The introduction of social robots in preschool settings has become a common research strategy for addressing educational challenges. Although teachers and educators play a central role in classroom dynamics, they are often underrepresented in studies on educational robots. Often, robots are presented as “black-boxes”, with little attention paid to providing teachers with dedicated training. This study describes the design and implementation of the Teacher Professional Development Research (TPDR) as a structured method for integrating social robots into early education, supporting teachers and educators. TPDR is an established educational practice that addresses pedagogical issues by engaging teachers as actors in the research process. Our project involved deploying a robot in four preschools and one nursery with a multicultural setting, primarily to foster intercultural integration. Both quantitative and qualitative data were collected to evaluate the impact of this approach on teachers' attitudes and willingness to adopt the robot. Findings indicate that the teachers gained a greater awareness of the robot’s social presence and a clearer understanding of its educational potential. There was also an overall positive shift in their intercultural sensitivity.

Co-design is widely used in educational contexts to involve stakeholders and make them active participants in the learning process. This study presents the co-design process conducted with teachers, educators, and families before introducing a social robot in four Italian preschools and a nursery. The robot is expected to promote intercultural awareness in a highly culturally diverse educational environment. We consider the co-design process an essential step, as teachers and educators, by knowing the social rules and pedagogical concepts of each specific educational context, can tailor the unique characteristics of the robot (being embodied and equipped with social behaviors) to effectively benefit their pedagogical reality. In addition, stakeholders, through co-design, can incorporate cultural awareness of children and their families into the robot design. The results obtained after the co-design process highlight that the co-creation of robotic applications and the robot’s imagery before its actual introduction into activities is fundamental for developing a framework tailored to a specific educational context, for reformulating the project’s prerogatives in such a way that it becomes part of the educational reality, and for giving teachers the opportunity to familiarize themselves with the robot, understand its capabilities, and exploit them according to their educational context.

The synergic orchestration of the cognitive and psychological dimensions characterizes human intelligence. Accordingly, carefully designing this mechanism in artificial intelligence can be a successful strategy to increase human likeness in a robot, enhancing mutual understanding and building a more natural and intuitive interaction. For this purpose, the main contribution of this work is a psychological and cognitive architecture tailored for HRI based on the interplay between robotic personality and memory-based cognitive processes. Indeed, the artificial personality manifests itself not only in various aspects of the behavior but also within the action selection process, which is closely intertwined with personality-dependent hedonic experiences linked to memories. Within this paper, we propose a task- and platform-independent framework, evaluated in a multiparty collaborative scenario. Obtained results show that a robot connected to our proposed framework is perceived as a cognitive agent capable of manifesting perceivable and distinguishable personality traits.

The goal-directed behavior observed in humans arises from the intricate interplay of various processes, including personality dynamics, emotional responses to others, memory encoding, the anticipation of future actions, and associated hedonic experiences. Integrating these multiple processes characteristic of human intelligence into a robotic framework aims to enhance the human-likeness of artificial agents and facilitate more natural and intuitive interactions with humans.For this purpose, in this paper, we propose a comprehensive psychological and cognitive architecture where, personality, as it happens for humans, not only influences the execution of actions but also shapes internal reactions to human emotions and guides anticipatory decision-making processes tailored to the agent’s traits. We demonstrate the framework’s effectiveness in generating perceivable synthetic personalities through an experiment involving participants in a dyadic conversation scenario with a digital human, where the digital human’s behavior is driven by its assigned personality. The results show that participants accurately perceive the artificial personality displayed by the digital human. We also demonstrate the potential of our robotic framework to bridge the gap between cognitive and psychological agents, as the findings highlight its ability to create a cognitively and emotionally intelligent digital human.

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.

Robotic personalities broaden the social dimension of an agent creating feelings of comfort in humans. In this work, we propose a taxonomy model to generate synthetic personalities based on the Big Five model. In particular, this paper describes a generalized framework for artificial personalities whose core is a Bidirectional Encoder Representations from Transformers (BERT) model capable of associating behaviors tailored to each personality trait. The generator is fully integrated within a modular software architecture capable of performing social interaction tasks, being at the same time task-and platform-independent. The proposed framework has been tested in a pilot experiment where human subjects were asked to interact with a humanoid robot displaying different personality traits. Results obtained by the statistical analysis of validated questionnaires show interesting insights about the capability of the framework of generating personalities that are clearly perceived by users, and whose personality dimensions are strongly distinguishable.