These critics encode robotics expertise to analyze motion-level execution traces for issues such as joint speed violations, collisions, and unsafe end-effector poses. When violations are detected, critics surface transparent feedback and offer one-click fixes that forward structured messages back to the LLM, enabling iterative refinement while keeping users in the loop. We instantiated RoboCritics in a web-based interface connected to a UR3e robot and evaluated it in a between-subjects user study (n=18). Compared to a baseline LLM interface, RoboCritics reduced safety violations, improved execution quality, and shaped how participants verified and refined their programs. Our findings demonstrate that RoboCritics enables more reliable and user-centered end-to-end robot programming with LLMs.

Robot morphology-the form, body shape, and structure of robots-makes up a key design space in human-robot interaction (HRI), shaping how robots function, express themselves, and interact with humans. Yet, despite its importance, little is known about how design frameworks might guide form exploration and generation. To address this gap, we introduce Elements of Robot Morphology, a design framework that identifies five fundamental elements: intelligence, kinematics, end effectors, locomotion, and structure. Based on an analysis of robots in the IEEE robot database, this framework provides a foundation for exploring diverse robot forms. To operationalize the framework, we developed Morphology Exploration Blocks (MEB), a set of tangible blocks that enable designers to physically build and experiment with different morphologies, fostering hands-on and collaborative exploration. We evaluated the framework and toolkit through a case study and a series of design workshops, demonstrating their support for analysis, ideation, reflection, and collaborative creation in robot design.

Robots are increasingly entering the daily lives of families, yet their successful integration into domestic life remains a challenge. We explore family routines as a critical entry point for understanding how robots might find a sustainable role in everyday family settings. Together with each of the ten families, we co-designed robot interactions and behaviors, and a plan for the robot to support their chosen routines, accounting for contextual factors such as timing, participants, locations, and the activities in the environment. We then designed, prototyped, and deployed a mobile social robot in a four-day, in-home user study. Families welcomed the robot’s reminders, with parents especially appreciating the offloading of some reminding tasks. At the same time, interviews revealed tensions around timing, authority, and family dynamics, highlighting the complexity of integrating robots into households beyond the immediate task of reminders. Based on these insights, we offer design implications for robot-facilitated contextual reminders and discuss broader considerations for designing robots for family settings.

Robots such as those serving as educational tutors, healthcare supporters, and collaborative partners must develop “rapport,” a construct that encompasses mutual understanding and interpersonal connection with people, to ensure their long-term success. In our earlier work, we constructed, evaluated, and validated an 18-item Connection–Coordination Rapport (CCR) scale to measure human–robot rapport (Studies 1–3). Even though the full-length 18-item CCR scale measures rapport thoroughly, it may not always be practical for researchers to adopt given its relatively long length. Therefore, in this work, we developed a reduced-length version of the CCR scale that still effectively measures rapport using just 8 items. Following recommended practices for short-form development and validation, we leveraged the input of Human–Robot Interaction (HRI) experts (Study 4, \(N=30\) ) to shorten the CCR scale from 18 items to 8 items (4 items per factor). Then, we evaluated this reduced-length CCR scale on a new sample (Study 5, \(N=186\) ) where online participants watched a HRI video and evaluated it using both the full-length and reduced-length CCR scales. We validated the reduced-length CCR scale by showing that it has high internal reliability, high overlap with the full-length CCR scale, a consistent factor structure, high construct validity, and significant time savings.

Roboticists are continually improving the quality of social robot behaviors and interactions with humans. This is a major goal of the field of social robotics, which seeks to create socially competent robots and improve their overall acceptance. Toward this effort, we propose the utilization of primal world beliefs (i.e., beliefs about the character of the world) to design behaviors that are relatable, intuitive, and based on an internal motivation. However, it is not yet clear whether humans can reliably discern these world beliefs in robots. In this work, we explore whether primals can serve as a novel framework to inform the design of robot personality and attempt to understand whether and how humans perceive primals within robots. Through two large online user studies ( \(n=300\) ; \(n=360\) ), we show that (1) participants are broadly able to discern intended primals in robots, (2) certain participant and robot primals predict participant perception of the robots, and (3) similarity between human and robot primals predicts improved perception of robots.

Working with children in human-robot interaction (HRI) research presents novel challenges that require thoughtful preparation and reflection.However, given that HRI is still an emerging field of research, there is limited guidance for early-career HRI researchers focusing on child-robot interaction.We present a practical guide to working with children and robots, tailored for early-career researchers whome we refer to throughout as 'roboticists.'We pose several questions to encourage reflection and consideration for working with children, and other vulnerable populations, when designing and testing robots.The goal of this guide is to provide a resource for early-career roboticists when designing human-robot interaction studies that include children.We structure our guide to reflect on three research phases-pre-study, study, and post-studyand address critical questions regarding ethics, logistics, and inclusive communication.With this WiP report, we hope to receive feedback from the IDC community and establish collaborations to improve the proposed guide.In future work, we plan to collect testimonials from roboticists to diversify and expand the practical insights into a more comprehensive practical guide.

AI-assisted learning companion robots are increasingly used in early education. Many parents express concerns about content appropriateness, while they also value how AI and robots could supplement their limited skill, time, and energy to support their children's learning. We designed a card-based kit, SET, to systematically capture scenarios that have different extents of parental involvement. We developed a prototype interface, PAiREd, with a learning companion robot to deliver LLM-generated educational content that can be reviewed and revised by parents. Parents can flexibly adjust their involvement in the activity by determining what they want the robot to help with. We conducted an in-home field study involving 20 families with children aged 3-5. Our work contributes to an empirical understanding of the level of support parents with different expectations may need from AI and robots and a prototype that demonstrates an innovative interaction paradigm for flexibly including parents in supporting their children.

Intergenerational co-creation using technology between grandparents and grandchildren can be challenging due to differences in technological familiarity.AI has emerged as a promising tool to support co-creative activities, offering flexibility and creative assistance, but its role in facilitating intergenerational connection remains underexplored.In this study, we conducted a user study with 29 grandparent-grandchild groups engaged in AI-supported story creation to examine how AI-assisted co-creation can foster meaningful intergenerational bonds.Our findings show that grandchildren managed the technical aspects, while grandparents contributed creative ideas and guided the storytelling.AI played a key role in structuring the activity, facilitating brainstorming, enhancing storytelling, and balancing the contributions of both generations.The process fostered mutual appreciation, with each generation recognizing the strengths of the other, leading to an engaging and cohesive co-creation process.We offer design implications for integrating AI into intergenerational co-creative activities, emphasizing how AI can enhance connection across skill levels and technological familiarity.

Automated planning is traditionally the domain of experts, utilized in fields like manufacturing and healthcare with the aid of expert planning tools. Recent advancements in LLMs have made planning more accessible to everyday users due to their potential to assist users with complex planning tasks. However, LLMs face several application challenges within end-user planning, including consistency, accuracy, and user trust issues. This paper introduces VeriPlan, a system that applies formal verification techniques, specifically model checking, to enhance the reliability and flexibility of LLMs for end-user planning. In addition to the LLM planner, VeriPlan includes three additional core features -- a rule translator, flexibility sliders, and a model checker -- that engage users in the verification process. Through a user study (n=12), we evaluate VeriPlan, demonstrating improvements in the perceived quality, usability, and user satisfaction of LLMs. Our work shows the effective integration of formal verification and user-control features with LLMs for end-user planning tasks.

In this late-breaking report, we present our design process motivated to build tangible, cost-effective, child- and family-friendly social robot prototypes aimed to (1) support the practical needs of families, as well as (2) serve as a feasible research platform to mitigate technical and logistical challenges faced within conducting inhome HRI studies. We apply a research through design approach, combined with participatory design methods, to produce functional prototypes. In future work, we are preparing to continue improving prototype design by conducting (1) iterative co-design sessions with artists and product designers focusing on the hardware design, (2) iterative co-design sessions with families focusing on the interaction design, and (3) long-term technology probe studies in homes to evaluate the effectiveness of the technological platform. We aim to solicit feedback from the HRI community on our design process and prototype research platform.

We demonstrate a telepresence robot system that supports multiple users to visit a place together remotely. People create meanings in places through their personal and social experiences in the place. Interacting with places is essential for people to obtain new knowledge, build their social ties, and form personal identities. However, people may not always have access to places they want to go, in particular if they want to visit with other people and share the experience together. Telepresence robots have the promise to address this challenge by reducing the travel cost and easing the coordination effort among multiple visitors. We propose the design of a telepresence robot that allows multiple users to have shared experiences in remote places and enhances the bonding between people and the place.

Robots, particularly in service and companionship roles, must develop positive relationships with people they interact with regularly to be successful. These positive human-robot relationships can be characterized as establishing “rapport,” which indicates mutual understanding and interpersonal connection that form the groundwork for successful long-term human-robot interaction. However, the human-robot interaction research literature lacks scale instruments to assess human-robot rapport in a variety of situations. In this work, we developed the 18-item Connection-Coordination Rapport (CCR) Scale to measure human-robot rapport. We first ran Study 1 (N = 288) where online participants rated videos of human-robot interactions using a set of candidate items. Our Study 1 results showed the discovery of two factors in our scale, which we named “Connection” and “Coordination.” We then evaluated this scale by running Study 2 (N = 201) where online participants rated a new set of human-robot interaction videos with our scale and an existing rapport scale from virtual agents research for comparison. We also validated our scale by replicating a prior in-person human-robot interaction study, Study 3 (N = 44), and found that rapport is rated significantly greater when participants interacted with a responsive robot (responsive condition) as opposed to an unresponsive robot (unresponsive condition). Results from these studies demonstrate high reliability and validity for the CCR scale, which can be used to measure rapport in both first-person and third-person perspectives. We encourage the adoption of this scale in future studies to measure rapport in a variety of human-robot interactions.

Foundation models are rapidly improving the capability of robots in performing everyday tasks autonomously such as meal preparation, yet robots will still need to be instructed by humans due to model performance, the difficulty of capturing user preferences, and the need for user agency. Robots can be instructed using various methods-natural language conveys immediate instructions but can be abstract or ambiguous, whereas end-user programming supports longer-horizon tasks but interfaces face difficulties in capturing user intent. In this work, we propose using direct manipulation of images as an alternative paradigm to instruct robots, and introduce a specific instantiation called ImageInThat which allows users to perform direct manipulation on images in a timeline-style interface to generate robot instructions. Through a user study, we demonstrate the efficacy of ImageInThat to instruct robots in kitchen manipulation tasks, comparing it to a text-based natural language instruction method. The results show that participants were faster with ImageInThat and preferred to use it over the text-based method. Supplementary material including code can be found at: https://image-in-that.github.io/.

If robots are to be deployed in public places, we need to understand what factors their design should consider. Informed by sociological studies of urban settings, particularly the work of William H. Whyte and the Street Life Project, we describe four characteristics of public places that affect and are affected by robot design: (1) localism-how robot design aligns with the identity, culture, and character of the place(s) they reside within; (2) environments-the physical characteristics of the environment in which public robots operate; (3) activities- consideration for the various daily, occasional, and situational activities that are tied to place(s) robots inhabit; and (4) sociability- how people collectively and individually relate to, interact with, and make sense of robots deployed in public places. Throughout, we illustrate these characteristics with examples drawn from empirical studies of public robots. We discuss how these key characteristics of public places can inform HRI design.

While robots possess many capabilities that may positively influence human lives, their autonomous navigation and sensing capabilities pose threats to user privacy. These threats may be addressed at three key phases: data collection, data retention, and data exposure. In this work, we discuss our prior, current, and proposed robot design efforts to reduce privacy violations during human-robot interaction (HRI). At the data collection phase, we are currently exploring designs that enable robots to inhibit data collection by blocking their own sensors. At the data retention phase, we propose the exploration of privacy preferences to inform designs that grant users greater control over retained data. Finally, in the data exposure phase, we discuss our prior works developing a privacy controller for appropriate data exposure and generating task-planning strategies to limit unintentional data exposure. Through this work, we hope to protect user data and reduce the likelihood of harm to users.

As interest in studying in-the-wild human-robot interaction grows, there is a need for methods to collect data over time and in naturalistic or potentially private environments. HRI researchers have increasingly used the diary method for these studies, asking study participants to self-administer a structured data collection instrument, i.e., a diary, over a period of time. Although the diary method offers a unique window into settings that researchers may not have access to, they also lack the interactivity and probing that interview-based methods offer. In this paper, we explore a novel data collection method in which a robot plays the role of an interactive diary. We developed the Diary Robot system and performed inhome deployments for a week to evaluate the feasibility and effectiveness of this approach. Using traditional text-based and audio-based diaries as benchmarks, we found that robots are able to effectively elicit the intended information. We reflect on our findings, and describe scenarios where the utilization of robots in diary studies as a data collection instrument may be especially applicable.

Robot-human trust is an important concern as robots become integrated into human spaces. We tested a method grounded in psychological theory to increase human-robot trust—synchronous motion. Human participants completed a goal-oriented ball-moving task with a robotic arm to sound cues that were synchronous or asynchronous with the robot’s pacing. Participants were instructed to follow sound cues without information about synchrony. We found that participants in the synchrony condition trusted the robot to complete a new task that was comparable to the task they completed, significantly more than the asynchrony condition. However, this effect did not extend to harder tasks. The participants in the synchrony condition also believed that the robot had more influence on the outcomes of the new task compared to the asynchrony condition. On average, participants’ trust increased with the robotic arm after completing the task, regardless of condition. We report findings from a thematic analysis that demonstrate that participants in the synchrony condition found synchrony to be beneficial, while participants in the asynchrony condition found it cognitively taxing to be out-of-sync. Results from this work may be used to improve human-robot interactions in various contexts.

Figure 1: In this paper, we present NarraGuide, an LLM-based narrative mobile robot that guides users to explore a remote physical place in real-time and uses dialogue to deliver rich location-based information to enhance the experience.

In this paper, we explore the design and use of conversational telepresence robots to help homebound older adults interact with the external world. An initial needfinding study (N=8) using video vignettes revealed older adults’ experiential needs for robot-mediated remote experiences such as exploration, reminiscence and social participation. We then designed a prototype system to support these goals and conducted a technology probe study (N=11) to garner a deeper understanding of user preferences for remote experiences. The study revealed user interactive patterns in each desired experience, highlighting the need of robot guidance, social engagements with the robot and the remote bystanders. Our work identifies a novel design space where conversational telepresence robots can be used to foster meaningful interactions in the remote physical environment. We offer design insights into the robot’s proactive role in providing guidance and using dialogue to create personalized, contextualized and meaningful experiences.

Traditional approaches to human-robot interaction design typically examine robot behaviors in controlled environments and narrow tasks. These methods are impractical for designing robots that interact with diverse user groups in complex human environments. Drawing from the field of theater, we present the construct of scenes—individual environments consisting of specific people, objects, spatial arrangements, and social norms—and tangible scenography, as a holistic design approach for human-robot interactions. We created a design tool, Tangible Scenography Kit (TaSK), with physical props to aid in design brainstorming. We conducted design sessions with eight professional designers to generate exploratory designs. Designers used tangible scenography and TaSK components to create multiple scenes with specific interaction goals, characterize each scene’s social environment, and design scene-specific robot behaviors. From these sessions, we found that this method can encourage designers to think beyond a robot’s narrow capabilities and consider how they can facilitate complex social interactions.

Increasing evidence suggests that many deployed AI systems do not sufficiently support end-user interaction and information needs. Engaging end-users in the design of these systems can reveal user needs and expectations, yet effective ways of engaging end-users in the AI explanation design remain under-explored. To address this gap, we developed a design method, called AI-DEC, that defines four dimensions of AI explanations that are critical for the integration of AI systems—communication content, modality, frequency, and direction—and offers design examples for end-users to design AI explanations that meet their needs. We evaluated this method through co-design sessions with workers in healthcare, finance, and management industries who regularly use AI systems in their daily work. Findings indicate that the AI-DEC effectively supported workers in designing explanations that accommodated diverse levels of performance and autonomy needs, which varied depending on the AI system’s workplace role and worker values. We discuss the implications of using the AI-DEC for the user-centered design of AI explanations in real-world systems.

Robots in real-world environments continuously engage with multiple users and encounter changes that lead to unexpected conflicts in fulfilling user requests. Recent technical advancements (e.g., large-language models (LLMs), program synthesis) offer various methods for automatically generating repair plans that address such conflicts. In this work, we understand how automated repair and explanations can be designed to improve user experience with robot failures through two user studies. In our first, online study (n = 162), users expressed increased trust, satisfaction, and utility with the robot performing automated repair and explanations. However, we also identified risk factors—safety, privacy, and complexity—that require adaptive repair strategies. The second, in-person study (n = 24) elucidated distinct repair and explanation strategies depending on the level of risk severity and type. Using a design-based approach, we explore automated repair with explanations as a solution for robots to handle conflicts and failures, complemented by adaptive strategies for risk factors. Finally, we discuss the implications of incorporating such strategies into robot designs to achieve seamless operation among changing user needs and environments.

Novel end-user programming (EUP) tools enable on-the-fly (i.e., spontaneous, easy, and rapid) creation of interactions with robotic systems. These tools are expected to empower users in determining system behavior, although very little is understood about how end users perceive, experience, and use these systems. In this paper, we seek to address this gap by investigating end-user experience with on-the-fly robot EUP. We trained 21 end users to use an existing on-the-fly EUP tool, asked them to create robot interactions for four scenarios, and assessed their overall experience. Our findings provide insight into how these systems should be designed to better support end-user experience with on-the-fly EUP, focusing on user interaction with an automatic program synthesizer that resolves imprecise user input, the use of multimodal inputs to express user intent, and the general process of programming a robot.

Traumatic brain injury (TBI) can cause a range of cognitive and communication challenges that negatively affect social participation in both face-to-face interactions and computer-mediated communication. In particular, individuals with TBI report barriers that limit access to participation on social media platforms. To improve access to and use of social media for users with TBI, we introduce the Social Media Accessibility and Rehabilitation Toolkit (SMART-TBI). The toolkit includes five aids (Writing Aid, Interpretation Aid, Filter Mode, Focus Mode, and Facebook Customization) designed to address the cognitive and communicative needs of individuals with TBI. We asked eight users with moderate-severe TBI and five TBI rehabilitation experts to evaluate each aid. Our findings revealed potential benefits of aids and areas for improvement, including the need for psychological safety, privacy control, and balancing business and accessibility needs; and overall mixed reactions among the participants to AI-based aids.

Learning companion robots for young children are increasingly adopted in informal learning environments. Although parents play a pivotal role in their children’s learning, very little is known about how parents prefer to incorporate robots into their children’s learning activities. We developed prototype capabilities for a learning companion robot to deliver educational prompts and responses to parent-child pairs during reading sessions and conducted in-home user studies involving 10 families with children aged 3–5. Our data indicates that parents want to work with robots as collaborators to augment parental activities to foster children’s learning, introducing the notion of parent-robot collaboration. Our findings offer an empirical understanding of the needs and challenges of parent-child interaction in informal learning scenarios and design opportunities for integrating a companion robot into these interactions. We offer insights into how robots might be designed to facilitate parent-robot collaboration, including parenting policies, collaboration patterns, and interaction paradigms.

Assistive technologies for adults with Down syndrome (DS) need designs tailored to their specific technology requirements. While prior research has explored technology design for individuals with intellectual disabilities, little is understood about the needs and expectations of adults with DS. Assistive technologies should leverage the abilities and interests of the population, while incorporating age- and context-considerate content. In this work, we interviewed six adults with DS, seven parents of adults with DS, and three experts in speech-language pathology, special education, and occupational therapy to determine how technology could support adults with DS. In our thematic analysis, four main themes emerged, including (1) community vs. home social involvement; (2) misalignment of skill expectations between adults with DS and parents; (3) family limitations in technology support; and (4) considerations for technology development. Our findings extend prior literature by including the voices of adults with DS in how and when they use technology.

As robotic products become more integrated into daily life, there is a greater need to understand authentic and real-world human-robot interactions to inform product design. Across many domestic, educational, and public settings, robots interact with not only individuals and groups of users, but also families, including children, parents, relatives, and even pets. However, products developed to date and research in human-robot and child-robot interactions have focused on the interaction with their primary users, neglecting the complex and multifaceted interactions between family members and with the robot. There is a significant gap in knowledge, methods, and theories for how to design robots to support these interactions. To inform the design of robots that can support and enhance family life, this paper provides (1) a narrative review exemplifying the research gap and opportunities for family-robot interactions and (2) an actionable family-centered framework for research and practices in human-robot and child-robot interaction.

Many industrial tasks-such as sanding, installing fasteners, and wire harnessing-are difficult to automate due to task complexity and variability. We instead investigate deploying robots in an assistive role for these tasks, where the robot assumes the physical task burden and the skilled worker provides both the high-level task planning and low-level feedback necessary to effectively complete the task. In this article, we describe the development of a system for flexible human-robot teaming that combines state-of-the-art methods in end-user programming and shared autonomy and its implementation in sanding applications. We demonstrate the use of the system in two types of sanding tasks, situated in aircraft manufacturing, that highlight two potential workflows within the human-robot teaming setup. We conclude by discussing challenges and opportunities in human-robot teaming identified during the development, application, and demonstration of our system.

Large-language models (LLMs) hold significant promise in improving human-robot interaction, offering advanced conversational skills and versatility in managing diverse, open-ended user requests in various tasks and domains. Despite the potential to transform human-robot interaction, very little is known about the distinctive design requirements for utilizing LLMs in robots, which may differ from text and voice interaction and vary by task and context. To better understand these requirements, we conducted a user study (n = 32) comparing an LLM-powered social robot against text- and voice-based agents, analyzing task-based requirements in conversational tasks, including choose, generate, execute, and negotiate. Our findings show that LLM-powered robots elevate expectations for sophisticated non-verbal cues and excel in connection-building and deliberation, but fall short in logical communication and may induce anxiety. We provide design implications both for robots integrating LLMs and for fine-tuning LLMs for use with robots.

In this paper, we explore how techniques from soft robotics can help create a new form of robot expression. We present Sprout, a soft expressive robot that conveys its internal states by changing its body shape. Sprout can extend, bend, twist, and expand using fiber-embedded actuators integrated into its construction. These deformations enable Sprout to express its internal states, for example, by expanding to express anger and bending its body sideways to express curiosity. Through two user studies, we investigated how users interpreted Sprout's expressions, their perceptions of Sprout, and their expectations from future iterations of Sprout's design. We argue that the use of soft actuators opens a novel design space for robot expressions to convey internal states, emotions, and intent.

Authored robotics applications have a diverse set of requirements for their authoring interfaces, being dependent on the underlying architecture of the program, the capabilities of the programmers and engineers using them, and the capabilities of the robot. Visual programming approaches have long been favored for both novice-level accessibility and clear graphical representations, but current tools are limited in their customizability and ability to be integrated holistically into larger design interfaces. OpenVP attempts to address this by providing a highly configurable and customizable component library that can be integrated easily into other modern web-based applications.

Robots are ubiquitous in small-to-large-scale manufacturers. While collaborative robots (cobots) have significant potential in these settings due to their flexibility and ease of use, proper integration is critical to realize their full potential. Specifically, cobots need to be integrated in ways that utilize their strengths, improve manufacturing performance, and facilitate use in concert with human workers. Effective integration requires careful consideration and the knowledge of roboticists, manufacturing engineers, and business administrators. We propose an approach involving the stages of planning, analysis, development, and presentation, to inform manufacturers about cobot integration within their facilities prior to the integration process. We contextualize our approach in a case study with an SME collaborator and discuss insights learned.

Despite advances in areas such as the personalization of robots, sustaining adoption of robots for long-term use in families remains a challenge. Recent studies have identified integrating robots into families’ routines and rituals as a promising approach to support long-term adoption. However, few studies explored the integration of robots into family routines and there is a gap in systematic measures to capture family preferences for robot integration. Building upon existing routine inventories, we developed Family-Robot Routines Inventory (FRRI), with 24 family routines and 24 child routine items, to capture parents’ attitudes toward and expectations from the integration of robotic technology into their family routines. Using this inventory, we collected data from 150 parents through an online survey. Our analysis indicates that parents had varying perceptions for the utility of integrating robots into their routines. For example, parents found robot integration to be more helpful in children’s individual routines, than to the collective routines of their families. We discuss the design implications of these preliminary findings, and how they may serve as a first step toward understanding the diverse challenges and demands of designing and integrating household robots for families.

In this paper, we explore the new design space of extra-linguistic cues inspired by graphical tropes used in graphic novels and animation to enhance the expressiveness of social robots. We identified a set of cues that can be used to generate expressions, including smoke/steam/fog, water droplets, and bubbles, and prototyped devices that can generate these fluid expressions for a robot. We conducted design sessions where eight designers explored the use and utility of these expressions in conveying the robot’s internal states in various design scenarios. Our analysis of the 22 designs, the associated design justifications, and the interviews with designers revealed patterns in how each form of expression was used, how they were combined with nonverbal cues, and where the participants drew their inspiration from. These findings informed the design of an integrated module called EmoPack, which can be used to augment the expressive capabilities of any robot platform.

The goal of this workshop is to have interdisciplinary discussions on family-centered interaction design of technology as an extension to child-centered design. The workshop will discuss the potential benefits of a family-centered approach to design, as well as the challenges and open questions that designers may face when adopting this approach. Through discussions and interactive activities, participants will have the opportunity to discuss and share ideas on how to effectively incorporate a family-centered perspective into their own design processes. A family-centered approach to design has the potential to create more meaningful and contextual experiences for children and their families.

In this work, we discuss a theoretically motivated family-centered design approach for child-robot interactions, adapted by Family Systems Theory (FST) and Family Ecological Model (FEM). Long-term engagement and acceptance of robots in the home is influenced by factors that surround the child and the family, such as child-sibling-parent relationships and family routines, rituals, and values. A family-centered approach to interaction design is essential when developing in-home technology for children, especially for social agents like robots with which they can form connections and relationships. We review related literature in family theories and connect it with child-robot interaction and child-computer interaction research. We present two case studies that exemplify how family theories, FST and FEM, can inform the integration of robots into homes, particularly research into child-robot and family-robot interaction. Finally, we pose five overarching recommendations for a family-centered design approach in child-robot interactions.

We aim to design robotic educational support systems that can promote socially and intellectually meaningful learning experiences for students while they complete school work outside of class. To pursue this goal, we conducted participatory design studies with 10 children (aged 10–12) to explore their design needs for robot-assisted homework. We investigated children’s current ways of doing homework, the type of support they receive while doing homework, and co-designed the speech and expressiveness of a homework companion robot. Children and parents attending our design sessions explained that an emotionally expressive social robot as a homework aid can support students’ motivation and engagement, as well as their affective state. Children primarily perceived the robot as a dedicated assistant at home, capable of forming meaningful friendships, or a shared classroom learning resource. We present key design recommendations to support students’ homework experiences with a learning companion robot.

Parent-child interaction is critical for child development, yet parents may need guidance in some aspects of their engagement with their children. Current research on educational math robots focuses on child-robot interactions but falls short of including the parents and integrating the critical role they play in children’s learning. We explore how educational robots can be designed to facilitate parent-child conversations, focusing on math talk, a predictor of later math ability in children. We prototyped capabilities for a social robot to support math talk via reading and play activities and conducted an exploratory Wizard-of-Oz in-home study for parent-child interactions facilitated by a robot. Our findings yield insights into how parents were inspired by the robot’s prompts, their desired interaction styles and methods for the robot, and how they wanted to include the robot in the activities, leading to guidelines for the design of parent-child-robot interaction in educational contexts.

Traumatic brain injury (TBI) can cause cognitive, communication, and psychological challenges that profoundly limit independence in everyday life. Conversational Agents (CAs) can provide individuals with TBI with cognitive and communication support, although little is known about how they make use of CAs to address injury-related needs. In this study, we gave nine adults with TBI an at-home CA for four weeks to investigate use patterns, challenges, and design requirements, focusing particularly on injury-related use. The findings revealed significant gaps between the current capabilities of CAs and accessibility challenges faced by TBI users. We also identified 14 TBI-related activities that participants engaged in with CAs. We categorized those activities into four groups: mental health, cognitive activities, healthcare and rehabilitation, and routine activities. Design implications focus on accessibility improvements and functional designs of CAs that can better support the day-to-day needs of people with TBI.

Cooking is a vital yet challenging activity for blind and low vision (BLV) people, which involves many visual tasks that can be difficult and dangerous. BLV training services, such as vision rehabilitation, can effectively improve BLV people’s independence and quality of life in daily tasks, such as cooking. However, there is a lack of understanding on the practices employed by the training professionals and the barriers faced by BLV people in such training. To fill the gap, we interviewed six professionals to explore their training strategies and technology recommendations for BLV clients in cooking activities. Our findings revealed the fundamental principles, practices, and barriers in current BLV training services, identifying the gaps between training and reality.

Traumatic brain injury (TBI) can result in chronic sensorimotor, cognitive, psychosocial, and communication challenges that can limit social participation. Social media can be a useful outlet for social participation for individuals with TBI, but there are barriers to access. While research has drawn attention to the nature of access barriers, few studies have investigated technological solutions to address these barriers, particularly considering the perspectives of individuals with TBI. To address this gap in knowledge, we used a participatory approach to engage 10 adults with TBI in conceptualizing tools to address their challenges accessing Facebook. Participants described multifaceted challenges in using social media, including interface overload, social comparisons, and anxiety over self-presentation and communication after injury. They discussed their needs and preferences and generated ideas for design solutions. Our work contributes to designing assistive and accessibility technology to facilitate an equal access to the benefits of social media for individuals with TBI.

We present a participatory design method to design human-robot interactions with older adults and its application through a case study of designing an assistive robot for a senior living facility. The method, called Situated Participatory Design (sPD), was designed considering the challenges of working with older adults and involves three phases that enable designing and testing use scenarios through realistic, iterative interactions with the robot. In design sessions with nine residents and three caregivers, we uncovered a number of insights about sPD that help us understand its benefits and limitations. For example, we observed how designs evolved through iterative interactions and how early exposure to the robot helped participants consider using the robot in their daily life. With sPD, we aim to help future researchers to increase and deepen the participation of older adults in designing assistive technologies.

Social robots are becoming increasingly prevalent in the real world. Unsupervised user interactions in a natural and familiar setting, such as the home, can reveal novel design insights and opportunities. This paper presents an analysis and key design insights from family-robot interactions, captured via on-robot recordings during an unsupervised four-week in-home deployment of an autonomous reading companion robot for children. We analyzed interviews and 160 interaction videos involving six families who regularly interacted with a robot for four weeks. Throughout these interactions, we observed how the robot's expressions facilitated unique interactions with the child, as well as how family members interacted with the robot. In conclusion, we discuss five design opportunities derived from our analysis of natural interactions in the wild.

Service robots for personal use in the home and the workplace require end-user development solutions for swiftly scripting robot tasks as the need arises. Many existing solutions preserve ease, efficiency, and convenience through simple programming interfaces or by restricting task complexity. Others facilitate meticulous task design but often do so at the expense of simplicity and efficiency. There is a need for robot programming solutions that reconcile the complexity of robotics with the on-the-fly goals of end-user development. In response to this need, we present a novel, multimodal, and on-the-fly development system, Tabula. Inspired by a formative design study with a prototype, Tabula leverages a combination of spoken language for specifying the core of a robot task and sketching for contextualizing the core. The result is that developers can script partial, sloppy versions of robot programs to be completed and refined by a program synthesizer. Lastly, we demonstrate our anticipated use cases of Tabula via a set of application scenarios.

Robots designed to interact with people in collaborative or social scenarios must move in ways that are consistent with the robot's task and communication goals. However, combining these goals in a naïve manner can result in mutually exclusive solutions, or infeasible or problematic states and actions. In this paper, we present Lively, a framework which supports configurable, real-time, task-based and communicative or socially-expressive motion for collaborative and social robotics across multiple levels of programmatic accessibility. Lively supports a wide range of control methods (i.e. position, orientation, and joint-space goals), and balances them with complex procedural behaviors for natural, lifelike motion that are effective in collaborative and social contexts. We discuss the design of three levels of programmatic accessibility of Lively, including a graphical user interface for visual design called LivelyStudio, the core library Lively for full access to its capabilities for developers, and an extensible architecture for greater customizability and capability.

We investigate how robotic camera systems can offer new capabilities to computer-supported cooperative work through the design, development, and evaluation of a prototype system called Periscope. With Periscope, a local worker completes manipulation tasks with guidance from a remote helper who observes the workspace through a camera mounted on a semi-autonomous robotic arm that is co-located with the worker. Our key insight is that the helper, the worker, and the robot should all share responsibility of the camera view-an approach we call shared camera control. Using this approach, we present a set of modes that distribute the control of the camera between the human collaborators and the autonomous robot depending on task needs. We demonstrate the system's utility and the promise of shared camera control through a preliminary study where 12 dyads collaboratively worked on assembly tasks. Finally, we discuss design and research implications of our work for future robotic camera systems that facilitate remote collaboration.

Robots hold significant promise to assist with providing care to an aging population and to help overcome increasing caregiver demands. Although a large body of research has explored robotic assistance for individuals with disabilities and age-related challenges, this past work focuses primarily on building robotic capabilities for assistance and has not yet fully considered how these capabilities could be used by professional caregivers. To better understand the workflows and practices of caregivers who support aging populations and to determine how robotic assistance can be integrated into their work, we conducted a field study using ethnographic and co-design methods in a senior living community. From our results, we created a set of design opportunities for robotic assistance, which we organized into three different parts: supporting caregiver workflows, adapting to resident abilities, and providing feedback to all stakeholders of the interaction.

Research in child-robot interactions suggests that engaging in “care-taking” of a social robot, such as tucking the robot in at night, can strengthen relationships formed between children and robots. In this work, we aim to better understand and explore the design space of caretaking activities with 10 children, aged 8–12 from eight families, involving an exploratory design session followed by a preliminary feasibility testing of robot caretaking activities. The design sessions provided insight into children’s current caretaking tasks, how they would take care of a social robot, and how these new caretaking activities could be integrated into their daily routines. The feasibility study tested two different types of robot caretaking tasks, which we call connection and utility, and measured their short term effects on children’s perceptions of and closeness to the social robot. We discuss the themes and present interaction design guidelines of robot caretaking activities for children.

Social robots are emerging as learning companions for children, and research shows that they facilitate the development of interest and learning even through brief interactions. However, little is known about how such technologies might support these goals in authentic environments over long-term periods of use and interaction. We designed a learning companion robot capable of supporting children reading popular-science books by expressing social and informational commentaries. We deployed the robot in homes of 14 families with children aged 10–12 for four weeks during the summer. Our analysis revealed critical factors that affected children’s long-term engagement and adoption of the robot, including external factors such as vacations, family visits, and extracurricular activities; family/parental involvement; and children’s individual interests. We present four in-depth cases that illustrate these factors and demonstrate their impact on children’s reading experiences and discuss the implications of our findings for robot design.

Conversational interfaces increasingly rely on human-like dialogue to offer a natural experience. However, relying on dialogue involving multiple exchanges for even simple tasks can overburden users, particularly older adults. In this paper, we explored the use of politeness theory in conversation design to alleviate this burden and improve user experience. To achieve this goal, we categorized the voice interaction offered by a smart display application designed for older adults into seven major speech acts: request, suggest, instruct, comment, welcome, farewell, and repair. We identified face needs for each speech act, applied politeness strategies that best address these needs, and tested the ability of these strategies to shape the perceived politeness of a voice assistant in an online study (n = 64). Based on the findings of this study, we designed direct and polite versions of the system and conducted a field study (n = 15) in which participants used each of the versions for five days at their homes. Based on five factors merged from our qualitative findings, we identified four distinctive user personas—socially oriented follower, socially oriented leader, utility oriented follower, and utility oriented leader—that can inform personalized design of smart displays.

Social robots are increasingly introduced into children’s lives as educational and social companions, yet little is known about how these products might best be introduced to their environments. The emergence of the “unboxing” phenomenon in media suggests that introduction is key to technology adoption where initial impressions are made. To better understand this phenomenon toward designing a positive unboxing experience in the context of social robots for children, we conducted three field studies with families of children aged 8 to 13: (1) an exploratory free-play activity (n = 12); (2) a co-design session (n = 11) that informed the development of a prototype box and a curated unboxing experience; and (3) a user study (n = 9) that evaluated children’s experiences. Our findings suggest the unboxing experience of social robots can be improved through the design of a creative aesthetic experience that engages the child socially to guide initial interactions and foster a positive child-robot relationship.

In telemanipulation, showing the user multiple views of the remote environment can offer many benefits, although such different views can also create a problem for control. Systems must either choose a single fixed control frame, aligned with at most one of the views or switch between view-aligned control frames, enabling view-aligned control at the expense of switching costs. In this paper, we explore the trade-off between these options. We study the feasibility, benefits, and drawbacks of switching the user's control frame to align with the actively used view during telemanipulation. We additionally explore the effectiveness of explicit and implicit methods for switching control frames. Our results show that switching between multiple view-specific control frames offers significant performance gains compared to a fixed control frame. We also find personal preferences for explicit or implicit switching based on how participants planned their movements. Our findings offer concrete design guidelines for future multi-camera interfaces.

The introduction of collaborative robots (cobots) into the workplace has presented both opportunities and chal-lenges for those seeking to utilize their functionality. Prior research has shown that despite the capabilities afforded by cobots, there is a disconnect between those capabilities and the applications that they currently are deployed in, partially due to a lack of effective cobot-focused instruction in the field. Experts who work successfully within this collaborative domain could offer insight into the considerations and process they use to more effectively capture this cobot capability. Using an analysis of expert insights in the collaborative interaction design space, we developed a set of Expert Frames based on these insights and integrated these Expert Frames into a new training and programming system that can be used to teach novice operators to think, program, and troubleshoot in ways that experts do. We present our system and case studies that demonstrate how Expert Frames provide novice users with the ability to analyze and learn from complex cobot application scenarios.

As social robots become increasingly prevalent in day-to-day environments, they will participate in conversations and appropriately manage the information shared with them. However, little is known about how robots might appropriately discern the sensitivity of information, which has major implications for human-robot trust. As a first step to address a part of this issue, we designed a privacy controller, Confidant, for conversational social robots, capable of using contextual metadata (e.g., sentiment, relationships, topic) from conversations to model privacy boundaries. Afterwards, we conducted two crowdsourced user studies. The first study ($n=174$) focused on whether a variety of human-human interaction scenarios were perceived as either private/sensitive or non-private/non-sensitive. The findings from our first study were used to generate association rules. Our second study ($n=95$) evaluated the effectiveness and accuracy of the privacy controller in human-robot interaction scenarios by comparing a robot that used our privacy controller against a baseline robot with no privacy controls. Our results demonstrate that the robot with the privacy controller outperforms the robot without the privacy controller in privacy-awareness, trustworthiness, and social-awareness. We conclude that the integration of privacy controllers in authentic human-robot conversations can allow for more trustworthy robots. This initial privacy controller will serve as a foundation for more complex solutions.

Remotely programming robots to execute tasks often relies on registering objects of interest in the robot's environment. Frequently, these tasks involve articulating objects such as opening or closing a valve. However, existing human-in-the-loop methods for registering objects do not consider articulations and the corresponding impact to the geometry of the object, which can cause the methods to fail. In this work, we present an approach where the registration system attempts to automatically determine the object model, pose, and articulation for user-selected points using nonlinear fitting and the iterative closest point algorithm. When the fitting is incorrect, the operator can iteratively intervene with corrections after which the system will refit the object. We present an implementation of our fitting procedure for one degree-of-freedom (DOF) objects with revolute joints and evaluate it with a user study that shows that it can improve user performance, in measures of time on task and task load, ease of use, and usefulness compared to a manual registration approach. We also present a situated example that integrates our method into an end-to-end system for articulating a remote valve.

Drones can provide a minimally-constrained adapting camera view to support robot telemanipulation. Furthermore, the drone view can be automated to reduce the burden on the operator during teleoperation. However, existing approaches do not focus on two important aspects of using a drone as an automated view provider. The first is how the drone should select from a range of quality viewpoints within the workspace (e.g., opposite sides of an object). The second is how to compensate for unavoidable drone pose uncertainty in determining the viewpoint. In this paper, we provide a nonlinear optimization method that yields effective and adaptive drone viewpoints for telemanipulation with an articulated manipulator. Our first key idea is to use sparse human-in-the-loop input to toggle between multiple automatically-generated drone viewpoints. Our second key idea is to introduce optimization objectives that maintain a view of the manipulator while considering drone uncertainty and the impact on viewpoint occlusion and environment collisions. We provide an instantiation of our drone viewpoint method within a drone-manipulator remote teleoperation system. Finally, we provide an initial validation of our method in tasks where we complete common household and industrial manipulations.

Robots are increasingly being introduced into domains where they assist or collaborate with human counterparts. There is a growing body of literature on how robots might serve as collaborators in creative activities, but little is known about the factors that shape human perceptions of robots as creative collaborators. This paper investigates the effects of a robot’s social behaviors on people’s creative thinking and their perceptions of the robot. We developed an interactive system to facilitate collaboration between a human and a robot in a creative activity. We conducted a user study (n = 12), in which the robot and adult participants took turns to create compositions using tangram pieces projected on a shared workspace. We observed four human behavioral traits related to creativity in the interaction: accepting robot inputs as inspiration, delegating the creative lead to the robot, communicating creative intents, and being playful in the creation. Our findings suggest designs for co-creation in social robots that consider the adversarial effect of giving the robot too much control in creation, as well as the role playfulness plays in the creative process.

Children’s early numerical knowledge establishes a foundation for later development of mathematics achievement and playing linear number board games is effective in improving basic numerical abilities. Besides the visuo-spatial cues provided by traditional number board games, learning companion robots can integrate multi-sensory information and offer social cues that can support children’s learning experiences. We explored how young children experience sensory feedback (audio and visual) and social expressions from a robot when playing a linear number board game, “RoboMath.” We present the interaction design of the game and our investigation of children’s (n = 19, aged 4) and parents’ experiences under three conditions: (1) visual-only, (2) audio-visual, and (3) audio-visual-social robot interaction. We report our qualitative analysis, including the themes observed from interviews with families on their perceptions of the game and the interaction with the robot, their child’s experiences, and their design recommendations.

Emotion expression in human-robot interaction has been widely explored, however little is known about how such expressions should be coupled with feelings and opinions expressed by a social robot. We explored how 12 children experienced emotionally expressive social commentaries from a reading companion robot across five interaction styles that differed in their non-verbal emotional expressiveness and opinionated conversational styles (neutral, divergent, or convergent opinions). We found that, while the robot’s opinions and non-verbal emotion expressions affected children’s experiences with the robot, the speech content of the commentaries was the more prominent factor in their experience. Additionally, children differed in their perceptions of social commentary: while some expressed a sense of connection-making with the robot’s self-disclosure commentaries, others felt distracted by them or felt like the robot was off-topic. We recommend designers pay particular attention to the robot’s speech content and consider children’s individual differences in designing emotional and opinionated speech.

Computational notebooks help data analysts analyze and visualize datasets, and share analysis procedures and outputs. However, notebooks typically combine code (e.g., Python scripts), notes, and outputs (e.g., tables, graphs). The combination of disparate materials is known to hinder the comprehension of notebooks, making it difficult for analysts to collaborate with other analysts unfamiliar with the dataset. To mitigate this problem, we introduce ToonNote, a JupyterLab extension that enables the conversion of notebooks into “data comics.” ToonNote provides a simplified view of a Jupyter notebook, highlighting the most important results while supporting interactive and free exploration of the dataset. This paper presents the results of a formative study that motivated the system, its implementation, and an evaluation with 12 users, demonstrating the effectiveness of the produced comics. We discuss how our findings inform the future design of interfaces for computational notebooks and features to support diverse collaborators.

Human-robot interaction designers and developers navigate a complex design space, which creates a need for tools that support intuitive design processes and harness the programming capacity of state-of-the-art authoring environments. We introduce Figaro, an expressive tabletop authoring environment for mobile robots, inspired by shadow puppetry, that provides designers with a natural, situated representation of human-robot interactions while exploiting the intuitiveness of tabletop and tangible programming interfaces. On the tabletop, Figaro projects a representation of an environment. Users demonstrate sequences of behaviors, or scenes, of an interaction by manipulating instrumented figurines that represent the robot and the human. During a scene, Figaro records the movement of figurines on the tabletop and narrations uttered by users. Subsequently, Figaro employs real-time program synthesis to assemble a complete robot program from all scenes provided. Through a user study, we demonstrate the ability of Figaro to support design exploration and development for human-robot interaction.

Manipulations of a constrained object often use a non-rigid grasp that allows the object to rotate relative to the end effector. This orientation slip strategy is often present in natural human demonstrations, yet it is generally overlooked in methods to identify constraints from such demonstrations. In this paper, we present a method to model and recognize prehensile orientation slip in human demonstrations of constrained interactions. Using only observations of an end effector, we can detect the type of constraint, parameters of the constraint, and orientation slip properties. Our method uses a novel hierarchical model selection method that is informed by multiple origins of physics-based evidence. A study with eight participants shows that orientation slip occurs in natural demonstrations and confirms that it can be detected by our method.

In this paper, we present a meta-algorithm intended to accelerate many existing path optimization algorithms. The central idea of our work is to strategically break up a waypoint path into consecutive groupings called "pods," then optimize over various pods concurrently using parallel processing. Each pod is assigned a color, either blue or red, and the path is divided in such a way that adjacent pods of the same color have an appropriate buffer of the opposite color between them, reducing the risk of interference between concurrent computations. We present a path splitting algorithm to create blue and red pod groupings and detail steps for a meta-algorithm that optimizes over these pods in parallel. We assessed how our method works on a testbed of simulated path optimization scenarios using various optimization tasks and characterize how it scales with additional threads. We also compared our meta-algorithm on these tasks to other parallelization schemes. Our results show that our method more effectively utilizes concurrency compared to the alternatives, both in terms of speed and optimization quality.

In this work, we present a per-instant pose optimization method that can generate configurations that achieve specified pose or motion objectives as best as possible over a sequence of solutions, while also simultaneously avoiding collisions with static or dynamic obstacles in the environment. We cast our method as a weighted sum non-linear constrained optimization-based IK problem where each term in the objective function encodes a particular pose objective. We demonstrate how to effectively incorporate environment collision avoidance as a single term in this multi-objective, optimization-based IK structure, and provide solutions for how to spatially represent and organize external environments such that data can be efficiently passed to a real-time, performance-critical optimization loop. We demonstrate the effectiveness of our method by comparing it to various state-of-the-art methods in a testbed of simulation experiments and discuss the implications of our work based on our results.

As collaborative robots become increasingly widespread in manufacturing settings, there is a greater need for tools and interfaces to support operators who integrate, supervise, and troubleshoot these systems. In this paper, we present an application of the Robot Attention Demand (RAD) metric for use in the design of user interfaces to support operators in collaborative manufacturing scenarios. Building on prior work that introduced RAD, we designed and implemented prototype timeline and countdown-timer interfaces to be used within a collaborative assembly-inspection task where an operator is also responsible for a separate sorting task. We performed a user evaluation to investigate the effects of displaying predictive RAD information on operator performance and perceptions of the task. Our results show lower perceived task load and increased usability scores compared to baseline condition without an interface. These findings suggest that predictive RAD should be used by designers and engineers developing operator interfaces for collaborative robot applications in manufacturing.

We present situated live programming for human-robot collaboration, an approach that enables users with limited programming experience to program collaborative applications for human-robot interaction. Allowing end users, such as shop floor workers, to program collaborative robots themselves would make it easy to “retask” robots from one process to another, facilitating their adoption by small and medium enterprises. Our approach builds on the paradigm of trigger-action programming (TAP) by allowing end users to create rich interactions through simple trigger-action pairings. It enables end users to iteratively create, edit, and refine a reactive robot program while executing partial programs. This live programming approach enables the user to utilize the task space and objects by incrementally specifying situated trigger-action pairs, substantially lowering the barrier to entry for programming or reprogramming robots for collaboration. We instantiate situated live programming in an authoring system where users can create trigger-action programs by annotating an augmented video feed from the robot’s perspective and assign robot actions to trigger conditions. We evaluated this system in a study where participants (n = 10) developed robot programs for solving collaborative light-manufacturing tasks. Results showed that users with little programming experience were able to program HRC tasks in an interactive fashion and our situated live programming approach further supported individualized strategies and workflows. We conclude by discussing opportunities and limitations of the proposed approach, our system implementation, and our study and discuss a roadmap for expanding this approach to a broader range of tasks and applications.

Child-robot interactions in educational, developmental, and health domains are widely explored, but little is known about how families perceive the presence of a social robot in their home environment and its participation in day-to-day activities. To close this gap, we conducted a participatory design (PD) study with six families, with children aged 10--12, to examine how families perceive in-home social robots participating in shared activities. Our analysis identified three main themes: (1) the robot can have a range of roles in the home as a companion or as an assistant; (2) family members have different preferences for how they would like to interact with the robot in group or personal interactions; and (3) families have privacy, confidentiality, and ethical concerns regarding a social robot's presence in the home. Based on these themes and existing literature, we provide guidelines for the future interaction design of in-home social robots for children.

Collaborative robots, or cobots, represent a breakthrough technology designed for high-level (e.g. collaborative) interactions between workers and robots with capabilities for flexible deployment in industries such as manufacturing. Understanding how workers and companies use and integrate cobots is important to inform the future design of cobot systems and educational technologies that facilitate effective worker-cobot interaction. Yet, little is known about typical training for collaboration and the application of cobots in manufacturing. To close this gap, we interviewed nine experts in manufacturing about their experience with cobots. Our thematic analysis revealed that, contrary to the envisioned use, experts described most cobot applications as only low-level (e.g. pressing start/stop buttons) interactions with little flexible deployment, and experts felt traditional robotics skills were needed for collaborative and flexible interaction with cobots. We conclude with design recommendations for improved future robots, including programming and interface designs, and educational technologies to support collaborative use.

Social robots have varied effectiveness when interacting with humans in different interaction contexts. A robot programmed to escort individuals to a different location, for instance, may behave more appropriately in a crowded airport than a quiet library, or vice versa. To address these issues, we exploit ideas from program synthesis and propose an approach to transforming the structure of hand-crafted interaction programs that uses user-scored execution traces as input, in which end users score their paths through the interaction based on their experience. Additionally, our approach guarantees that transformations to a program will not violate task and social expectations that must be maintained across contexts. We evaluated our approach by adapting a robot program to both real-world and simulated contexts and found evidence that making informed edits to the robot's program improves user experience.

In this paper, we design and evaluate a novel form of visually-simulated haptic feedback cue for communicating weight in robot teleoperation. We propose that a visuo-proprioceptive cue results from inconsistencies created between the user's visual and proprioceptive senses when the robot's movement differs from the movement of the user's input. In a user study where participants teleoperate a six-DoF robot arm, we demonstrate the feasibility of using such a cue for communicating weight in four telemanipulation tasks to enhance user experience and task performance.

In this paper, we study the effects of delays in a mimicry-control robot teleoperation interface which involves a user moving their arms to directly show the robot how to move and the robot follows in real time. Unlike prior work considering delays in other teleoperation systems, we consider delays due to robot slowness in addition to latency in the onset of movement commands. We present a human-subjects study that shows how different amounts and types of delays have different effects on task performance. We compare the movements under different delays to reveal the strategies that operators use to adapt to delay conditions and to explain performance differences. Our results show that users can quickly develop strategies to adapt to slowness delays but not onset latency delays. We discuss the implications of our results for the future development of methods designed to mitigate the effects of delays.

In this paper, we investigate how collaborative robots, or cobots, typically composed of a robotic arm and a gripper carrying out manipulation tasks alongside human coworkers, can be enhanced with HRI capabilities by applying ideas and principles from character animation. To this end, we modified the appearance and behaviors of a cobot, with minimal impact on its functionality and performance, and studied the extent to which these modifications improved its communication with and perceptions by human collaborators. Specifically, we aimed to improve the Appeal of the robot by manipulating its physical appearance, posture, and gaze, creating an animal-like character with a head-on-neck morphology; to utilize Arcs by generating smooth trajectories for the robot arm; and to increase the lifelikeness of the robot through Secondary Action by adding breathing motions to the robot. In two user studies, we investigated the effects of these cues on collaborator perceptions of the robot. Findings from our first study showed breathing to have a positive effect on most measures of robot perception and reveal nuanced interactions among the other factors. Data from our second study showed that, using gaze cues alone, a robot arm can improve metrics such as likeability and perceived sociability.

Human-robot teaming is becoming increasingly common within manufacturing processes. A key aspect practitioners need to decide on when developing effective processes is the level of task interdependence between human and robot team members. Task interdependence refers to the extent to which one's behavior affects the performance of others in a team. In this work, we examine the effects of three levels of task interdependence - pooled, sequential, reciprocal - in human-robot teaming on human worker's mental states, task performance, and perceptions of the robot. Participants worked with the robot in an assembly task while their heart rate variability was being recorded. Results suggested human workers in the reciprocal interdependence level experienced less stress and perceived the robot more as a collaborator than other two levels. Task interdependence did not affect perceived safety. Our findings highlight the importance of considering task structure in human-robot teaming and inform future research on and industry practices for human-robot task allocation.

Collaborative robots promise to transform work across many industries and promote human-robot teaming as a novel paradigm. However, realizing this promise requires the understanding of how existing tasks, developed for and performed by humans, can be effectively translated into tasks that robots can singularly or human-robot teams can collaboratively perform. In the interest of developing tools that facilitate this process we present Authr, an end-to-end task authoring environment that assists engineers at manufacturing facilities in translating existing manual tasks into plans applicable for human-robot teams and simulates these plans as they would be performed by the human and robot. We evaluated Authr with two user studies, which demonstrate the usability and effectiveness of Authr as an interface and the benefits of assistive task allocation methods for designing complex tasks for human-robot teams. We discuss the implications of these findings for the design of software tools for authoring human-robot collaborative plans.

Education research offers strong evidence that social supports, learning interventions situated in meaningful social interaction, during learning can aid in developing interest and promote understanding for the content. However, children are often asked to complete homework tasks in isolation. To address this discrepancy, we build on prior work in social robotics to demonstrate the effectiveness of a socially adept robot, as compared to a socially neutral robot to generate situational interest and improve learning while reading a science textbook. We conducted a randomized controlled experiment (N = 63) of one reading interaction with either the socially adept or socially neutral robot. Our results show that children who read with a socially adept robot found the robot to be friendlier and more attractive, reported a higher level of closeness and mutual-liking for the robot, had higher situational interest, and made more scientifically accurate statements on a concept-map activity. We discuss the practical and theoretical implications of these findings.

Robots must exercise socially appropriate behavior when interacting with humans. How can we assist interaction designers to embed socially appropriate and avoid socially inappropriate behavior within human-robot interactions? We propose a multi-faceted interaction-design approach that intersects human-robot interaction and formal methods to help us achieve this goal. At the lowest level, designers create interactions from scratch and receive feedback from formal verification, while higher levels involve automated synthesis and repair of designs. In this extended abstract, we discuss past, present, and future work within each level of our design approach.

Human demonstrations are important in a range of robotics applications, and are created with a variety of input methods. However, the design space for these input methods has not been extensively studied. In this paper, focusing on demonstrations of hand-scale object manipulation tasks to robot arms with two-finger grippers, we identify distinct usage paradigms in robotics that utilize human-to-robot demonstrations, extract abstract features that form a design space for input methods, and characterize existing input methods as well as a novel input method that we introduce, the instrumented tongs. We detail the design specifications for our method and present a user study that compares it against three common input methods: free-hand manipulation, kinesthetic guidance, and teleoperation. Study results show that instrumented tongs provide high quality demonstrations and a positive experience for the demonstrator while offering good correspondence to the target robot.

In this section, we discuss some extensions of Section III and expand on the limitations mentioned in Section VI of the main article.

We present an offline method to generate smooth, feasible motion for robot arms such that end-effector pose goals of a 6-DoF path are matched within acceptable limits specified by the user. Our approach aims to accurately match the position and orientation goals of the given path, and allows deviation from these goals if there is danger of self-collisions, joint-space discontinuities or kinematic singularities. Our method generates multiple candidate trajectories, and selects the best by incorporating sparse user input that specifies what kinds of deviations are acceptable. We apply our method to a range of challenging paths and show that our method generates solutions that achieve smooth, feasible motions while closely approximating the given pose goals and adhering to user specifications.

We present a discrete-optimization technique for finding feasible robot arm trajectories that pass through provided 6-DOF Cartesian-space end-effector paths with high accuracy, a problem called pathwise-inverse kinematics. The output from our method consists of a path function of joint-angles that best follows the provided end-effector path function, given some definition of “best”. Our method, called Stampede, casts the robot motion translation problem as a discrete-space graph-search problem where the nodes in the graph are individually solved for using non-linear optimization; framing the problem in such a way gives rise to a well-structured graph that affords an effective best path calculation using an efficient dynamic-programming algorithm. We present techniques for sampling configuration space, such as diversity sampling and adaptive sampling, to construct the search-space in the graph. Through an evaluation, we show that our approach performs well in finding smooth, feasible, collision-free robot motions that match the input end-effector trace with very high accuracy, while alternative approaches, such as a state-of-the-art per-frame inverse kinematics solver and a global non-linear trajectory-optimization approach, performed unfavorably.

Designing and implementing human-robot interactions requires numerous skills, from having a rich understanding of social interactions and the capacity to articulate their subtle requirements, to the ability to then program a social robot with the many facets of such a complex interaction. Although designers are best suited to develop and implement these interactions due to their inherent understanding of the context and its requirements, these skills are a barrier to enabling designers to rapidly explore and prototype ideas: it is impractical for designers to also be experts on social interaction behaviors, and the technical challenges associated with programming a social robot are prohibitive. In this work, we introduce Synthé, which allows designers to act out, or bodystorm, multiple demonstrations of an interaction. These demonstrations are automatically captured and translated into prototypes for the design team using program synthesis. We evaluate Synthé in multiple design sessions involving pairs of designers bodystorming interactions and observing the resulting models on a robot. We build on the findings from these sessions to improve the capabilities of Synthé and demonstrate the use of these capabilities in a second design session.

In this paper, we present a method that improves the ability of remote users to teleoperate amanipulation robot arm by continuously providing them with an effective viewpoint using a secondcamera-in-hand robot arm. The user controls the manipulation robot usinganyteleoperation interface, and the camera-in-hand robot automatically servos to provide a view of the remote environment that is estimated to best support effective manipulations. Our method avoids occlusions with the manipulation arm to improve visibility, provides context and detailed views of the environment by varying the camera-target distance, utilizes motion prediction to cover the space of the user»s next manipulation actions, and actively corrects views to avoid disorienting the user as the camera moves. Through two user studies, we show that our method improves teleoperation performance over alternative methods of providing visual support for teleoperation. We discuss the implications of our findings for real-world teleoperation and for future research.

In this paper, we present a novel shared-control telemanipulation method that is designed to incrementally improve a user»s motor ability. Our method initially corrects for the user»s suboptimal control trajectories, gradually giving the user more direct control over a series of training trials as he/she naturally gets more accustomed to the task. Our shared-control method, calledShared Dynamic Curves, blends suboptimal user translation and rotation control inputs with known translation and rotation paths needed to complete a task. Shared Dynamic Curves provide a translation and rotation path in space along which the user can easily guide the robot, and this curve can bend and flex in real-time as a dynamical system to pull the user»s motion gracefully toward a goal. We show through a user study that Shared Dynamic Curves affords effective motor learning on certain tasks compared to alternative training methods. We discuss our findings in the context of shared control and speculate on how this method could be applied in real-world scenarios such as job training or stroke rehabilitation.

How can we enable users to create effective, perception-driven task plans for collaborative robots? We conducted a 35-person user study with the Behavior Tree-based CoSTAR system to determine which strategies for end user creation of generalizable robot task plans are most usable and effctive. CoSTAR allows domain experts to author complex, perceptually grounded task plans for collaborative robots. As a part of CoSTAR's wide range of capabilities, it allows users to specify SmartMoves: abstract goals such as “pick up component A from the right side of the table.” Users were asked to perform pick-and-place assembly tasks with either SmartMoves or one of three simpler baseline versions of CoSTAR. Overall, participants found CoSTAR to be highly usable, with an average System Usability Scale score of 73.4 out of 100. SmartMove also helped users perform tasks faster and more effectively; all SmartMove users completed the first two tasks, while not all users completed the tasks using the other strategies. SmartMove users showed better performance for incorporating perception across all three tasks.

As social agents, robots designed for human interaction must adhere to human social norms. How can we enable designers, engineers, and roboticists to design robot behaviors that adhere to human social norms and do not result in interaction breakdowns? In this paper, we use automated formal-verification methods to facilitate the encoding of appropriate social norms into the interaction design of social robots and the detection of breakdowns and norm violations in order to prevent them. We have developed an authoring environment that utilizes these methods to provide developers of social-robot applications with feedback at design time and evaluated the benefits of their use in reducing such breakdowns and violations in human-robot interactions. Our evaluation with application developers (N=9) shows that the use of formal-verification methods increases designers' ability to identify and contextualize social-norm violations. We discuss the implications of our approach for the future development of tools for effective design of social-robot applications.

As collaborative robots begin to appear on factory floors, there is a need to consider how these robots can best help their human partners. In this paper, we propose an optimization framework that generates task assignments and schedules for a human-robot team with the goal of improving both time and ergonomics and demonstrate its use in six real-world manufacturing processes that are currently performed manually. Using the strain index method to quantify human physical stress, we create a set of solutions with assigned priorities on each goal. The resulting schedules provide engineers with insight into selecting the appropriate level of compromise and integrating the robot in a way that best fits the needs of an individual process.

Successful collaboration relies on the coordination and alignment of communicative cues. In this paper, we present mechanisms of bidirectional gaze - the coordinated production and detection of gaze cues - by which a virtual character can coordinate its gaze cues with those of its human user. We implement these mechanisms in a hybrid stochastic/heuristic model synthesized from data collected in human-human interactions. In three lab studies wherein a virtual character instructs participants in a sandwich-making task, we demonstrate how bidirectional gaze can lead to positive outcomes in error rate, completion time, and the agent's ability to produce quick, effective nonverbal references. The first study involved an on-screen agent and the participant wearing eye-tracking glasses. The second study demonstrates that these positive outcomes can be achieved using head-pose estimation in place of full eye tracking. The third study demonstrates that these effects also transfer into virtual-reality interactions.

In face-to-face interaction, moving with and mimicking the body movements of communication partners has been widely demonstrated to affect interpersonal processes, including feel- ings of affiliation and closeness. In this paper, we examine effects of movement and mimicry in robot-mediated communication. Participants were instructed to get to know their partner, a confederate, who interacted with them via a telepresence robot. The robot either (a) mimicked the participant's body orientation (mimicry condition), (b) mimicked pre-recorded movements of another participant (random movement condition), or (c) did not move during the interaction (static condition). Results showed that mimicry and random movement had similar effects on participants' perceptions of similarity and closeness to their partners and that these effects depend on the participant's gender and level of self-monitoring. The findings suggest that the social movements of a telepresence robot affect interpersonal processes and that these effects are shaped by individual differences.

The development of literacy and reading proficiency is a building block of lifelong learning that must be supported both in the classroom and at home. While the promise of interactive learning technologies has widely been demonstrated, little is known about how an interactive robot might play a role in this development. We used eight design features based on recommendations from interest-development and human-robot-interaction literatures to design an in-home learning companion robot for children aged 11--12. The robot was used as a technology probe to explore families' (N=8) habits and views about reading, how a reading technology might be used, and how children perceived reading with the robot. Our results indicate reading with the learning companion to be a way to socially engage with reading, which may promote the development of reading interest and ability. We discuss design and research implications based on our findings.

In this paper, we introduce a novel interface that allows novice users to effectively and intuitively tele-operate robot manipulators. The premise of our method is that an interface that allows its user to direct a robot arm using the natural 6-DOF space of his/her hand would afford effective direct control of the robot; however, a direct mapping between the user's hand and the robot's end effector is impractical because the robot has different kinematic and speed capabilities than the human arm. Our key technical idea that by relaxing the constraint of the direct mapping between hand position and orientation and end effector configuration, a system can provide the user with the feel of direct control, while still achieving the practical requirements for telemanipulation, such as motion smoothness and singularity avoidance. We present methods for implementing a motion retargeting solution that achieves this relaxed control using constrained optimization and describe a system that utilizes it to provide real-time control of a robot arm. We demonstrate the effectiveness of our approach in a user study that shows novice users can complete a range of tasks more efficiently and enjoyably using our relaxed-mimicry based interface compared to standard interfaces.

Interactions with simulated robots are typically presented on screens. Virtual reality (VR) offers an attractive alternative as it provides visual cues that are more similar to the real world. In this paper, we explore how virtual reality mediates human-robot interactions through two user studies. The first study shows that in situations where perception of the robot is challenging, a VR display provides significantly improved performance on a collaborative task. The second study shows that this improved performance is primarily due to stereo cues. Together, the findings of these studies suggest that VR displays can offer users unique perceptual benefits in simulated robotics applications.

Efficient collaboration requires collaborators to monitor the behaviors of their partners, make inferences about their task intent, and plan their own actions accordingly. To work seamlessly and efficiently with their human counterparts, robots must similarly rely on predictions of their users' intent in planning their actions. In this paper, we present an anticipatory control method that enables robots to proactively perform task actions based on anticipated actions of their human partners. We implemented this method into a robot system that monitored its user's gaze, predicted his or her task intent based on observed gaze patterns, and performed anticipatory task actions according to its predictions. Results from a human-robot interaction experiment showed that anticipatory control enabled the robot to respond to user requests and complete the task faster-2.5 seconds on average and up to 3.4 seconds-compared to a robot using a reactive control method that did not anticipate user intent. Our findings highlight the promise of performing anticipatory actions for achieving efficient human-robot teamwork.

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.

Planning effective arm motions is integral to manipulation tasks. In general, motion synthesis methods have focused on functional objectives, such as minimizing time and maximizing efficiency. However, recent work in human-robot collaboration suggests that choices in motion design can influence collaboration performance and quality. Some motion designs are easier than others for human observers to interpret. In this paper, we explore the tradeoffs in robot arm movements designed to be observed by people. Through a series of human-subjects experiments, we compare collaboration performance between several motion-synthesis methods explored by prior work. We find that a number of factors, including the design of the robot arm and metric for success, affect the relative merits of different approaches.

Monitoring, analyzing, or comparing the motions of a robot can be a critical activity but a tedious and inefficient one in research settings and practical applications. In this paper, we present an approach we call motion synopsis for providing users with a global view of a robot's motion trajectory as a set of key poses in a static 2D image, allowing for more efficient robot motion review, preview, analysis, and comparisons. To accomplish this presentation, we construct a 3D scene, select a camera view direction and position based on the motion data, decide what interior poses should be shown based on robot motion features, and organize the robot mesh models and graphical information in a way that provides the user with an at-a-glance view of the motion. Through examples and a user study, we document how our approach performs against alternative summarization techniques and highlight where the approach offers benefit and where it is limited.

We present an approach for adding directed gaze movements to characters animated using full-body motion capture. Our approach provides a comprehensive authoring solution that automatically infers plausible directed gaze from the captured body motion, provides convenient controls for manual editing, and adds synthetic gaze movements onto the original motion. The foundation of the approach is an abstract representation of gaze behavior as a sequence of gaze shifts and fixations toward targets in the scene. We present methods for automatic inference of this representation by analyzing the head and torso kinematics and scene features. We introduce tools for convenient editing of the gaze sequence and target layout that allow an animator to adjust the gaze behavior without worrying about the details of pose and timing. A synthesis component translates the gaze sequence into coordinated movements of the eyes, head, and torso, and blends these with the original body motion. We evaluate the effectiveness of our inference methods, the efficiency of the authoring process, and the quality of the resulting animation.

Socially assistive robots are envisioned to provide social and cognitive assistance where they will seek to motivate and engage people in therapeutic activities. Due to their physicality, robots serve as a powerful technology for motivating people. Prior work has shown that effective motivation requires adaption to user needs and characteristics, but how robots might successfully achieve such adaptation is still unknown. In this paper, we present work on matching a robot's personality-expressed via its gaze behavior-to that of its users. We confirmed in an online study with 22 participants that the robot's gaze behavior can successfully express either an extroverted or introverted personality. In a laboratory study with 40 participants, we demonstrate the positive effect of personality matching on a user's motivation to engage in a repetitive task. These results have important implications for the design of adaptive robot behaviors in assistive human-robot interaction.

Robotic telepresence systems-videoconferencing systems that allow a remote user to drive around in another location-are an emerging technology for supporting geographically-distributed teams. Thus far, many of these systems rely on affordances designed for stationary systems, such as a single, narrow-view camera to provide vision for the remote user. Teleoperation has offered some solutions to this via an augmented field-of-view, but how these solutions support task outcomes in collaborative mobile telepresence tasks has yet to be understood. To investigate this, we conducted a three condition (field-of-view: narrow (45°) vs. wide-angle (180°) vs. panoramic (360°)) between-participants controlled laboratory experiment. We asked participants (N=24) to collaborate with a confederate via a robotic telepresence system while using one of these views in a redecoration task. Our results showed that wider views supported task efficiency and fewer collisions, but were perceived as more difficult to use.

Across history and cultures, robots have been envisioned as assistants working alongside people. Following this vision, an emerging family of products-collaborative manufacturing robots-is enabling human and robot workers to work side by side as collaborators in manufacturing tasks. Their introduction presents an opportunity to better understand people's interactions with and perceptions of a robot "co-worker" in a real-world setting to guide the design of these products. In this paper, we present findings from an ethnographic field study at three manufacturing sites and a Grounded Theory analysis of observations and interviews. Our results show that, even in this safety-critical manufacturing setting, workers relate to the robot as a social entity and rely on cues to understand the robot's actions, which we observed to be critical for workers to feel safe when near the robot. These findings contribute to our understanding of interactions with robotic products in real-world settings and offer important design implications.

Emerging wearable and mobile communication technologies, such as lightweight head-mounted displays (HMDs) and handheld devices, promise support for everyday remote collaboration. Despite their potential for widespread use, their effectiveness as collaborative tools is unknown, particularly in physical tasks involving mobility. To better understand their impact on collaborative behaviors, perceptions, and performance, we conducted a two-by-two (technology type: HMD vs. tablet computer; task setting: static vs. dynamic) between-subjects study where participants (n=66) remotely collaborated as ``helper' and ``worker' pairs in the construction of a physical object. Our results showed that, in the dynamic task, HMD use enabled helpers to offer more frequent directing commands and more proactive assistance, resulting in marginally faster task completion. In the static task, while tablet use helped convey subtle visual information, helpers and workers had conflicting perceptions of how the two technologies contributed to their success. Our findings offer strong design and research implications, underlining the importance of a consistent view of the shared workspace and the differential support collaborators with different roles receive from technologies.

As social robots begin to enter our lives as providers of information, assistance, companionship, and motivation, it becomes increasingly important that these robots are capable of interacting effectively with human users across different cultural settings worldwide. A key capability in establishing acceptance and usability is the way in which robots structure their speech to build credibility and express information in a meaningful and persuasive way. Previous work has established that robots can use speech to improve credibility in two ways: expressing practical knowledge and using rhetorical linguistic cues. In this paper, we present two studies that build on prior work to explore the effects of language and cultural context on the credibility of robot speech. In the first study (n=96), we compared the relative effectiveness of knowledge and rhetoric on the credibility of robot speech between Arabic-speaking robots in Lebanon and English-speaking robots in the USA, finding the rhetorical linguistic cues to be more important in Arabic than in English. In the second study (n=32), we compared the effectiveness of credible robot speech between robots speaking either Modern Standard Arabic or the local Arabic dialect, finding the expression of both practical knowledge and rhetorical ability to be most important when using the local dialect. These results reveal nuanced cultural differences in perceptions of robots as credible agents and have important implications for the design of human-robot interactions across Arabic and Western cultures.

Small flying robots represent a rapidly emerging family of robotic technologies with aerial capabilities that enable unique forms of assistance in a variety of collaborative tasks. Such tasks will necessitate interaction with humans in close proximity, requiring that designers consider human perceptions regarding robots flying and acting within human environments. We explore the design space regarding explicit robot communication of flight intentions to nearby viewers. We apply design constraints to robot flight behaviors, using biological and airplane flight as inspiration, and develop a set of signaling mechanisms for visually communicating directionality while operating under such constraints. We implement our designs on two commercial flyers, requiring little modification to the base platforms, and evaluate each signaling mechanism, as well as a no-signaling baseline, in a user study in which participants were asked to predict robot intent. We found that three of our designs significantly improved viewer response time and accuracy over the baseline and that the form of the signal offered tradeoffs in precision, generalizability, and perceived robot usability.

This paper details the creation of a two-day computer science and robotics outreach course aimed at simultaneously engaging youth (children, ages 9-14) and senior (their grandparents, ages 55+) students. Our goal is to encourage enthusiasm for science and technology in students of all ages as well as provide practical instruction regarding common computer science concepts, including variables, loops, and boolean logic. To this end, we ground our course in the emerging field of social robotics, which enables the design of several multidisciplinary hands-on activities for students. We report on a four-year experience in the development of our course, which has been offered twelve times and involved over 210 youth and senior students. Our work presents a discussion regarding the challenges in designing a course for students from diverse ages, guidelines for creating similar courses, and a reflection on how we might improve our own class. The activities and project code developed for our course are available online as open-source resources.

Abstract A person's emotions and state of mind are apparent in their face and eyes. As a Latin proverb states: ‘The face is the portrait of the mind; the eyes, its informers’. This presents a significant challenge for Computer Graphics researchers who generate artificial entities that aim to replicate the movement and appearance of the human eye, which is so important in human–human interactions. This review article provides an overview of the efforts made on tackling this demanding task. As with many topics in computer graphics, a cross‐disciplinary approach is required to fully understand the workings of the eye in the transmission of information to the user. We begin with a discussion of the movement of the eyeballs, eyelids and the head from a physiological perspective and how these movements can be modelled, rendered and animated in computer graphics applications. Furthermore, we present recent research from psychology and sociology that seeks to understand higher level behaviours, such as attention and eye gaze, during the expression of emotion or during conversation. We discuss how these findings are synthesized in computer graphics and can be utilized in the domains of Human–Robot Interaction and Human–Computer Interaction for allowing humans to interact with virtual agents and other artificial entities. We conclude with a summary of guidelines for animating the eye and head from the perspective of a character animator.

To facilitate natural interactions between humans and embodied conversational agents (ECAs), we need to endow the latter with the same nonverbal cues that humans use to communicate. Gaze cues in particular are integral in mechanisms for communication and management of attention in social interactions, which can trigger important social and cognitive processes, such as establishment of affiliation between people or learning new information. The fundamental building blocks of gaze behaviors are gaze shifts : coordinated movements of the eyes, head, and body toward objects and information in the environment. In this article, we present a novel computational model for gaze shift synthesis for ECAs that supports parametric control over coordinated eye, head, and upper body movements. We employed the model in three studies with human participants. In the first study, we validated the model by showing that participants are able to interpret the agent’s gaze direction accurately. In the second and third studies, we showed that by adjusting the participation of the head and upper body in gaze shifts, we can control the strength of the attention signals conveyed, thereby strengthening or weakening their social and cognitive effects. The second study shows that manipulation of eye--head coordination in gaze enables an agent to convey more information or establish stronger affiliation with participants in a teaching task, while the third study demonstrates how manipulation of upper body coordination enables the agent to communicate increased interest in objects in the environment.

Robotic telepresence systems - videoconferencing systems that allow a remote user to drive around in another location - provide an alternative to video-mediated communications as a way of interacting over distances. These systems, which are seeing increasing use in business and medical settings, are unique in their ability to grant the remote user the ability to maneuver in a distant location. While this mobility promises increased feelings of "being there" for remote users and thus greater support for task collaboration, whether these promises are borne out, providing benefits in task performance, is unknown. To better understand the role that mobility plays in shaping the remote user's sense of presence and its potential benefits, we conducted a two-by-two (system mobility: stationary vs. mobile; task demands for mobility: low vs. high) controlled laboratory experiment. We asked participants (N=40) to collaborate in a construction task with a confederate via a robotic telepresence system. Our results showed that mobility significantly increased the remote user's feelings of presence, particularly in tasks with high mobility requirements, but decreased task performance. Our findings highlight the positive effects of mobility on feelings of "being there," while illustrating the need to design support for effective use of mobility in high-mobility tasks.

Robotic products are envisioned to offer rich interactions in a range of environments. While their specific roles will vary across applications, these products will draw on fundamental building blocks of interaction, such as greeting people, narrating information, providing instructions, and asking and answering questions. In this paper, we explore how such building blocks might serve as interaction design patterns that enable design exploration and prototyping for human-robot interaction. To construct a pattern library, we observed human interactions across different scenarios and identified seven patterns, such as question-answer pairs. We then designed and implemented Interaction Blocks, a visual authoring environment that enabled prototyping of robot interactions using these patterns. Design sessions with designers and developers demonstrated the promise of using a pattern language for designing robot interactions, confirmed the usability of our authoring environment, and provided insights into future research on tools for human-robot interaction design.

To design computer-supported collaborative work (CSCW) systems that effectively support remote collaboration, designers need a better understanding of how people collaborate face-to-face and the mechanisms that they use to coordinate their actions. While research in CSCW has studied how specific social cues might facilitate collaboration in specific tasks, such as the role of gestures in video instruction, less is known about how a range of communicative cues might facilitate activities across many collaborative settings. In this paper, we model the predictive relationships between facial, gestural, and vocal cues and collaborative outcomes in three different tasks, drawing conclusions on how each cue might contribute to these outcomes in a given task and how such relationships generalize across tasks. The resulting models provide a quantitative understanding of the relative importance of each type of social cue in predicting collaborative outcomes, as well as a more thorough understanding of how the role of each social cue changes across tasks. Additionally, our results provide confirmation and illumination of prior findings in face-to-face and computer-mediated communication research.

In order to communicate with their users in a natural and effective manner, humanlike robots must seamlessly integrate behaviors across multiple modalities, including speech, gaze, and gestures. While researchers and designers have successfully drawn on studies of human interactions to build models of humanlike behavior and to achieve such integration in robot behavior, the development of such models involves a laborious process of inspecting data to identify patterns within each modality or across modalities of behavior and to represent these patterns as "rules" or heuristics that can be used to control the behaviors of a robot, but provides little support for validation, extensibility, and learning. In this paper, we explore how a learning-based approach to modeling multimodal behaviors might address these limitations. We demonstrate the use of a dynamic Bayesian network (DBN) for modeling how humans coordinate speech, gaze, and gesture behaviors in narration and for achieving such coordination with robots. The evaluation of this approach in a human-robot interaction study shows that this learning-based approach is comparable to conventional modeling approaches in enabling effective robot behaviors while reducing the effort involved in identifying behavioral patterns and providing a probabilistic representation of the dynamics of human behavior. We discuss the implications of this approach for designing natural, effective multimodal robot behaviors.

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.

No abstract available.

As robots collaborate with humans in increasingly diverse environments, they will need to effectively refer to objects of joint interest and adapt their references to various physical, environmental, and task conditions. Humans use a broad range of deictic gestures-gestures that direct attention to collocated objects, persons, or spaces-that include pointing, touching, and exhibiting to help their listeners understand their references. These gestures offer varying levels of support under different conditions, making some gestures more or less suitable for different settings. While these gestures offer a rich space for designing communicative behaviors for robots, a better understanding of how different deictic gestures affect communication under different conditions is critical for achieving effective human-robot interaction. In this paper, we seek to build such an understanding by implementing six deictic gestures on a humanlike robot and evaluating their communicative effectiveness in six diverse settings that represent physical, environmental, and task conditions under which robots are expected to employ deictic communication. Our results show that gestures which come into physical contact with the object offer the highest overall communicative accuracy and that specific settings benefit from the use of particular types of gestures. Our results highlight the rich design space for deictic gestures and inform how robots might adapt their gestures to the specific physical, environmental, and task conditions.

Assistive free-flyers (AFFs) are an emerging robotic platform with unparalleled flight capabilities that appear uniquely suited to exploration, surveillance, inspection, and telepresence tasks. However, unconstrained aerial movements may make it difficult for colocated operators, collaborators, and observers to understand AFF intentions, potentially leading to difficulties understanding whether operator instructions are being executed properly or to safety concerns if future AFF motions are unknown or difficult to predict. To increase AFF usability when working in close proximity to users, we explore the design of natural and intuitive flight motions that may improve AFF abilities to communicate intent while simultaneously accomplishing task goals. We propose a formalism for representing AFF flight paths as a series of motion primitives and present two studies examining the effects of modifying the trajectories and velocities of these flight primitives based on natural motion principles. Our first study found that modified flight motions might allow AFFs to more effectively communicate intent and, in our second study, participants preferred interacting with an AFF that used a manipulated flight path, rated modified flight motions as more natural, and felt safer around an AFF with modified motion. Our proposed formalism and findings highlight the importance of robot motion in achieving effective human-robot interactions.

Communication technologies are becoming increasingly diverse in form and functionality, making it important to identify which aspects of these technologies actually improve geographically distributed communication. Our study examines two potentially important aspects of communication technologies which appear in robot-mediated communication - physical embodiment and control of this embodiment. We studied the impact of physical embodiment and control upon interpersonal trust in a controlled laboratory experiment using three different videoconferencing settings: (1) a handheld tablet controlled by a local user, (2) an embodied system controlled by a local user, and (3) an embodied system controlled by a remote user (n = 29 dyads). We found that physical embodiment and control by the local user increased the amount of trust built between partners. These results suggest that both physical embodiment and control of the system influence interpersonal trust in mediated communication and have implications for future system designs.

Internet technology is revolutionizing education. Teachers are developing massive open online courses (MOOCs) and using innovative practices such as flipped learning in which students watch lectures at home and engage in hands-on, problem solving activities in class. This work seeks to explore the design space afforded by these novel educational paradigms and to develop technology for improving student learning. Our design, based on the technique of adaptive content review, monitors student attention during educational presentations and determines which lecture topic students might benefit the most from reviewing. An evaluation of our technology within the context of an online art history lesson demonstrated that adaptively reviewing lesson content improved student recall abilities 29% over a baseline system and was able to match recall gains achieved by a full lesson review in less time. Our findings offer guidelines for a novel design space in dynamic educational technology that might support both teachers and online tutoring systems.

MACH--My Automated Conversation coacH--is a novel system that provides ubiquitous access to social skills training. The system includes a virtual agent that reads facial expressions, speech, and prosody and responds with verbal and nonverbal behaviors in real time. This paper presents an application of MACH in the context of training for job interviews. During the training, MACH asks interview questions, automatically mimics certain behavior issued by the user, and exhibit appropriate nonverbal behaviors. Following the interaction, MACH provides visual feedback on the user's performance. The development of this application draws on data from 28 interview sessions, involving employment-seeking students and career counselors. The effectiveness of MACH was assessed through a weeklong trial with 90 MIT undergraduates. Students who interacted with MACH were rated by human experts to have improved in overall interview performance, while the ratings of students in control groups did not improve. Post-experiment interviews indicate that participants found the interview experience informative about their behaviors and expressed interest in using MACH in the future.

Abstract Existing models of gaze motion for character animation simulate human movements, incorporating anatomical, neurophysiological, and functional constraints. While these models enable the synthesis of humanlike gaze motion, they only do so in characters that conform to human anatomical proportions, causing undesirable artifacts such as cross‐eyedness in characters with non‐human or exaggerated human geometry. In this paper, we extend a state‐of‐the‐art parametric model of human gaze motion with control parameters for specifying character geometry, gaze dynamics, and performative characteristics in order to create an enhanced model that supports gaze motion in characters with a wide range of geometric properties that is free of these artifacts. The model also affords “staging effects” by offering softer functional constraints and more control over the appearance of the character's gaze movements. An evaluation study showed that the model, compared with the state‐of‐the‐art model, creates gaze motion with fewer artifacts in characters with non‐human or exaggerated human geometry while retaining their naturalness and communicative accuracy.

In social interaction, people draw on a large repertoire of social acts tailoring their use of these acts to meet the demands of the social situation and to achieve the goals of the interaction. This paper presents an approach to creating such a repertoire of social acts for robots and enabling designers to specify the social situation to which robots may adapt their behaviors. Drawing on principles of Activity Theory and social-scientific findings on human social behavior, this paper introduces an implementation of this approach---the Robot Behavior Toolkit---and two studies that use a limited, proof-of-concept repertoire of specifications for gaze cues to demonstrate the feasibility of this approach for controlling robot gaze behavior. The first study assessed the feasibility of the use of this repertoire, comparing it to alternative, baseline repertoires in two human-robot interaction tasks, and found that it enabled the robot to more effectively support the interaction goals. The second study investigated the feasibility of the robot adapting its use of the repertoire to a social situation by comparing different goal specifications in two human-robot interaction tasks. The results showed that these specifications enabled the robot to achieve some of its task and communicative goals, although participant gender strongly affected whether the robot elicited these interaction outcomes.

Virtual agents hold great promise in human-computer interaction with their ability to afford embodied interaction using nonverbal human communicative cues. Gaze cues are particularly important to achieve significant high-level outcomes such as improved learning and feelings of rapport. Our goal is to explore how agents might achieve such outcomes through seemingly subtle changes in gaze behavior and what design variables for gaze might lead to such positive outcomes. Drawing on research in human physiology, we developed a model of gaze behavior to capture these key design variables. In a user study, we investigated how manipulations in these variables might improve affiliation with the agent and learning. The results showed that an agent using affiliative gaze elicited more positive feelings of connection, while an agent using referential gaze improved participants' learning. Our model and findings offer guidelines for the design of effective gaze behaviors for virtual agents.

As an emerging technology that enables geographically distributed work teams, mobile remote presence (MRP) systems present new opportunities for supporting effective team building and collaboration. MRP systems are physically embodied mobile videoconferencing systems that remote co-workers control. These systems allow remote users, pilots, to actively initiate conversations and to navigate throughout the local environment. To investigate ways of encouraging team-like behavior among local and remote co-workers, we conducted a 2 (visual framing: decoration vs. no decoration) x 2 (verbal framing: interdependent vs. independent performance scoring) between-participants study (n=40). We hypothesized that verbally framing the situation as interdependent and visually framing the MRP system to create a sense of self-extension would enhance group cohesion between the local and the pilot. We found that the interdependent framing was successful in producing more in-group oriented behaviors and, contrary to our predictions, visual framing of the MRP system weakened team cohesion.

Embodied agents hold great promise as educational assistants, exercise coaches, and team members in collaborative work. These roles require agents to closely monitor the behavioral, emotional, and mental states of their users and provide appropriate, effective responses. Educational agents, for example, will have to monitor student attention and seek to improve it when student engagement decreases. In this paper, we draw on techniques from brain-computer interfaces (BCI) and knowledge from educational psychology to design adaptive agents that monitor student attention in real time using measurements from electroencephalography (EEG) and recapture diminishing attention levels using verbal and nonverbal cues. An experimental evaluation of our approach showed that an adaptive robotic agent employing behavioral techniques to regain attention during drops in engagement improved student recall abilities 43% over the baseline regardless of student gender and significantly improved female motivation and rapport. Our findings offer guidelines for developing effective adaptive agents, particularly for educational settings.

The purpose of this half-day workshop is to explore the role of social gaze in human-robot interaction, both how to measure social gaze behavior by humans and how to implement it in robots that interact with them. Gaze directed at an interaction partner has become a subject of increased attention in human-robot interaction research. While traditional robotics research has focused work on robot gaze solely on the identification and manipulation of objects, researchers in HRI have come to recognize that gaze is a social behavior in addition to a way of sensing the world. This workshop will approach the problem of understanding the role of social gaze in human-robot interaction from the dual perspectives of investigating human-human gaze for design principles to apply to robots and of experimentally evaluating human-robot gaze interaction in order to assess how humans engage in gaze behavior with robots.

Social robots have to potential to serve as personal, organizational, and public assistants as, for instance, diet coaches, teacher's aides, and emergency respondents. The success of these robots - whether in motivating users to adhere to a diet regimen or in encouraging them to follow evacuation procedures in the case of a fire - will rely largely on their ability to persuade people. Research in a range of areas from political communication to education suggest that the nonverbal behaviors of a human speaker play a key role in the persuasiveness of the speaker's message and the listeners' compliance with it. In this paper, we explore how a robot might effectively use these behaviors, particularly vocal and bodily cues, to persuade users. In an experiment with 32 participants, we evaluate how manipulations in a robot's use of nonverbal cues affected participants' perceptions of the robot's persuasiveness and their compliance with the robot's suggestions across four conditions: (1) no vocal or bodily cues, (2) vocal cues only, (3) bodily cues only, and (4) vocal and bodily cues. The results showed that participants complied with the robot's suggestions significantly more when it used nonverbal cues than they did when it did not use these cues and that bodily cues were more effective in persuading participants than vocal cues were. Our model of persuasive nonverbal cues and experimental results have direct implications for the design of persuasive behaviors for humanlike robots.

Social interaction involves a large number of patterned behaviors that people employ to achieve particular communicative goals. To achieve fluent and effective humanlike communication, robots must seamlessly integrate the necessary social behaviors for a given interaction context. However, very little is known about how robots might be equipped with a collection of such behaviors and how they might employ these behaviors in social interaction. In this paper, we propose a framework that guides the generation of social behavior for humanlike robots by systematically using specifications of social behavior from the social sciences and contextualizing these specifications in an Activity-Theory-based interaction model. We present the Robot Behavior Toolkit, an open-source implementation of this framework as a Robot Operating System (ROS) module and a community-based repository for behavioral specifications, and an evaluation of the effectiveness of the Toolkit in using these specifications to generate social behavior in a human-robot interaction study, focusing particularly on gaze behavior. The results show that specifications from this knowledge base enabled the Toolkit to achieve positive social, cognitive, and task outcomes, such as improved information recall, collaborative work, and perceptions of the robot.

During conversations, speakers employ a number of verbal and nonverbal mechanisms to establish who participates in the conversation, when, and in what capacity. Gaze cues and mechanisms are particularly instrumental in establishing the participant roles of interlocutors, managing speaker turns, and signaling discourse structure. If humanlike robots are to have fluent conversations with people, they will need to use these gaze mechanisms effectively. The current work investigates people's use of key conversational gaze mechanisms, how they might be designed for and implemented in humanlike robots, and whether these signals effectively shape human-robot conversations. We focus particularly on whether humanlike gaze mechanisms might help robots signal different participant roles, manage turn-exchanges, and shape how interlocutors perceive the robot and the conversation. The evaluation of these mechanisms involved 36 trials of three-party human-robot conversations. In these trials, the robot used gaze mechanisms to signal to its conversational partners their roles either of two addressees, an addressee and a bystander, or an addressee and a nonparticipant. Results showed that participants conformed to these intended roles 97% of the time. Their conversational roles affected their rapport with the robot, feelings of groupness with their conversational partners, and attention to the task.

To seamlessly integrate into the human physical and social environment, robots must display appropriate proxemic behavior - that is, follow societal norms in establishing their physical and psychological distancing with people. Social-scientific theories suggest competing models of human proxemic behavior, but all conclude that individuals' proxemic behavior is shaped by the proxemic behavior of others and the individual's psychological closeness to them. The present study explores whether these models can also explain how people physically and psychologically distance themselves from robots and suggest guidelines for future design of proxemic behaviors for robots. In a controlled laboratory experiment, participants interacted with Wakamaru to perform two tasks that examined physical and psychological distancing of the participants. We manipulated the likeability (likeable/dislikeable) and gaze behavior (mutual gaze/averted gaze) of the robot. Our results on physical distancing showed that participants who disliked the robot compensated for the increase in the robot's gaze by maintaining a greater physical distance from the robot, while participants who liked the robot did not differ in their distancing from the robot across gaze conditions. The results on psychological distancing suggest that those who disliked the robot also disclosed less to the robot. Our results offer guidelines for the design of appropriate proxemic behaviors for robots so as to facilitate effective human-robot interaction.

We study the empirical strategies that humans follow as they teach a target concept with a simple 1D threshold to a robot. Previous studies of computational teaching, particularly the teaching dimension model and the curriculum learning principle, offer contradictory predictions on what optimal strategy the teacher should follow in this teaching task. We show through behavioral studies that humans employ three distinct teaching strategies, one of which is consistent with the curriculum learning principle, and propose a novel theoretical framework as a potential explanation for this strategy. This framework, which assumes a teaching goal of minimizing the learner's expected generalization error at each iteration, extends the standard teaching dimension model and offers a theoretical justification for curriculum learning.

During conversations, speakers establish their and others' participant roles (who participates in the conversation and in what capacity)--or "footing" as termed by Goffman-using gaze cues. In this paper, we study how a robot can establish the participant roles of its conversational partners using these cues. We designed a set of gaze behaviors for Robovie to signal three kinds of participant roles: addressee, bystander, and overhearer. We evaluated our design in a controlled laboratory experiment with 72 subjects in 36 trials. In three conditions, the robot signaled to two subjects, only by means of gaze, the roles of (1) two addressees, (2) an addressee and a bystander, or (3) an addressee and an overhearer. Behavioral measures showed that subjects' participation behavior conformed to the roles that the robot communicated to them. In subjective evaluations, significant differences were observed in feelings of groupness between addressees and others and liking between overhearers and others. Participation in the conversation did not affect task performance-measured by recall of information presented by the robot-but affected subjects' ratings of how much they attended to the task.

Human communication involves a number of nonverbal cues that are seemingly unintentional, unconscious, and automatic-both in their production and perception-and convey rich information on the emotional state and intentions of an individual. One family of such cues is called "nonverbal leakage." In this paper, we explore whether people can read nonverbal leakage cues-particularly gaze cues-in humanlike robots and make inferences on robots' intentions, and whether the physical design of the robot affects these inferences. We designed a gaze cue for Geminoid-a highly humanlike android-and Robovie-a robot with stylized, abstract humanlike features-that allowed the robots to "leak" information on what they might have in mind. In a controlled laboratory experiment, we asked participants to play a game of guessing with either of the robots and evaluated how the gaze cue affected participants' task performance. We found that the gaze cue did, in fact, lead to better performance, from which we infer that the cue led to attributions of mental states and intentionality. Our results have implications for robot design, particularly for designing expression of intentionality, and for our understanding of how people respond to human social cues when they are enacted by robots.

Robots are becoming increasingly integrated into the workplace, impacting organizational structures and processes, and affecting products and services created by these organizations. While robots promise significant benefits to organizations, their introduction poses a variety of design challenges. In this paper, we use ethnographic data collected at a hospital using an autonomous delivery robot to examine how organizational factors affect the way its members respond to robots and the changes engendered by their use. Our analysis uncovered dramatic differences between the medical and post-partum units in how people integrated the robot into their workflow and their perceptions of and interactions with it. Different patient profiles in these units led to differences in workflow, goals, social dynamics, and the use of the physical environment. In medical units, low tolerance for interruptions, a discrepancy between the perceived cost and benefits of using the robot, and breakdowns due to high traffic and clutter in the robot's path caused the robot to have a negative impact on the workflow and staff resistance. On the contrary, post-partum units integrated the robot into their workflow and social context. Based on our findings, we provide design guidelines for the development of robots for organizations.

In this paper, we present a methodology for recognizing seated postures using data from pressure sensors installed on a chair. Information about seated postures could be used to help avoid adverse effects of sitting for long periods of time, or to predict a user’s activities as input to a humancomputer interface. Our approach to posture recognition avoids the use of expensive hardware and complex prediction algorithms while providing recognition for users, for whom the classifier is not trained, using a near-optimal sensor placement strategy. We evaluated the performance of our technology in a series of empirical evaluations including (1) cross-validation experiments (classification accuracy of 87% for ten postures), and (2) a physical deployment of our system (78% classification accuracy).

Designers generally agree that understanding the context of use is important in designing products. However, technologically advanced products such as personal robots engender complex contextual characteristics that are not yet well understood. The social context of use shapes the roles that the user and the product play in the interaction. For instance, an intelligent agent that acts as a coach for an exercise program and one that supervises a physical rehabilitation regimen for the physically challenged function in different social contexts. Only a few studies to date have considered the social context of use as part of the design. My research proposes a conceptual framework for understanding the critical social aspects of interaction with products such as the social context of use. I combine interaction design and social science methodology to make an evaluation of my framework with a series of empirical studies. Author Keywords Interaction design, design methodology, social interaction, social context of use, user attributes, social products, personal robots

In this paper, we explore how dynamic visual cues can be used to create accessible and meaningful social interfaces without raising expectations beyond what is achievable with current technology. Our approach is inspired by research in perceptual causality, which suggests that simple displays in motion can evoke high-level social and emotional content. For our exploration, we iteratively designed and implemented a public social interface using abstraction and motion as design elements. Our interface communicated simple social and emotional content such as displaying happiness when there is high social interaction in the environment. Our qualitative evaluations showed that people frequently and repeatedly interacted with the interface while they tried to make sense of the underlying social content. They also shared their models with others, which led to more social interaction in the environment.

Recent developments in humanoid robotics have made possible a vision of robots in everyday use in the home and workplace. However, little is known about how we should design social interactions with humanoid robots. We explored how co-operation versus competition in a game shaped people's perceptions of ASIMO. We found that in the co-operative interaction, people found the robot more sociable and more intellectual than in the competitive interaction while people felt more positive and were more involved in the task in the competitive condition than in the co-operative condition. Our poster presents these findings with the supporting theoretical background.

Recent developments in humanoid robotics have made possible technologically advanced robots and a vision for their everyday use as assistants in the home and workplace. Nonetheless, little is known about how we should design interactions with humanoid robots. In this paper, we argue that adaptation for user attributes (in particular gender) and task structure (in particular a competitive vs. a cooperative structure) are key design elements. We experimentally demonstrate how these two elements affect the user's social perceptions of ASIMO after playing an interactive video game with him