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.

To enable a smooth co-existence between robots and human workers in an industrial setting, we implemented two robot working memory configurations onto a mobile manipulator RB-KAIROS+ robot (Robotnik): A GRU-based one and a bioinspired alternative called WorkMATe which enabled the robot to adapt its navigation strategy depending on the presence of human workers. To evaluate the two working memory configurations against a non adaptive behavior, we tested a possible co-working scenario between two ostensible workers and the RB- KAIROS+ robot navigating in two mocked industrial set-ups. The application of behavioral adaptation through a working memory component was highly beneficial as it led to reduced energy consumption and, more importantly, to fewer acceleration anomalies in robot navigation than the non adaptive one. This suggests that a robot’s adaptive navigation through working memory can increase workers’ safety and improve the efficiency of the human-robot system as a whole in industrial applications.

Games are often used to foster human partners' engagement and natural behavior, even when they are played with or against robots. Therefore, beyond their entertainment value, games represent ideal interaction paradigms where to investigate natural human-robot interaction and to foster robots' diffusion in the society. However, most of the state-of-the-art games involving robots, are driven with a Wizard of Oz approach. To address this limitation, we present an end-to-end (E2E) architecture to enable the iCub robotic platform to autonomously lead an entertaining magic card trick with human partners. We demonstrate that with this architecture a robot is capable of autonomously directing the game from beginning to end. In particular, the robot could detect in real-time when the players lied in the description of one card in their hands (the secret card). In a validation experiment the robot achieved an accuracy of 88.2% (against a chance level of 16.6%) in detecting the secret card while the social interaction naturally unfolded. The results demonstrate the feasibility of our approach and its effectiveness in entertaining the players and maintaining their engagement. Additionally, we provide evidence on the possibility to detect important measures of the human partner`s inner state such as cognitive load related to lie creation with pupillometry in a short and ecological game-like interaction with a robot.