Social robots are beginning to be utilized as part of the collective networks supporting pediatric treatment, however there are few studies on children's perceptions of these agents in hospitals from a privacy and safety perspective. Through a mixed-method and value-sensitive design approach, we introduced hypothetical vignettes and engaged in discussion with 15 youth who are either receiving cancer treatments or are in remission (ages 6-25), 11 of their parents, and 5 out of 8 of their clinical staff to learn how stakeholders in pediatric oncology discuss privacy concerns regarding child-robot interactions. Our thematic analysis imparts how stakeholders perceive robots as social, non-authoritative extensions of the hospital's care network. From this privacy-sensitive perspective, this study revealed that for maximizing a robot's social utility within care systems while critically engaging with the comfort and privacy preferences of stakeholders, robots should take on the role of 1) mediators of social interaction among various stakeholders, 2) companions for children and 3) informational tools for clinicians when consent is given by the family. We emphasize how assistive technologies in pediatrics should continue to be co-designed within communities for identifying appropriate roles and returning agency to stakeholders as they navigate the blurry boundaries of privacy in healthcare.

Social robots for children have focused mainly on conventional education domains such as teaching language, science, and math, while applications focusing on the enhancement of cultural competency are quite scarce. In this paper, we present a prototype of a robot-mediation framework for cross-cultural communication. This framework paves the way for a social robot to act as a mediator between groups of schoolchildren from different countries. First, we conducted a participatory design activity by an interdisciplinary team, resulting in the extraction of the design, robot’s roles, and technical requirements. Based on these requirements, we built the robot-mediation system prototype. We conducted a pilot study using the system with groups of high school children in Japan and Australia and our results show the potential of the system to drive children’s interest in communicating, sharing, and discussing cultural themes with their remote peers through the social robot.

In this paper, we identify a set of socio-emotional cues and signals that are promoted by a tabletop social mediator robot in the context of a school setting. The robot adopts different roles to enhance such socio-emotional features, consequently aiding their identification by a structured annotation system. Various socio-emotional signals were observed for different robot roles, as well as different cues (gaze, speech). Future work will analyze cultural nuances as it expands the pilot to more schools worldwide.

Conveying a robot's target mood is crucial to successful social interactions. The robot's expressive performance must be appropriate, persuasive, and consistent. However, this is challenging when interactions contain a mixture of scripted and improvised content, such as those generated by language models. In this paper, we take on the task of teaching robots to stay in character, that is to say, exhibit consistency in mood during interactions. We start by defining a communication strategy module that allows for the top-down specification of a target robot mood for a given task, goal, or context. We then propose a mood steering framework for enforcing robot mood consistency throughout an interaction that supports several target moods. Our framework consists of two components: 1. expressivity steering specifies the speech and behavior to be used by the robot to convey a target mood, and 2. language model steering ensures that improvised language is consistent with the robot's target mood. As a first step toward identifying effective communication strategies, we implement grumpy and cheerful strategies for a collaborative storytelling game and compare them to a neutral baseline. Evaluation in a collaborative storytelling game shows that our approach generates robot behavior that successfully conveys the robot's target mood throughout gameplay and language model steering generates story contributions that capture the target mood without quality degradation and raises important issues for communication strategy design.

The eyes play a significant role in how robots are perceived socially by humans due to the eye’s centrality in human communication. To date there has been no consistent or reliable system for designing and transferring affective emotional eye gestures to anthropomorphized social robots. Combining research findings from Oculesics, Laban Movement Analysis and the Twelve Principles of Animation, this paper discusses the design and evaluation of the prototype LMA12-O framework for the purpose of maximising the emotive communication potential of eye gestures in anthropomorphized social robots. Results of initial user testings evidenced LMA12-O to be effective in designing affective emotional eye gestures in the test robot with important considerations for future iterations of this framework.

Collaborative storytelling has long been a goal of social robotics, however, much of this research is limited in interactivity or assumes that story content is curated. In this paper, we present a working fully-automatic collaborative storytelling robot, which can collaborate with a person to create a unique, improvised story by using a large-scale neural language model to dynamically generate continuations to a story. Because effective storytelling requires engaging the emotions of participants, we explore several modalities of procedurally-generated expressivity: 1. an expressive text-to-speech voice with several delivery styles, 2. physical and verbal reactions performed by the robot, and 3. an external display used to show instructions and graphics during storytelling.To understand the issues associated with improvised collaborative storytelling with a social robot, we conduct an online survey and elicitation study with a group of online observers of collaborative storytelling gameplay, comparing several expressivity strategies in terms of storytelling-related characteristics, expressivity characteristics, and personality traits as measured by RoSAS. This evaluation showed that expressivity strategies using both emotive voice and performed reactions were perceived to be more competent storytellers and more strongly associated with positive personality traits.

In this paper, we explore the storytelling potential of a robot. We exploit the use of creative contents that maximize the embodied communication affordance of the empathic robot Haru. We identify the elements in storytelling such as narration, agency, engagement and education and synthesized these into the robot. Through effective design we investigated the possible answers that could leverage the limitations and the challenges in developing storytelling applications through a robotic medium. Our preliminary findings show that the use of an embodied agent such as a robot in storytelling only has meaning when its communicative affordance (i.e. embodiment, expressiveness, and other modalities) is tapped, adding new dimension to the experience. Otherwise, traditional storytelling delivery (e.g. tablet) without the use of embodiment will suffice. Hence, robots need to be performers rather than just mere props in storytelling.

Defining a robot's expressivity is a difficult task that requires thoughtful consideration of the potential of various robot modalities and a model of communication that humans understand. Humanoid and zoomorphic-designed robots can easily take cues from human and animals, respectively when designing their expressivity. However, a robot design that is neither human nor animal-like does not have a clear model to follow in terms of designing expressivity. Animation presents a potential model in these circumstances as animated characters in movies take various forms, sizes, shapes and styles, and are successful in defining expressivity that is widely accepted across different languages and cultures. In this paper, we discuss the development and design of the expressivity of Haru, a table top robot that is neither human nor animal-like and the application of animation expertise to the holistic treatment of the different modalities. The method maximizes animation techniques and expertise normally applied to movies to generate expressivity that is then transferred to the robot hardware. Experimental results show that the robot's expressivity generated using our method is easily understood and are preferred to the conventional approach of generating expressions.

This paper discusses the design and development of an experimental tabletop robot called "Haru" based on design thinking methodology. Right from the very beginning of the design process, we have brought an interdisciplinary team that includes animators, performers and sketch artists to help create the first iteration of a distinctive anthropomorphic robot design based on a concept that leverages form factor with functionality. Its unassuming physical affordance is intended to keep human expectation grounded while its actual interactive potential stokes human interest. The meticulous combination of both subtle and pronounced mechanical movements together with its stunning visual displays, highlight its affective affordance. As a result, we have developed the first iteration of our tabletop robot rich in affective potential for use in different research fields involving long-term human-robot interaction.