# Recent Advances in Human-Robot Interactions
## 2 An Overview of Published Articles  
The study conducted by [PERSON] et al. [21] highlights the crucial role that the development of methods for generating appropriate prosody in the responses of embodied conversational agents will play in the coming years. Specifically, it is projected that the number of smart devices will exceed 100 billion by 2050, many of which will integrate conversational user interfaces. The experimental study analyzed the reactions of individuals from diverse cultural backgrounds to the prosody and phonetic choices of a fictitious language based on IPA symbols used by a virtual conversational agent. The results include an analysis of responses to a post-experimental Likert-scale questionnaire and emotions estimated through facial expression analysis. This approach enabled the construction of a phonetic embedding matrix. The authors conclude that there is no common cross-cultural basis for prosodic impressions and that phonetic elements with acoustic similarity are not necessarily close within the embedding space. Recent advancements in human-robot interaction (HRI) have also been highlighted by the study of [PERSON] et al. [22]. Although humanoid social robots have undergone rigorous testing and have been implemented in various contexts in recent years, research on robot-child interactions remains limited, particularly concerning deaf children. The experimental study conducted by [PERSON] et al. [22] explores the ability of both hearing and deaf children to interact with a humanoid robot and recognize the emotions (happiness, sadness, and anger) expressed by it. Depending on the experimental condition, the humanoid robot responded to the emotions presented in the videos either congruently or incongruently. The results showed no differences in emotion recognition ability between the two conditions (congruent vs. incongruent). Although no significant differences emerged between hearing and deaf children, this feasibility study aims to provide a foundation for future research in HRI and inclusivity-related topics. Another study [23] investigated the role of trust in human-robot collaboration (HRC). The authors designed an experimental procedure that involved monitoring cerebral, electrodermal, respiratory, and ocular activity, allowing them to map both the dispositional and learned dimensions of trust. The findings provide clear evidence of the crucial role that initial interactions play in the trust-building process and its temporal evolution. Additionally, the identification of key psychophysiological indicators for trust detection and the importance of personalized assessments contribute to a deeper understanding of trust in HRC contexts. These insights are essential for enhancing human-robot interaction in collaborative settings, facilitating smoother and more effective integration. In another study, [PERSON] et al. [24] proposed the design of a self-reconfigurable centipede-type rescue robot characterized by high stability during movement. Moreover, the robot can navigate heterogeneous environments and overcome obstacles thanks to the integration of an optical sensor that facilitates life-form recognition, terrain analysis, scene comprehension, and obstacle avoidance. These features are particularly advantageous for rescue operations in complex environments. Stability tests conducted on diverse types of terrain demonstrate the robot's ability to maintain a stable trajectory even in rugged conditions, with an F1-score exceeding 93% and an average precision rate above 98%. These results confirm the robot's high reliability and efficiency in rescue operations. The study by [PERSON] et al. [25] proposes an innovative multilayer rendering method capable of more accurately approximating the motion blur effect, surpassing traditional techniques that often introduce chromatic deviations from true colors. The authors' approach stores motion vectors for each pixel, divides these vectors into multiple sampling points, and performs a backward search from the current pixel.