Robots interacting with humans pose data privacy risks, potentially leading to uncontrollable leaks of personal information. Robots interact with older adults in public places, homes, care facilities, and hospitals. Older adults may have concerns about privacy issues related to robots and would benefit from developing their robot literacy skills regarding data privacy. Research on older adults’ robot literacy in relation to data privacy is scarce. We conducted a qualitative and explorative human-centered design study with care home residents (N=9) to explore their perceptions of and interest in robot data privacy literacy. In the study, they interacted with an early prototype of a robot-assisted learning application implemented on the social robot QTrobot. Participants were concerned about the "superpowers" and data storage of robots. Based on our findings and existing literature, we redesigned the prototype into "Myth Buster," a robot-assisted learning application aimed at enhancing older adults’ data privacy literacy regarding robots. Our work contributes to the understanding of older adults’ data privacy literacy, which is currently under-researched in Human-Robot Interaction. We also present design-relevant insights for developing robot-assisted data privacy learning applications to enhance robot literacy of older adults.

We adopt a novel approach of co-learning between elementary school pupils and university students around social robots and robotics. Social robots provide a motivational learning tool for various learning tasks. Having different learner groups together may bring in new insights, perspectives and learning. Although social robots provide an interesting platform for learning, they have challenges in terms of data privacy, as they track, process and transfer personal data. These matters should be carefully considered. We describe a qualitative and exploratory study including two phases: 1) design of co-learning activities (N=16), and 2) evaluation of co-learning activities (N=56). All co-learning tasks were developed by utilizing privacy-sensitive robotics approach and the tasks included some learning content about data privacy. The evaluation was conducted as co-learning workshops with school pupils of 10 to 15 years and international university students. We report findings about the co-learning experience of these learner groups, as well as their data privacy learnings on social robots. We also present considerations for educational robotics from the data privacy perspective.

Non-anthropomorphic robots can be cost-effective and efficient choice in certain context in comparison to social or humanoid robots. However, introduction of nonanthropomorphic robots can evoke uncertainty and anxiety due to novelty of technology. The goal of this paper is to explore personality design space for non-anthropomorphic wellness robots in office environment to foster acceptance among users. Through Participatory Design approach, we explored appropriate personalities for a well-being robot, which would detect employees’ sitting posture and suggest small wellness interventions. We addressed the following research questions: (i) How can personalities be designed and integrated to non-anthropomorphic wellness robots to promote users’ acceptance? (ii) How do the users perceive designed personalities of non-anthropomorphic wellness robot in the office context? We conducted one contextual inquiry (n=5) and one co-design workshop (n=15) followed by evaluation (n=5) in IT office environment with office employees. As a contribution to the paper, we present personalities and design implications for non-anthropomorphic wellness robot in the office context. Our contribution will serve as a guideline for designers to explore and expand their knowledge on designing robot personalities for non-anthropomorphic robots in the context.

The integration of collaborative robots (cobots) is changing manufacturing and production processes in factories. When cobots are designed to be efficient, skillful and safe to interact with, workers can collaborate with them conveniently. As workers often work with cobots intensively, it is crucial to explore the user experience (UX) of cobots. The goal of our research is to explore how factory cobots could be used in ways that support pleasurable worker experiences. We adapted "research through design" (RtD) to conduct exploratory research on novel interactions related to affectiveness, playfulness and relatedness in human-robot collaboration (HRC) using collaborative robot arm, Panda. RtD is a method that utilizes methods and practices of design to produce new knowledge. We conducted an exploratory study with 33 participants to evaluate three HRC storyboards scenarios in two complementary remote workshops. The findings report suitability of affective and playful behavior of cobots in an industrial setting. In addition, we deduced that personality of the robot plays a crutial role in HRC.