Social robots have great potential in supporting individuals’ physical and mental health/wellbeing. While they have been increasingly evaluated in some domains, such as with children with autism, their evaluation has not been as extensive in other areas. We present a systematic review of domains in which social robots have been evaluated specifically in health/wellbeing contexts. We ask which robots have been evaluated, who the participants were, and how participants interacted with the robots. PRISMA guidelines for systematic reviews were followed. Articles with children as participants, using a purely robotic device, and in languages other than English were excluded. A total of 9,362 peer-reviewed articles (up to February 2021) from ACM DL, IEEE Xplore, Scopus, PubMed, and PsychInfo were identified. After applying the inclusion/exclusion criteria 443 articles were included in the review. The majority of studies were conducted at care centers while studies in hospitals/clinics have seen relatively limited attention. In many cases, the social robots were not programmed for specific health-related tasks, limiting their application. We also discuss robots used in real-world settings and propose a “Personal healthcare journey,” which includes different stages of one’s life which could benefit from a social robot, with the goal of increasing long-term adoption of social robots for supporting health/wellbeing.

Dangers in modern human society are commonly attributed to the safety of online activities. In the domain of cybersecurity, Social Engineering (SE) relates to how attackers manipulate and coerce their targets into divulging sensitive information. One major problem in designing social engineering defenses is making users aware they are being targeted. In the context of fostering human empowerment and building an inclusive society, we explore the possibility of leveraging social robot companions to provide improved protection for individuals and companies against cybersecurity attacks, specifically focusing on the realm of social engineering (SE) tactics. We asked participants to play an immersive interactive storytelling game, challenging them with risky and social-engineering-related decisions and monitoring their explicit (i.e., decisions) and implicit (i.e., mouse trajectories and facial expressions) behavior. After each decision, the Furhat tabletop robot intervened, always suggesting the not-selected option. We compared two Compliance Gaining Behaviors (CGBs) the robot could use, either leveraging affection with the participants or logical thinking. Overall, Furhat’s interventions increased the acceptance of risky and SE proposals. However, comparing the situations in which the robot tried to convince participants to avoid a social engineering request to those in which it tried to persuade them to accept it, the former was significantly more successful. Also, participants struggled with ignoring Furhat’s advice, as shown by their more uncertain mouse trajectories and negative emotional valence. From the latter results, we trained a Decision Tree model, based on mouse trajectory features only, to predict if participants would change their minds with an accuracy of 64.9%. Such defense mechanisms could help better understand users’ decision-making process in cybersecurity and social engineering, designing more helpful and supportive robot companions.

The use of collaborative robots is becoming more widespread across industries. This makes it essential to study robot planning in order to work effectively and smoothly with human teammates while maintaining a positive human perception of the robots. This paper evaluates the influence of a robot’s strategy and decision making on the participants’ perception of the robot. We designed an online experiment where a robot and participants need to collaborate and organize a set of objects. We studied three different strategies where the robot either prioritizes the human’s objective, its own objective, or uses a balanced strategy. We then analyze and report the results based on participants’ answers to questionnaires before and after the experiment, their comments, and their actions during the experiment. The results show that strategies prioritizing the human’s objective, or balancing between the robot’s and the human’s objectives can effectively improve participants’ perception of the robot and create a collaborative environment.

Technological advancements in creating and commercializing novel unobtrusive and wearable physiological sensors generate new opportunities to develop adaptive human-robot interaction (HRI) scenarios. Detecting complex human states such as engagement and stress when interacting with social agents could bring numerous advantages to create meaningful interactive experiences. Despite being widely used to explain human behaviors in post-interaction analysis with social agents, using bodily signals to create more adaptive and responsive systems remains an open challenge. This paper presents the development of an open-source, integrative, and modular library created to facilitate the design of physiologically adaptive HRI scenarios. The HRI Physio Lib streamlines the acquisition, analysis, and translation of human body signals to additional dimensions of perception in HRI applications using social robots. The software framework has four main components: signal acquisition, processing and analysis, social robot and communication, and scenario and adaptation. Information gathered from the sensors is synchronized and processed to allow designers to create adaptive systems that can respond to detected human states. This paper describes the library and presents a use case that uses a humanoid robot as a cardio-aware exercise coach that uses heartbeats to adapt the exercise intensity to maximize cardiovascular performance. The main challenges, lessons learned, scalability of the library, and implications of the physio-adaptive coach are discussed.

Human partners are very effective at coordinating in space and time. Such ability is particular remarkable considering that visual perception of space is a complex inferential process, which is affected by individual prior experience (e.g. the history of previous stimuli). As a result, two partners might perceive differently the same stimulus. Yet, they find a way to align their perception, as demonstrated by the high degree of coordination observed in sports or even in everyday gestures as shaking hands. Robots would need a similar ability to align with their partner's perception. However, to date there is no knowledge of how the inferential mechanism supporting visual perception operates during social interaction. In the current work, we use a humanoid robot to address this question. We replicate a standard protocol for the quantification of perceptual inference in a HRI setting. Participants estimated the length of a set of segments presented by the humanoid robot iCub. The robot behaved in one condition as a mechanical arm driven by a computer and in another condition as an interactive, social partner. Even if the stimuli presented were the same in the two conditions, length perception was different when the robot was judged as an interactive agent rather than a mechanical tool. When playing with the social robot, participants relied significantly less on stimulus history. This result suggests that the brain changes optimization strategies during interaction and lay the foundations to design human-aware robot visual perception.

Authority and obedience are key regulatory elements in a society. Robots are becoming important part of our world, and are starting to interact in domains in which authority is an important aspect, as healthcare, teaching or law enforcement. Yet, there is little research on how people behave when robots show authority. In particular, although extensive investigations have been carried out on how authority can circumvent people's morality with experiments such as Milgram's or Stanford Prison, almost no research evaluated the effect of robots pushing the limits of people's own morality. This experiment tries to study this aspect by using a robot (geminoid) with the appearance, and thus authority of a famous person, and by pushing the boundaries asking morally controversial requests. The results show that, even though most people hesitate and recognize the requests as socially inappropriate, they obey to robots with authority. This suggests that the authority of the robot can push people to perform tasks usually considered as inappropriate.