Eye contact between strangers, even fleeting, can spark interaction and foster connection, happiness, and belonging. Yet in public spaces, such encounters are often suppressed by “civil inattention,” with many people absorbed in their phones. We explore how reconfiguring the ambient environment with MirrorBot, a mobile robot with adaptive mirrors, can encourage social encounters by subtly redirecting glances. By shifting reflections between self- and mutual recognition, MirrorBot invites serendipitous eye contact, shared awareness, and low-stakes engagement. In a controlled 2×2 between-subjects study with 90 participants (45 dyads) across four conditions (MirrorBot, Bot-only, Mirror-only, and Control), we found that MirrorBot led participants to initiate conversation more often, feel greater closeness and togetherness, and have more enjoyable interactions. Our findings position robots not only as social agents but as socio-spatial interfaces that choreograph sight lines and shared attention in physical space, opening new possibilities for technologies that cultivate human connection in public life.

Brief eye contact with strangers can foster connection, belonging, and positive affect, yet such moments are often scarce in public spaces. This paper investigates how a spatially situated robot can reshape the visual field of a shared space to influence how strangers notice and respond to one another. We present MirrorBot, a mobile robot equipped with two actuated mirrors that dynamically redirect reflections to reshape sightlines between people. In a study with 32 strangers in 16 pairs in a waiting-room setting, MirrorBot elicited patterns such as low-stakes icebreaking, nonverbal synchrony, joint sensemaking, asymmetric engagement, and avoidance. Participants also attributed multiple roles to the robot, such as mediator, observer, magnifier, or disrupter, revealing that its social meaning was fluid and co-constructed. Our work extends HRI by showing that robots can act not only as conversational partners but also as spatial mediators, curating opportunities for human–human connection through the reconfiguration of spatial relationships

The question “what is a robot?” has long been contested as automated embodied systems encompass many forms. We examine this fundamental question in Human-Robot Interaction through the case of Major League Baseball’s “robot umpire,” officially known as the Automated Ball-Strike System (ABS). Drawing on the concept of “fluid technology,” we analyze how the robot umpire is not a fixed technological artifact but a fluid sociotechnical assemblage whose definition and function are continuously negotiated. Through ethnographic fieldwork and interviews with stakeholders across the baseball ecosystem, we demonstrate that the robot umpire’s physical boundaries, operational parameters, and authorship remain contested and evolving, shaped by ongoing interactions between technology developers, league officials, umpires, players, and fans. Our findings reveal that treating robots as fluid technologies—rather than as discrete objects—opens new possibilities for understanding human-robot relationships. We contribute both theoretical insights regarding the ontological flexibility of “robots” and methodological approaches for studying and designing robots as sociotechnical assemblages.

This paper presents a virtual reality (VR) study that examines how multiple road users jointly interact with an autonomous vehicle (AV) in complex traffic scenarios. Moving beyond dyadic studies (e.g., AV-pedestrian or AV-passenger), our multi-user setup simulates ambiguous all-way stop intersections involving a pedestrian, a human driver in a conventional vehicle, and a passenger in an AV, all interacting simultaneously with the AV. We investigated how users perceive and respond to two distinct types of AV behaviors: an efficient AV that proceeds as soon as it is safe to do so, and a prosocial AV that yields to others before entering the intersection. Sixteen groups of three participants (N=48) took part in the study, with each group interacting with a single AV type across four ambiguous traffic scenarios. Our findings show that even simple AV behavior logics can meaningfully shape crossing negotiation dynamics and highlight how trust and perception can vary across different user roles. We conclude by discussing how our methods and insights can inform the research and design of AV interactions in complex multi-agent traffic environments.

This study investigates the application of real-world Reinforcement Learning (RL) in facilitating synchronized movement and fostering social connections during initial interactions between strangers. We introduced a novel, projection-based system where an RL agent is projected onto a table to interact with participants through its movements. Our research involved the development and testing of a Deep Q-Network (DQN) integrated with a Long Short-Term Memory (LSTM) model that identifies synchronized movements and determines the reward for the DQN. The preliminary results showcase the effectiveness of the LSTM model, revealing that synchronized movement can be a potent reward mechanism, enabling RL agents to learn effective policies. The initial findings indicate the feasibility of the RL approach in enhancing social interactions, providing partial support for our designed actions.

We present an interactive virtual agent that employs politeness strategies to suggest compromises during a joint decision-making task with a human partner.In our approach, politeness is expressed not only through language, but also through the agent's willingness to compromise by proposing options that better align with the human partner's preferences.We conducted a within-participant experiment (𝑛 = 97) in which users interacted with two types of agents: a performance-optimizing agent and a socially considerate agent.The former prioritized decision quality, while the latter occasionally made socially motivated quality concessions to the user as a form of face-work to defer to the human partner.Our results show that participants reported higher satisfaction with the final decision and consistent intention to collaborate with the socially considerate agent on both tasks.However, the social agent was less effective than the performance-optimizing agent at improving the users' initial decisions.These findings highlight trade-offs in human-agent interaction between optimizing task performance and managing the social dynamics of collaboration.We conclude by discussing the ethical and cultural considerations that may be involved in navigating this trade-off.

As the potential for human-AI design collaboration increases, understanding the role of artificial intelligence (AI) in the design process becomes more important. How does AI currently support the design process and how could it do so in the future? To answer this question, we categorized existing AI design support systems (DSS) according to the Double-Diamond design process model, and found that they are mostly used in the later stages of the design process, focusing on generating design solutions. In contrast, very few systems focus on the early stages of the process, which include discovering and defining design problems. To explore this finding’s alignment with designers’ expectations in real-world design, we present a case study involving emerging AI technologies such as ChatGPT and robots. This study proposes that AI agents can potentially assist designers by providing inspirations, defining design problems with constraints, offering grounded metaphors, and exploring design materials.

Designing plant-driven actuators presents an opportunity to create new types of devices that grow, age, and decay, such as robots that embody these qualities in their physical structure. Plant-robot hybrids that grow and decay incorporate unpredictable and gradual transformations inherent across living organisms and suggest an alternative to the design principles of immediacy, responsiveness, control, accuracy, and durability commonly found in robotic design. To explore this, we present a design space of primitives for plant-driven robotic actuators. Proof-of-concept prototypes illustrate how concepts like slow change, slow movement, decay, and destruction can be incorporated into robotic forms. We describe the design considerations required for building plant-driven actuators for robots, including experimental findings regarding the mechanical properties of plant forces. Finally, we speculate on the potential benefits of plant-robot hybrids to interactive domains such as robotics.

As artificial intelligence (AI) is playing an increasingly important role in our society and global economy, AI education and literacy have become necessary components in college and K-12 education to prepare students for an AI-powered society. However, current AI curricula have not yet been made accessible and engaging enough for students and schools from all socio-economic backgrounds with different educational goals. In this work, we developed an open-source learning module for college and high school students, which allows students to build their own robot companion from the ground up. This open platform can be used to provide hands-on experience and introductory knowledge about various aspects of AI, including robotics, machine learning (ML), software engineering, and mechanical engineering. Because of the social and personal nature of a socially assistive robot companion, this module also puts a special emphasis on human-centered AI, enabling students to develop a better understanding of human-AI interaction and AI ethics through hands-on learning activities. With open-source documentation, assembling manuals and affordable materials, students from different socio-economic backgrounds can personalize their learning experience based on their individual educational goals. To evaluate the student-perceived quality of our module, we conducted a usability testing workshop with 15 college students recruited from a minority-serving institution. Our results indicate that our AI module is effective, easy-to-follow, and engaging, and it increases student interest in studying AI/ML and robotics in the future. We hope that this work will contribute toward accessible and engaging AI education in human-AI interaction for college and high school students.

Shape-changing skin is an exciting modality due to its accessible and engaging nature. Its softness and flexibility make it adaptable to different interactive devices that children with and without visual impairments can share. Although their potential as an emotionally expressive medium has been shown for sighted adults, their potential as an inclusive modality remains unexplored. This work explores the shape-emotional mappings in children with and without visual impairment. We conducted a user study with 50 children (26 with visual impairment) to investigate their emotional associations with five skin shapes and two movement conditions. Results show that shape-emotional mappings are dependent on visual abilities. Our study raises awareness of the influence of visual experiences on tactile vocabulary and emotional mapping among sighted, low-vision, and blind children. We finish discussing the causal associations between tactile stimuli and emotions and suggest inclusive design recommendations for shape-changing devices.

Children with visual impairments often struggle to fully participate in group activities due to limited access to visual cues. They have difficulty perceiving what is happening, when, and how to act-leading to children with and without visual impairments being frustrated with the group activity, reducing mutual interactions. To address this, we created Touchibo, a tactile storyteller robot acting in a multisensory setting, encouraging touch-based interactions. Touchibo provides an inclusive space for group interaction as touch is a highly accessible modality in a mixed-visual ability context. In a study involving 107 children (37 with visual impairments), we compared Touchibo to an audio-only storyteller. Results indicate that Touchibo significantly improved children's individual and group participation perception, sparking touch-based interactions and the storyteller was more likable and helpful. Our study highlights touch-based robots' potential to enrich children's social interactions by prompting interpersonal touch, particularly in mixed-visual ability settings.

Home robots are envisioned to provide in-home assistance for older adults and other people who may need help with daily tasks. To gather information for inferring user status, robots typically require cameras to detect human subjects, track their positions, and recognize their activities or poses. However, having cameras in personal spaces, such as homes, could pose privacy concerns and risks due to the potential misuse or compromise of personal image data. It can also lead to psychological unease and feelings of insecurity, stemming from the fear of being watched and recorded. To address this issue, this paper proposes a method for preserving privacy based on physically obstructing the robot’s camera image and computer vision methods for detection and tracking of humans in these obstructed images. We present a hardware platform that includes a semi-transparent physical layer in front of the robot’s cameras to obtain privacy-preserving shadow images, and a software framework that uses a pre-trained EfficientNet, retrained with a newly-collected dataset of human shadow images for detecting and tracking human subjects. The testing results reveal that the network achieves reliable accuracy in detecting humans from various distances and angles, and it can be applied to a new subject that it has never seen before. Finally, the algorithm is implemented in a gaze-based human-robot interaction scenario, demonstrating its ability to track humans in real time while preserving privacy.

We investigate what it might look like for a robot to work with a human on a need-finding design task using an affinity diagram. While some recent projects have examined how human–robot teams might explore solutions to design problems, human–robot collaboration in the sensemaking aspects of the design process has not been studied. Designers use affinity diagrams to make sense of unstructured information by clustering paper notes on a work surface. To explore human–robot collaboration on a sensemaking design activity, we developed HIRO, an autonomous robot that constructs affinity diagrams with humans. In a within-user study, 56 participants affinity-diagrammed themes to characterize needs in quotes taken from real-world user data, once alone and once with HIRO. Users spent more time on the task with HIRO than alone, without strong evidence for corresponding effects on cognitive load. In addition, a majority of participants said they preferred to work with HIRO. From post-interaction interviews, we identified eight themes leading to four guidelines for robots that collaborate with humans on sensemaking design tasks: (1) account for the robot’s speed, (2) pursue mutual understanding rather than just correctness, (3) identify opportunities for constructive disagreements, and (4) use other modes of communication in addition to physical materials.

In this work, we present a method for personalizing human-robot interaction by using emotive facial expressions to generate affective robot movements. Movement is an important medium for robots to communicate affective states, but the expertise and time required to craft new robot movements promotes a reliance on fixed preprogrammed behaviors. Enabling robots to respond to multimodal user input with newly generated movements could stave off staleness of interaction and convey a deeper degree of affective understanding than current retrieval-based methods. We use autoencoder neural networks to compress robot movement data and facial expression images into a shared latent embedding space. Then, we use a reconstruction loss to generate movements from these embeddings and triplet loss to align the embeddings by emotion classes rather than data modality. To subjectively evaluate our method, we conducted a user survey and found that generated happy and sad movements could be matched to their source face images. However, angry movements were most often mismatched to sad images. This multimodal data-driven generative method can expand an interactive agent’s behavior library and could be adopted for other multimodal affective applications.

Robots have the potential to assist in emergency evacuation tasks, but it is not clear how robots should behave to evacuate people who are not fully compliant, perhaps due to panic or other priorities in an emergency. In this paper, we compare two robot strategies: an actively nudging robot that initiates evacuation and pulls toward the exit and a passively waiting robot that stays around users and waits for instruction. Both strategies were automatically synthesized from a description of the desired behavior. We conduct a within participant study ( = 20) in a simulated environment to compare the evacuation effectiveness between the two robot strategies. Our results indicate an advantage of the nudging robot for effective evacuation when being exposed to the evacuation scenario for the first time. The waiting robot results in lower efficiency, higher mental load, and more physical conflicts. However, participants like the waiting robots equally or slightly more when they repeat the evacuation scenario and are more familiar with the situation. Our qualitative analysis of the participants' feedback suggests several design implications for future emergency evacuation robots.

Movement and embodiment are communicative affordances central to social robotics, but designing embodied movements for robots often requires extensive knowledge of both robotics and movement theory. More accessible methods such as learning from demonstration often rely on physical access to the robot which is usually limited to research settings. Machine learning (ML) algorithms can complement hand-crafted or learned movements by generating new behaviors, but this requires large and diverse training datasets, which are hard to come by. In this work, we propose an embodied telepresence system for remotely crowdsourcing emotive robot movement samples that can serve as ML training data. Remote users control the robot through the internet using the motion sensors in their smartphones and view the movement either from a first-person or a third-person perspective. We evaluated the system in an online study where users created emotive movements for the robot and rated their experience. We then utilized the user-crafted movements as inputs to a neural network to generate new movements. We found that users strongly preferred the third-person perspective and that the ML-generated movements are largely comparable to the user-crafted movements. This work supports the usability of telepresence robots as a movement crowdsourcing platform.

Biological skin has numerous functions like protection, sensing, expression, and regulation. On the contrary, a robot’s skin is usually regarded as a passive and static separation between the body and environment. In this article, we explore the design opportunities of a robot’s skin as a socially expressive medium. Inspired by living organisms, we discuss the roles of interactive robotic skin from four perspectives: expression, perception, regulation, and mechanical action. We focus on the expressive function of skin to sketch design concepts and present a flexible technical method for embodiment. The proposed method integrates pneumatically actuated dynamic textures on soft skin, with forms and kinematic patterns generating a variety of visual and haptic expressions. We demonstrate the proposed design space with six texture-changing skin prototypes and discuss their expressive capacities.

Designing and evaluating the affectivity of the robot body has become a frontier topic in Human-Robot Interaction (HRI) , with previous studies [ 1 , 2 ] emphasizing the importance of robot embodiment for human-robot communication.In particular, there is growing interest in how the tactile, haptic materiality of the robot influences and mediates users' affective and emotional states.Indeed, the sheer physicality of robotic systems is a crucial factor in the morphology of the robotic platform, and therefore in the robot's appearance to the user.How do the tactile properties of materials subtly influence user interaction?Why do certain morphologies prompt more empathetic interactions than others?How is nonverbal communication affected through the coordination of movements of the torso, head, and appendages to provide more naturalistic-seeming interaction?What is the role of nonverbal communication in the production of artificial empathy?And how do such factors encourage trust and foster confidence for nonexpert users to interact in the first place?This recognition of machinic corporeality has been of practical interest to designers and engineers working across a range of robot forms and functions.The objective of this special issue is to further this discussion, to consider theoretical, ethical, empirical, and methodological questions related to the design of robotic bodies in the context of affective HRI, and thus foster cross currents among engineering, design, social science, and artistic communities.It originally emerged as a set of conceptual and practical questions from a workshop at the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI'20) in Cambridge, UK, co-organized by two of the editors [ 3 ].The workshop, like so many other events, was canceled because of the restrictions of the COVID-19 pandemic.Consequently, we tried to

This work investigates the collaborative task of object handovers between a human and a robot, a central aspect of human-robot collaboration. Our research contributes along three directions: first, designing robot controllers for previously unexplored human-robot handover scenarios; second, investigating gaze behaviors of a receiver in human-to-human and human-to-robot handovers; and third, investigating human behavior in bimanual and multiple sequential human-to-human handovers. Our contributions could help enable robots perform the complex but essential tasks of handing over objects to and receiving objects from humans.

We develop and evaluate two human-robot handover controllers that allow end-users to specify timing parameters for the robot reach motion, and that provide feedback if the robot cannot satisfy those constraints. End-user tuning with feedback is a useful controller feature in settings where robots have to be re-programmed for varying task requirements but end-users do not have programming knowledge. The two controllers we propose are both receding-horizon controllers that differ in their objective function, and their user specified parameters, and subsequently their user-interface: One controller uses a minimum cumulative jerk (MCJ) objective function, and the other a minimum cumulative error (MCE) objective function. We implemented the controllers on a collaborative robot and conducted two controlled experiments to compare the user experience and performance of these controllers vis-à-vis a baseline proportional velocity (PV) controller. In each experiment, participants (n = 30) interactively tuned the controller parameters, and collaborated with a robot to perform a time-constrained repetitive task. We found that the timing controller with the MCE implementation can provide a better user experience, both while setting the parameters (p =0.011) and performing the handovers with the robot (p < 0.001), and fewer failures (p =0.016) compared to the PV controller, however the MCJ implementation did not provide better user experience compared to the PV controller. The MCJ controller also resulted in more failures than the PV controller. These results could inform the design of usable and effective end-user configurable controllers for human-robot interaction.

Most robots are unclothed. However, we believe that robot clothes present an underutilized opportunity for the field of designing interactive systems. Clothes can help robots become better robots––by helping them be useful in a new, wider array of contexts, or better adapt and function in the contexts they are already in. In this paper, we provide a foundation for a research area of robot clothing by speculating on its potential. We systematically present functional requirements of robot clothing, considerations, and parameters for robot clothing designers, as well as key reference cases of robots in clothes. We then discuss what robot clothes can do specifically for the field of designing interactive systems.

In this paper we explore what role humans might play in designing tools for reinforcement learning (RL) agents to interact with the world. Recent work has explored RL methods that optimize a robot’s morphology while learning to control it, effectively dividing an RL agent’s environment into the external world and the agent’s interface with the world. Taking a user-centered design (UCD) approach, we explore the potential of a human, instead of an algorithm, redesigning the agent’s tool. Using UCD to design for a machine learning agent brings up several research questions, including what it means to understand an RL agent’s experience, beliefs, tendencies, and goals. After discussing these questions, we then present a system we developed to study humans designing a 2D racecar for an RL autonomous driver. We conclude with findings and insights from exploratory pilots with twelve users using this system.

We present the design process of the robot YOLO aimed at stimulating creativity in children. This robot was developed under a human-centered design approach with participatory design practices during two years and involving 142 children as active contributors at all design stages. The main contribution of this work is the development of methods and tools for child-centered robot design. We adapted existing participatory design practices used with adults to fit children's development stages. We followed the Double-Diamond Design Process Model and rested the design process of the robot on the following principles: low floor and wide walls, creativity provocations, open-ended playfulness, and disappointment avoidance through abstraction. The final product is a social robot designed for and with children. Our results show that YOLO increases their creativity during play, demonstrating a successful robot design project. We identified several guidelines that made the design process successful: the use of toys as tools, playgrounds as spaces, the emphasis of playfulness for child expression, and child policies as allies for design studies. The design process described empowers children's in the design of robots.

Robots designed for social interaction are predicted to be employed in a wide range of environments including homes, schools, and workplaces (Bartneck & Forlizzi, 2004; Fong, Nourbakhsh, & Dautenha...

We provide guidelines for planning, executing, analyzing, and reporting hypothesis-driven experiments in Human–Robot Interaction (HRI). The intended audience are researchers in the field of HRI who are not trained in empirical research but who are interested in conducting rigorous human-participant studies to support their research. Following the chronological order of research activities and grounded in updated research practices in psychological and behavioral sciences, this primer covers recommended methods and common pitfalls for defining research questions, identifying constructs and hypotheses, choosing appropriate study designs, operationalizing constructs as variables, planning and executing studies, sampling, choosing statistical tools for data analysis, and reporting results.

We propose a method for modifying affective robot movements using neural networks. Social robots use gestures and other movements to express their internal states. However, a robot's interactive capabilities are hindered by the predominant use of a limited set of preprogrammed or hand-animated behaviors, which can be repetitive and predictable, making sustained human-robot interactions difficult to maintain. To address this, we developed a method for modifying existing emotive robot movements by using neural networks. We use hand-crafted movement samples and a classifying variational autoencoder trained on these samples. Our method then allows for adjustment of affective movement features by using simple arithmetic in the network's latent embedding space. We present the implementation and evaluation of this approach and show that editing in the latent space can modify the emotive quality of the movements while preserving recognizability and legibility in many cases. This supports neural networks as viable tools for creating and modifying expressive robot behaviors.

We address the challenge of inferring the design intentions of a human by an intelligent virtual agent that collaborates with the human. First, we propose a dynamic Bayesian network model that relates design intentions, objectives, and solutions during a human's exploration of a problem space. We then train the model on design behaviors generated by a search agent and use the model parameters to infer the design intentions in a test set of real human behaviors. We find that our model is able to infer the exact intentions across three objectives associated with a sequence of design outcomes 31.3% of the time. Inference accuracy is 50.9% for the top two predictions and 67.2% for the top three predictions. For any singular intention over an objective, the model's mean F1-score is 0.719. This provides a reasonable foundation for an intelligent virtual agent to infer design intentions purely from design outcomes toward establishing joint intentions with a human designer. These results also shed light on the potential benefits and pitfalls in using simulated data to train a model for human design intentions.

We describe a physical interactive system for human-robot collaborative design (HRCD) consisting of a tangible user interface (TUI) and a robotic arm that simultaneously manipulates the TUI with the human designer. In an observational study of 12 participants exploring a complex design problem together with the robot, we find that human designers have to negotiate both the physical and the creative space with the machine. They also often ascribe social meaning to the robot's pragmatic behaviors. Based on these findings, we propose four considerations for future HRCD systems: managing the shared workspace, communicating preferences about design goals, respecting different design styles, and taking into account the social meaning of design acts.

We evaluate the emotional expression capacity of skin texture change, a new design modality for social robots. In contrast to the majority of robots that use gestures and facial movements to express internal states, we developed an emotionally expressive robot that communicates using dynamically changing skin textures. The robot's shell is covered in actuated goosebumps and spikes, with programmable frequency and amplitude patterns. In a controlled study ( n=139) we presented eight texture patterns to participants in three interaction modes: online video viewing, in person observation, and touching the texture. For most of the explored texture patterns, participants consistently perceived them as expressing specific emotions, with similar distributions across all three modes. This indicates that a texture changing skin can be a useful new tool for robot designers. Based on the specific texture-to-emotion mappings, we provide actionable design implications, recommending using the shape of a texture to communicate emotional valence, and the frequency of texture movement to convey emotional arousal. Given that participants were most sensitive to valence when touching the texture, and were also most confident in their ratings using that mode, we conclude that touch is a promising design channel for human-robot communication.

In a controlled experiment, participants ( n=60) competed in a monotonous task with an autonomous robot for real monetary incentives. For each participant, we manipulated the robot's performance and the monetary incentive level across ten rounds. In each round, a participant's performance compared to the robot's would affect their odds in a lottery for the monetary prize. Standard economic theory predicts that people's effort will increase with prize value. Furthermore, recent work in behavioral economics predicts that there will also be a discouragement effect, with stronger robot performance discouraging human effort, and that this effect will increase with prize. We were not able to detect a meaningful effect of monetary prize, but we found a small discouragement effect, with human effort decreasing with increased robot performance, significant at the level. Using per-round subjective indicators, we also found a positive effect of robot performance on its perceived competence, a negative effect on the participants' liking of the robot, and a negative effect on the participants' own competence, all at . These findings shed light on how people may exert work effort and perceive robotic competitors in a human-robot workforce, and could have implications on labor supply decisions and the design of compensation schemes in the workplace.

Social robots use gestures to express internal and affective states, but their interactive capabilities are hindered by relying on preprogrammed or hand-animated behaviors, which can be repetitive and predictable. We propose a method for automatically synthesizing affective robot movements given manually-generated examples. Our approach is based on techniques adapted from deep learning, specifically generative adversarial neural networks (GANs).

This paper combines techniques from Formal Methods and Human-Robot Interaction (HRI) to address the challenge of a robot walking with a human while maintaining a socially acceptable distance and avoiding collisions. We formulate a set of constraints on the robot motion using Satisfiability Modulo Theories (SMT) formulas, and synthesize robot control that is guaranteed to be safe and correct. Due to its use of high-level formal specifications, the controller is able to provide feedback to the user in situations where human behavior causes the robot to fail. This feedback allows the human to adjust their behavior and recover joint navigation. We demonstrate the behavior of the robot in a variety of simulated scenarios and compare it to utility-based side-by-side navigation control.

We present a controller for human-robot handovers that is automatically synthesized from high-level specifications in Signal Temporal Logic (STL). In contrast to existing controllers, this approach can provide formal guarantees on the timing of each of the handover phases. Using synthesis also allows end-users to specify and dynamically change the robot's behaviors using high-level requirements of goals and constraints rather than by tuning low-level controller parameters. We illustrate the proposed approach by replicating the behavior of existing handover strategies from the literature. We also identify specification parameters that are likely to lead to successful handovers using a public database of human-human handovers.

Collaborative fluency is the coordinated meshing of joint activities between members of a well-synchronized team. In recent years, researchers in human-robot collaboration have been developing robots to work alongside humans aiming not only at task efficiency, but also at human-robot fluency. As part of this effort, we have developed a number of metrics to evaluate the level of fluency in human-robot shared-location teamwork. While these metrics are being used in existing research, there has been no systematic discussion on how to measure fluency and how the commonly used metrics perform and compare. In this paper, we codify subjective and objective human-robot fluency metrics, provide an analytical model for four objective metrics, and assess their dynamics in a turn-taking framework. We also report on a user study linking objective and subjective fluency metrics and survey recent use of these metrics in the literature.

Blossom is an open-source social robotics platform responding to three challenges in human-robot interaction (HRI) research: (1) Designing, manufacturing, and programming social robots requires a high level of technical knowledge; (2) social robot designs are fixed in appearance and movement capabilities, making them hard to adapt to a specific application; and (3) the use of rigid mechanisms and hard outer shells limits the robots’ expressive capabilities. Addressing these challenges, Blossom aims at three design objectives: accessibility, flexibility, and expressiveness. The robot’s mechanism can be quickly assembled and partially extended by end-users. Blossom’s appearance is open-ended through handcrafted fabric exteriors created and customized by users. Smooth organic movements are achieved with tensile mechanisms, elastic components, and a soft exterior cover attached loosely to the body. Blossom’s smartphone-based gesture generation requires neither programming nor character animation experience, allowing lay users to create their own behaviors. All elements in the design were conceived with a low barrier-of-entry in mind. The result is an accessible and customizable social robot for researchers. This article details the implementation of Blossom’s design and demonstrates the platform’s potential through four field deployment case studies.

This paper presents the design of a wearable robotic forearm for close-range human-robot collaboration. The robot's function is to serve as a lightweight supernumerary third arm for shared workspace activities. We present a functional prototype resulting from an iterative design process including several user studies. An analysis of the robot's kinematics shows an increase in reachable workspace by 246 % compared to the natural human reach. The robot's degrees of freedom and range of motion support a variety of usage scenarios with the robot as a collaborative tool, including self-handovers, fetching objects while the human's hands are occupied, assisting human-human collaboration, and stabilizing an object. We analyze the bio-mechanical loads for these scenarios and find that the design is able to operate within human ergonomic wear limits. We then report on a pilot human-robot interaction study that indicates robot autonomy is more task-time efficient and preferred by users when compared to direct voice-control. These results suggest that the design presented here is a promising configuration for a lightweight wearable robotic augmentation device, and can serve as a basis for further research into human-wearable collaboration.

Opening encounters are an integral element of everyday social interaction, and are essential for forming and maintaining social relationships between people. We present an abstract non-humanoid robotic object called the Greeting Machine, designed to communicate positive and negative social cues in the context of opening encounters. The design includes a small ball rolling on a larger dome, with a custom gear and lever mechanism that supports a variety of subtle movements. Gestures were designed with movement experts, and were evaluated using a physical first-person qualitative study. Our findings reveal that an abstract robot designed with no specific metaphor can effectively take part in opening encounters. Furthermore, a minimal brief movement, designed as an Approach or Avoid gesture, may be enough to evoke positive and negative experiences. The ability to create opening encounters with low Degree of Freedom abstract robots is promising, both due to the low complexity, low cost, and design flexibility of such devices, and due to the possible generalization of the Approach and Avoid gestures for a variety of morphologies.

This paper describes the design and development of YOLO, a social robot aimed at boosting creativity in children. Creativity is one of the most sought-after competencies as we move from industrialized economies, in which standardized knowledge was imperative, to creative economies, where the ability to innovate is crucial for the workforce. YOLO is a robot to be used by children as a tool to boost new ideas and stimulate their creativity. This paper describes how established educational strategies that enhance creativity were combined with co-designing with children as informants to reach the the prototype design of the robot.

With domestic technology on the rise, the quantity and complexity of smart-home devices are becoming an important interaction design challenge. We present a novel design for a home control interface in the form of a social robot, commanded via tangible icons and giving feedback through expressive gestures. We experimentally compare the robot to three common smart-home interfaces: a voice-control loudspeaker; a wall-mounted touch-screen; and a mobile application. Our findings suggest that interfaces that rate higher on flow rate lower on usability, and vice versa. Participants' sense of control is highest using familiar interfaces, and lowest using voice control. Situation awareness is highest using the robot, and also lowest using voice control. These findings raise questions about voice control as a smart-home interface, and suggest that embodied social robots could provide for an engaging interface with high situation awareness, but also that their usability remains a considerable design challenge.

This paper presents the design of a wearable robotic forearm that provides the user with an assistive third hand, along with a study of interaction scenarios for the design. Technical advances in sensors, actuators, and materials have made wearable robots feasible for personal use, but the interaction with such robots has not been sufficiently studied. We describe the development of a working prototype along with three usability studies. In an online survey we find that respondents presented with images and descriptions of the device see its use mainly as a functional tool in professional and military contexts. A subsequent contextual inquiry among building construction workers reveals three themes for user needs: extending a worker's reach, enhancing their safety and comfort through bracing and stabilization, and reducing their cognitive load in repetitive tasks. A subsequent laboratry study in which participants wear a working prototype of the robot finds that they prioritize lowered weight and enhanced dexterity, seek adjustable autonomy and transparency of the robot's intent, and prefer a robot that looks distinct from a human arm. These studies inform design implications for further development of wearable robotic arms.

Creativity is one of the most important and pervasive of all human abilities. However, it seems to decline during school age years, in a phenomenon entitled “creative crisis”. As developed societies are shifting from an industrialized economy to a creative economy, there is a need to support creative abilities through life. With this work, we aim to use social robots as boosters for creative-driven behaviors with children.

Responsiveness to one's bids for proximity in times of need is a linchpin of human interaction. Thus, the ability to be perceived as responsive has design implications for socially assistive robots. We report on a large-scale experimental laboratory study (n = 102) examining robot responsiveness and its effects on human attitudes and behaviors. In one-on-one sessions, participants disclosed a personal event to a non-humanoid robot. The robot responded either responsively or unresponsively across two modalities: Simple gestures and written text. We replicated previous findings that the robot's responsiveness increased perceptions of its appealing traits. In addition, we found that robot responsiveness increased nonverbal approach behaviors (physical proximity, leaning toward the robot, eye contact, smiling) and participants' willingness to be accompanied by the robot during stressful events. These findings suggest that humans not only utilize responsiveness cues to ascribe social intentions to personal robots, but actually change their behavior towards responsive robots and may want to use such robots as a source of consolation.

Social presence has two opposing effects on human corruption: the collaborative and contagious nature of another person's presence can cause people to behave in a more corrupt manner. In contrast, the monitoring nature of another person's presence can decrease corruption. We hypothesize that a robot's presence can provide the best of both worlds: Decreasing corruption by providing a monitoring presence, without increasing it by collusion. We describe an experimental study currently underway that examines this hypothesis, and report on initial findings from pilot runs of our experimental protocol.

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.

Human-robot collaboration will increasingly take place in human social settings, including contexts where ethical and honest behavior is paramount. How might these robots affect human honesty? In this paper, we present first evidence of how a robot's presence affects people's ethical behavior in a controlled field study. We observed people passing by a food plate marked as “reserved”, comparing three conditions: no observer, a human observer, and a robot observer. We found that a human observer elicits less attention than a robot, but evokes more of a socially normative presence causing people to act honestly. Conversely, we found that a robot observer elicits more attention, engagement, and a monitoring presence. But even though people were suspicious that they were being monitored, they still behaved dishonestly in the robot observer condition.

We describe the design process of “Vyo”, a personal assistant serving as a centralized interface for smart home devices. Building on the concepts of ubiquitous and engaging computing in the domestic environment, we identified five design goals for the home robot: engaging, unobtrusive, device-like, respectful, and reassuring. These goals led our design process, which included simultaneous iterative development of the robot's morphology, nonverbal behavior and interaction schemas. We continued with user-centered design research using puppet prototypes of the robot to assess and refine our design choices. The resulting robot, Vyo, straddles the boundary between a monitoring device and a socially expressive agent, and presents a number of novel design outcomes: The combination of TUI “phicons” with social robotics; gesture-related screen exposure; and a non-anthropomorphic monocular expressive face. We discuss how our design goals are expressed in the elements of the robot's final design.

We present the design and initial evaluation of Kip3, a social robotic device for students with ADHD that provides immediate feedback for inattention or impulsivity events. We designed a research platform comprised of a tablet-based Continuous Performance Test (CPT) that is used to assess inattention and impulsivity, and a socially expressive robotic device (Kip3) as feedback. We evaluated our platform with 10 students with ADHD in a within subject user study, and report that 9 out of 10 participants felt that Kip3 helped them regain focus, but wondered if it will be effective over time and how it will identify inattention in more complex situations outside the lab.

Within the next few years, personal robots are expected to enter our homes, offices, schools, hospitals, construction sites, and workshops. For these robots to play a successful role in people's professional and personal lives, they need to display the kind of efficient and satisfying interaction that humans are accustomed to from each other. Designing this human-robot interaction is a multifaceted challenge, balancing requirements of the robot's appearance and behavior. A robot's appearance evokes interaction affordances and triggers emotional responses; its behavior communicates internal states, and can support action coordination and joint planning. Good HRI design should enlist both facets to enable untrained humans to work fluently and intuitively with the robot.

Robots are predicted to serve in environments in which human honesty is important, such as the workplace, schools, and public institutions. Can the presence of a robot facilitate honest behavior? In this paper, we describe an experimental study evaluating the effects of robot social presence on people's honesty. Participants completed a perceptual task, which is structured so as to allow them to earn more money by not complying with the experiment instructions. We compare three conditions between subjects: Completing the task alone in a room; completing it with a non-monitoring human present; and completing it with a non-monitoring robot present. The robot is a new expressive social head capable of 4-DoF head movement and screen-based eye animation, specifically designed and built for this research. It was designed to convey social presence, but not monitoring. We find that people cheat in all three conditions, but cheat equally less when there is a human or a robot in the room, compared to when they are alone. We did not find differences in the perceived authority of the human and the robot, but did find that people felt significantly less guilty after cheating in the presence of a robot as compared to a human. This has implications for the use of robots in monitoring and supervising tasks in environments in which honesty is key.

We present the design, implementation, and evaluation of a peripheral empathy-evoking robotic conversation companion, Kip1. The robot's function is to increase people's awareness to the effect of their behavior towards others, potentially leading to behavior change. Specifically, Kip1 is designed to promote non-aggressive conversation between people. It monitors the conversation's nonverbal aspects and maintains an emotional model of its reaction to the conversation. If the conversation seems calm, Kip1 responds by a gesture designed to communicate curious interest. If the conversation seems aggressive, Kip1 responds by a gesture designed to communicate fear. We describe the design process of Kip1, guided by the principles of peripheral and evocative. We detail its hardware and software systems, and a study evaluating the effects of the robot's autonomous behavior on couples' conversations. We find support for our design goals. A conversation companion reacting to the conversation led to more gaze attention, but not more verbal distraction, compared to a robot that moves but does not react to the conversation. This suggests that robotic devices could be designed as companions to human-human interaction without compromising the natural communication flow between people. Participants also rated the reacting robot as having significantly more social human character traits and as being significantly more similar to them. This points to the robot's potential to elicit people's empathy.

Mirror Puppeteering is a system for easily creating gestures ("animations") for robotic toys, custom robots, and virtual characters. Lay users can record animations by simply moving a robot's limbs in front of a webcam. Makers and hobbyists can use the system to easily set up their custom-built robots for animation. Gamers and amateur animators can real-time control or save animations for virtual characters. Our system works by tracking circular markers on the robot's surface and translating these into motor commands, using a calibration map between marker locations in camera space and motor angles. New robots can be quickly set up for Mirror Puppeteering without knowledge of the robot's 3D structure, as we demonstrate on several robots. In a user study, participants found our method more enjoyable, usable, easy to learn, and successful than traditional animation methods.

We present the notion of Empathy Objects, ambient robotic devices accompanying human-human interaction. Empathy Objects respond to human behavior using physical gestures as nonverbal expressions of their "emotional states". The goal is to increase people's self-awareness to the emotional state of others, leading to behavior change. We demonstrate an Empathy Object prototype, Kip1, a conversation companion designed to promote non-aggressive conversation between people.

In human relationships, responsiveness---behaving in a sensitive manner that is supportive of another person's needs---plays a major role in any interaction that involves effective communication, caregiving, and social support. Perceiving one's partner as responsive has been tied to both personal and relationship well-being. In this work, we examine whether and how a robot's behavior can instill a sense of responsiveness, and the effects of a robot's perceived responsiveness on the human's perception of the robot. In an experimental between-subject study (n=34), a desktop non-anthropomorphic robot performed either positive or negative responsiveness behaviors across two modalities (simple gestures and written text) in response to participants' negative event disclosure. We found that perceived partner responsiveness, positive human-like traits, and robot attractiveness were higher in the positively responsive condition. This has design implications for interactive robots, in particular for robots in caregiving roles.

Timing plays a role in a range of human-robot interaction scenarios, as humans are highly sensitive to timing and interaction fluency. It is central to spoken dialogue, with turn-taking, interruptions, and hesitation influencing both task efficiency and user affect. Timing is also an important factor in the interpretation and generation of gestures, gaze, facial expressions, and other nonverbal behavior. Beyond communication, temporal synchronization is functionally necessary for sharing resources and physical space, as well as coordinating multi-agent actions. Timing is thus crucial to the success of a broad spectrum of HRI applications, including but not limited to situated dialogue; collaborative manipulation; performance, musical, and entertainment robots; and expressive robot companions. Recent years have seen a growing interest in the HRI community in the various research topics related to human-robot timing. The purpose of this workshop is to explore and discuss theories, computational models, systems, empirical studies, and interdisciplinary insights related to the notion of timing, fluency, and rhythm in human-robot interaction.

This paper makes the case for designing interactive robots with their expressive movement in mind. As people are highly sensitive to physical movement and spatiotemporal affordances, well-designed robot motion can communicate, engage, and offer dynamic possibilities beyond the machines' surface appearance or pragmatic motion paths. We present techniques for movement centric design, including character animation sketches, video prototyping, interactive movement explorations, Wizard of Oz studies, and skeletal prototypes. To illustrate our design approach, we discuss four case studies: a social head for a robotic musician, a robotic speaker dock listening companion, a desktop telepresence robot, and a service robot performing assistive and communicative tasks. We then relate our approach to the design of non-anthropomorphic robots and robotic objects, a design strategy that could facilitate the feasibility of real-world human-robot interaction.

Family car rides can become a source of boredom for child passengers, and consequently cause tension inside the car. In an attempt to overcome this problem, we developed Mileys -- a novel in-car game that integrates location-based information, augmented reality and virtual characters. It is aimed to make car rides more interesting for child passengers, strengthen the bond between family members, encourage safe and ecological driving, and connect children with their environment instead of their entertainment devices. We evaluated Mileys with a six-week long field study, which revealed differences between children and parents regarding their desired in-car experience. Children wish to play enjoyable games, whereas parents view car rides as an opportunity for strengthening the bond between family members and for educating their children. Based on our findings, we identify five key challenges for in-car game design for children: different expectations by parents and children, undesired detachment, short interaction span, poor GPS reception, and motion sickness.

Combining high-performance, sensor-rich mobile devices with simple, low-cost robotic platforms could accelerate the adoption of personal robotics in real-world environments. We present a case study of this "dumb robot, smart phone" paradigm: a robotic speaker dock and music listening companion. The robot is designed to enhance a human¿s listening experience by providing social presence and embodied musical performance. In its initial application, it generates segmentspecific, beat-synchronized gestures based on the song's genre, and maintains eye-contact with the user. All of the robot's computation, sensing, and high-level motion control is performed on a smartphone, with the rest of the robot¿s parts handling mechanics and actuator bridging.

We present Shimon, an interactive improvisational robotic marimba player, developed for research in Robotic Musicianship. The robot listens to a human musician and continuously adapts its improvisation and choreography, while playing simultaneously with the human. We discuss the robot's mechanism and motion-control, which uses physics simulation and animation principles to achieve both expressivity and safety. We then present a novel interactive improvisation system based on the notion of gestures for both musical and visual expression. The system also uses anticipatory beat-matched action to enable real-time synchronization with the human player. Our system was implemented on a full-length human-robot Jazz duet, displaying highly coordinated melodic and rhythmic human-robot joint improvisation. We have performed with the system in front of a live public audience.

Shimon is a interactive robotic marimba player, developed as part of our ongoing research in Robotic Musicianship (RM). One of the potential benefits of RM is that it provides human players with embodied information that relates spatial movement to tone generation. This can aid in anticipation and coordination of synchronous playing. As part of a human-robot Jazz improvisation system, we present an anticipatory system enabling beat-matched real-time synchronization. Our system enables flexible, yet coordinated call-and-response, a standard type of musical interaction. It was used in a live public human-robot joint Jazz performance. We also describe a preliminary study evaluating the effect of embodiment on this call-and-response musical synchronization task. We conducted a 3×2 within-subject study manipulating the level of embodiment (visual co-presence, physical presence but visual occlusion, and synthesized sound), and the accuracy of the robot's response. Our findings indicate that synchronization is aided by visual contact when uncertainty is high, but that pianists can resort to internal rhythmic coordination in more predictable settings. We find that visual coordination is more effective for synchronization for slow sequences compared to faster sequences; and that occluded physical presence may be less effective than audio-only note generation.

We have developed a cognitive architecture for robotic teammates based on the neuro-psychological principles of perceptual symbols and simulation, with the aim of attaining increased fluency in human-robot teams. An instantiation of this architecture was implemented on a robotic desk lamp, performing in a human-robot collaborative task. This paper describes initial results from a human-subject study measuring team efficiency and team fluency, in which the robot works on a joint task with untrained subjects. We find significant differences in a number of efficiency and fluency metrics, when comparing our architecture to a purely reactive robot with similar capabilities.

This paper describes a robotic puppeteering system used in a theatrical production involving one robot and two human performers on stage. We draw from acting theory and human-robot interaction to develop a hybrid-control puppeteering interface which combines reactive expressive gestures and parametric behaviors with a point-of-view eye contact module. Our design addresses two core considerations: allowing a single operator to puppeteer the robotpsilas full range of behaviors, and allowing for gradual replacement of human-controlled modules by autonomous subsystems. We wrote a play specifically for a performance between two humans and one of our research robots, a robotic lamp which embodied a lead role in the play. We staged three performances with the robot as part of a local festival of new plays. Though we have yet to perform a formal statistical evaluation of the system, we interviewed the actors and director and present their feedback about working with the system.

A crucial skill for fluent action meshing in human team activity is a learned and calculated selection of anticipatory actions. We believe that the same holds for robotic teammates, if they are to perform in a similarly fluent manner with their human counterparts.In this work, we propose an adaptive action selection mechanism for a robotic teammate, making anticipatory decisions based on the confidence of their validity and their relative risk. We predict an improvement in task efficiency and fluency compared to a purely reactive process.We then present results from a study involving untrained human subjects working with a simulated version of a robot using our system. We show a significant improvement in best-case task efficiency when compared to a group of users working with a reactive agent, as well as a significant difference in the perceived commitment of the robot to the team and its contribution to the team's uency and success. By way of explanation, we propose a number of fluency metrics that differ significantly between the two study groups.

A crucial skill for fluent action meshing in human team activity is a learned and calculated selection of anticipatory actions. We believe that the same holds for robotic teammates, if they are to perform in a similarly fluent manner with their human counterparts. In this work, we describe a model for human-robot joint action, and propose an adaptive action selection mechanism for a robotic teammate, which makes anticipatory decisions based on the confidence of their validity and their relative risk. We conduct an analysis of our method, predicting an improvement in task efficiency compared to a purely reactive process. We then present results from a study involving untrained human subjects working with a simulated version of a robot using our system. We show a significant improvement in best-case task efficiency when compared to a group of users working with a reactive agent, as well as a significant difference in the perceived commitment of the robot to the team and its contribution to the team's fluency and success. By way of explanation, we raise a number of fluency metric hypotheses, and evaluate their significance between the two study conditions.

In Socially Guided Machine Learning we explore the ways in which machine learning can more fully take advantage of natural human interaction. In this work we are studying the role real-time human interaction plays in training assistive robots to perform new tasks. We describe an experimental platform, Sophie's World, and present descriptive analysis of human teaching behavior found in a user study. We report three important observations of how people administer reward and punishment to teach a simulated robot a new task through Reinforcement Learning. People adjust their behavior as they develop a model of the learner, they use the reward channel for guidance as well as feedback, and they may also use it as a motivational channel.

While reinforcement learning (RL) is not traditionally designed for interactive supervisory input from a human teacher, several works in both robot and software agents have adapted it for human input by letting a human trainer control the reward signal. In this work, we experimentally examine the assumption underlying these works, namely that the human-given reward is compatible with the traditional RL reward signal. We describe an experimental platform with a simulated RL robot and present an analysis of real-time human teaching behavior found in a study in which untrained subjects taught the robot to perform a new task. We report three main observations on how people administer feedback when teaching a robot a task through reinforcement learning: (a) they use the reward channel not only for feedback, but also for future-directed guidance; (b) they have a positive bias to their feedback -possibly using the signal as a motivational channel; and (c) they change their behavior as they develop a mental model of the robotic learner. In conclusion, we discuss future extensions to RL to accommodate these lessons

Nonverbal communication plays an important role in coordinating teammates' actions for collaborative activities. In this paper, we explore the impact of non-verbal social cues and behavior on task performance by a human-robot team. We report our results from an experiment where naive human subjects guide a robot to perform a physical task using speech and gesture. Both self-report via questionnaire and behavioral analysis of video offer evidence to support our hypothesis that implicit non-verbal communication positively impacts human-robot task performance with respect to understandability of the robot, efficiency of task performance, and robustness to errors that arise from miscommunication.