In this paper, we set out to explore how people judge the personality of a non-anthropomorphic virtual agent during group interactions. Using a Wizard of Oz (WoZ) based approach, we observed that people judged the acceptability of a virtual agent’s behavior from a tool-based lens, that is, if this robot and its behavior are useful to the team or not. Furthermore, we found that while people were able to acknowledge the virtual agent’s personality and recognize its identity through social cues, the tool-based framing impacts these perceptions into a normative judgment of the robots’ utility. We present two case studies that we think highlight this tool-based interpretation of robotic personality: robots expressing non-factual opinions and robots expressing humor. Finally, we suggest that researchers should consider the impact of this tool-based framing on people’s perceptions of a robot’s identity when designing robots for social interaction.

This study examines three modalities for designing live-in van interiors. Participants (N=18) situated within an empty van were asked to explore potential designs using physical cardboard prototyping, a commercial software application (Vanspace 3D) for planning van interiors, and an augmented reality application that we developed. Participants were asked to think aloud as they designed van interiors for fictive journeys using each modality. A qualitative evaluation was conducted to assess how participants’ conceptualizations of space shifted across the use of each prototyping method. The results demonstrate that each design method influenced design outcomes due to the physicality of the task. This study highlights the importance of considering the role of physicality in the selection of prototyping modality for the design exploration process.

Artificially intelligent (AI) agents such as robots are increasingly delegated power in work settings, yet it remains unclear how power functions in interactions with both humans and robots, especially when they directly compete for influence. Here we present an experiment where every participant was matched with one human and one robot to perform decision-making tasks. By manipulating who has power, we created three conditions: human as leader, robot as leader, and a no-power-difference control. The results showed that the participants were significantly more influenced by the leader, regardless of whether the leader was a human or a robot. However, they generally held a more positive attitude toward the human than the robot, although they considered whichever was in power as more competent. This study illustrates the importance of power for future Human-Robot Interaction (HRI) and Human-AI Interaction (HAI) research, as it addresses pressing concerns of society about AI-powered intelligent agents.

Robotic appliances are continually being adopted into private homes. However, users have yet to exhibit the same acceptance towards domestic social robots. In this paper, we seek to bridge this issue by augmenting already-existing home appliances with capabilities mimicking social robots. We present a robotic toaster designed with animated movements to enhance and personalize the toast-making experience. Not only does the robotic toaster assist in completing the task itself, it also acts as a conscious agent with whom users may interact in a social and playful manner. Using a series of video vignettes, we identify three key themes of the robotic toaster that influence its relationship with users: these are related to (1) context awareness, (2) increased interactivity through initiative action, and (3) expression of personality despite limited degrees of freedom. Lastly, we discuss how the portrayal of home appliances with social characteristics can potentially serve as an introductory step for social robots in the home.

We present an exploratory human participant study (N=3) examining how people interact with a pen-plotting robot that interactively plays Tic-Tac-Toe on a shared physical sheet of paper. Each participant played a round of Tic-Tac-Toe against the robot, while we observed. We particularly focused our observations on the participants’ physical and social behaviors during game interaction, as well as in-moment reactions from the participants. Following each game, we performed semi-structured qualitative interviews to understand the user’s experience interacting with the robot. Our questions were designed to elicit comparisons of their experience with less tangible interactions that players might have with a computer or phone-based app, as well as more traditional interactions that players might have with other people. We found that participants directly addressed the robot by talking to it during play and openly expressed competitiveness against the robot. Furthermore, participants displayed careful movements around the robot and attentively observed its behaviors. Based on these initial insights from our exploratory study, we are planning future experiments to investigate the effect that the mutuality of the physical Tic-Tac-Toe interaction has on social responses to the robot to understand what this implies for embodied and tangible interaction design.

In this paper, we investigate how to elicit new perspectives in research-through-design (RtD) studies through annotated portfolios. Situating the usage in human-robot interaction (HRI), we used two robotic artefacts as a case study: we first created our own annotated portfolio and subsequently ran online workshops during which we asked HRI experts to annotate our robotic artefacts. We report on the different aspects revealed about the value, use, and further improvements of the robotic artefacts through using the annotated portfolio technique ourselves versus using it with experts. We suggest that annotated portfolios – when performed by external experts – allow design researchers to obtain a form of creative and generative peer critique. Our paper offers methodological considerations for conducting expert annotation sessions. Further, we discuss the use of annotated portfolios to unveil designerly HRI knowledge in RtD studies.

We propose the use of interactive vignettes as an alternative to traditional text- and video-based vignettes for conducting large-scale Human-Robot Interaction (HRI) studies. Interactive vignettes maintain the advantages of traditional vignettes while offering additional affordances for participant interaction and data collection through interactive elements. We discuss the core affordances of interactive vignettes, including explorability, responsiveness, and non-linearity, and look into how these affordances can enable HRI research with more complex scenarios. To demonstrate the strength of the approach, we present a case study of our own research project with N=87 participants and show the data we collect through interactive vignettes. We suggest that the use of interactive vignettes can benefit HRI researchers in learning how participants interact with, respond to, and perceive a robot’s behavior in pre-defined scenarios.

Fast-paced contemporary life full of planned interaction usually makes people miss out on wonderful moments. We here present BubbleBot, a speculative robot designed to support serendipity of interactions in public space. After observations in public spaces and embodied design workshops, we have designed BubbleBot to be a peripheral public-space robot, bursting bubbles at passersby to invite serendipitous interactions. BubbleBot is a speculative robot to create magical moments among people with minimal peripheral social interaction. With this project, we aim at generating a conversation about the future roles and interaction paradigms of robots in public space.

Robots can deal with different kinds of challenges using tactile sensing arrays as a primary resource. This paper demonstrates the ability of robotic hands to recognize objects' shapes using only a flexible tactile sensor arrays attached to the robotic hand's surface without building the 3D models of objects. A telemanipulation module was developed to achieve a co-moving mechanism between the robotic hand and human hands so that the robotic hand can directly learn the best way to grasp objects from human hands without additional path planning process. Tactile array data were collected while the robotic hand was performing a reiterative grasping process. By extracting the proper features from the tactile sensor array data, the support vector machines (SVMs) were employed to perform object classification. From experiments, the proposed method can achieve 96.67% classification accuracy based on sensory data and SVMs.