The fuzzy inference system for obstacle avoidance developed in this paper is designed for NAO humanoid robot. The fuzzy obstacle avoidance (Fuzzy OA) has been tested in Webots virtual environment and the results showed that this method is almost two times faster than the Naoqi framework obstacle avoidance (Naoqi OA) while the robot is much more stable. Because the fuzzy inference system is a method that relies on trial an error and experience, the obstacle avoidance algorithm is subject to improvements. Future developments will take into account these results and will add other fuzzy inference systems for navigation, in order to get more autonomy for Nao robot.

Are social robots more interesting than humans? Quantitative assessment of Joint Attention in autistic and typically developing children.

Conventional upper limb rehabilitation methods often encounter significant obstacles, including high costs, limited accessibility, and reduced patient adherence. Emerging technological solutions, such as telerehabilitation, virtual reality (VR), and wearable sensor-based systems, address some of these challenges but still face issues concerning supervision quality, affordability, and usability. To overcome these limitations, this study presents an innovative and cost-effective rehabilitation system based on advanced computer vision techniques and artificial intelligence (AI). Developed using Python (3.11.5), the proposed system utilizes a standard webcam in conjunction with robust pose estimation algorithms to provide real-time analysis of patient movements during guided upper limb exercises. Instructional exercise videos featuring an NAO robot facilitate patient engagement and consistency in practice. The system generates instant quantitative feedback on movement precision, repetition accuracy, and exercise phase completion. The core advantages of the proposed approach include minimal equipment requirements, affordability, ease of setup, and enhanced interactive guidance compared to traditional telerehabilitation methods. By reducing the complexity and expense associated with many VR and wearable-sensor solutions, while acknowledging that some lower-cost and haptic-enabled VR options exist, this single-webcam approach aims to broaden access to guided home rehabilitation without specialized hardware.

ObjectivesThis study investigates how to facilitate the use of the social robot NAO in medical settings to support interactions with children diagnosed with Autism Spectrum Disorder (ASD). The objective was to develop intuitive control methods that enable healthcare professionals to easily integrate the robot into clinical practice.MethodsTwo control modes were designed: Puppet mode, where clinicians manually operate the robot via a graphical console, and Assistant mode, where a Large Language Model translates clinicians’ spoken requests into robot actions and dialogue. Twenty-three doctors evaluated both modes through video demonstrations and completed questionnaires assessing usability, usefulness, and ethical acceptability.ResultsBoth modes were considered effective and user-friendly. Assistant mode was perceived as more intuitive and adaptable, facilitating seamless interaction, whereas Puppet mode was judged slightly more reassuring for patients and somewhat more appropriate in terms of robot actions.ConclusionOverall, both approaches were positively received, with Assistant mode emerging as the preferred option for integration into clinical workflows due to its perceived simplicity and flexibility. These findings highlight clinicians’ positive perceptions of two novel control modes and emphasize NAO’s potential to enhance patient engagement and reduce stress. Further empirical validation with children in real clinical trials is warranted to confirm these benefits and optimize robot-assisted interventions in ASD care.

Although numerous studies have examined the field of Socially Assistive Robots (SARs), less attention has been paid to evaluating the role of the NAO robot in enhancing the communication skills of children with autism spectrum disorder (ASD) across two distinct time periods (2013–2019 and 2020–2025), highlighting advancements in both SAR-based interventions and NAO’s technological capabilities. A systematic search was conducted across international databases, using strict inclusion and exclusion criteria, resulting in the selection of 44 relevant studies. This review synthesizes findings on the types of interventions implemented, NAO’s functional characteristics, and its effectiveness in facilitating social communication. The results indicate a positive impact on children’s communication abilities, with more recent studies (2020–2025) reporting greater improvements, possibly due to advancements in NAO’s capabilities and the refinement of intervention methodologies. To the best of our knowledge, this is the first systematic review to compare NAO-assisted interventions across two different periods, offering novel insights into the evolving role of SARs in autism therapy.

ABSTRACT Bilingualism is a widespread linguistic phenomenon globally, and Kazakhstan is no exception. The interplay between bilingualism and autism is an emerging area of research being explored far and wide. The current study examines the socio-emotional outcomes of 34 monolingual and bilingual children with Autism Spectrum Disorder (ASD), aged 3 to 12 years, in the context of robot-assisted autism therapy (RAAT). The children participated in an average of five sessions with the NAO robot in a rehabilitation center. The findings reveal that bilingual children demonstrated socio-emotional outcomes and engagement levels comparable to their monolingual peers. However, monolingual children exhibited significantly higher levels of positive affect, as indicated by more frequent smiling, challenging the notion of bilingual advantage in this context. Additionally, ethnicity did not moderate the children’s engagement with the social robot, indicating no significant cultural differences based on ethnic background. Notably, some differences in social outcomes were observed based on the children’s first language (L1). These findings highlight the influence of local linguistic and cultural realities on socio-emotional outcomes in RAAT. The study provides valuable insights into the relationship between language, autism, and robot-assisted therapy, with implications for future research and therapeutic practices.

This study examines user experience evolution across three repeated interactions with an on-screen NAO robot designed to express artificial empathy through verbal communication and music. The participant numbers across the three interactions were N1 = 139, N2 = 129, and N3 = 121 respectively, with 121 participants completing all sessions. During interaction, the robot gave empathic feedback and/or played music to the participant as a token of empathy. Repeated measures MANCOVA and Structural Equation Modeling revealed that initial bonding tendencies and perceptions of the robot trying to be empathetic faded over time. In its place, a tendency emerged of the robot becoming more personally relevant and remarkably, its design appeared to become more realistic, like a human being. When the robot merely tried empathetic conversation or just played music, participants were disappointed about its capabilities, visible in increased levels of negative valence. Bonding and perceived empathy flourished when the robot played music while talking empathically in chorus, a mutual reinforcement effect. At first, for the loneliest individuals, the mere presence of the robot, rather than its empathic behaviors, was more influential in determining the robot’s relevance to their concerns. These results underscore the importance of a multimodal approach in designing empathic robots.

Modelling effective human-robot interaction requires the robot to detect and respond to engagement signals, which include attention, interest, and empathy. This study explores the application of automated gaze-labelling techniques, among the use of other measures connected to the emotional, cognitive and behavioural engagement constructs, to classify the engagement in child-robot interactions using the NAO robot in a storytelling paradigm. A total of 102 children, aged 7 to 9, participated in structured individual sessions recorded for gaze analysis and engagement scoring. Engagement measures were manually annotated by observers using the Engagement Observation Scale. Gaze-360 and K-means clustering were used for automated gaze labelling. According to the assigned engagement score, 38 children were included in the study and divided into low/high engagement group. Fifteen features were extracted and organized in 4 datasets considering the engagement constructs (i.e. affective, behavioural and cognitive). These datasets were classified with 4 machine learning techniques namely Support Vector Machine (linear and quadratic kernel), Decision Tree and K-Nearest Neighbour according to the high/low engagement rate. Results indicate an engagement recognition accuracy >89% for the best configuration that includes features related to the engagement of all constructs. This result suggests that the set of extracted features can be used to classify the level of engagement.

Abstract Comprehensive sex education (SE) in schools plays a vital role in establishing a profound link to and actively advocating for sexual health, aiming at educating children about sexual health, ethics, and behaviour. The implementation of SE in elementary schools can significantly transform students’ attitudes and comprehension of sexual knowledge. However, teaching SE has been challenging at times due to students’ beliefs, attitudes, and occasional shyness or emotional reservations. Socially assistive robots (SARs) sometimes are perceived as more trustworthy than humans, based on research showing that they are not anticipated as judgmental. Inspired by those evidences, this study aims to assess the success of a SAR as a facilitator for SE lessons for elementary school students. We conducted two experiments, (a) a group activity in the school classroom where the Nao robot gave a SE lecture, and we evaluated how much information the students acquired from the lecture, and (b) an individual activity where the students interacted 1:1 with the robot, and we evaluated their attitudes towards the subject of SE, and if they felt comfortable to ask SE related questions to the robot. Data based on given pre- and post-questionnaires and video annotations demonstrated that the SAR statistically significantly improved students’ attitudes towards SE. Moreover, they addressed to the robot questions regarding SE and body parts. The study also highlights the SAR characteristics that make them efficient to support SE, such as their embodiment and non-judgmental behavior. This study is unique in its focus on emphasizing the SAR’s potential to support SE for elementary school students in a real class environment.

Given their embodied social nature, robots have the potential to facilitate educational processes. As childhood is an opportune age to introduce a new language, robots could be particularly useful in the area of second language (L2) learning for children. This study assessed whether a social robot could provide efficient teaching to young children and how social cues of joint attention (pointing and gazing) can influence L2 learning. 82 children participated in an experiment with the Nao robot telling a story in French, adding -or not- joint attention gestures to the story (interactive versus non interactive robot). During the storytelling, target-words were frequently repeated by the robot and were then used to evaluate children’s learning through a preferential looking procedure (PLP) with an eye-tracking system, thus providing a quantitative measurement of learning. To our knowledge, no other study has combined a storytelling task with eye-tracking to quantify learning gains. Both behavioral and gaze data indicated that children are able to learn the vocabulary provided during storytelling by the two types of robots, although there is an advantage for the interactive robot that triggered more significant learning results (particularly for gaze data that is a more reliable measure than pointing). Our findings suggest that social robots can be used effectively as teachers for L2 learning, but also that learning can be influenced positively by their social nature and behaviors. Indeed, providing explicit social signals in a timely manner, such as joint attention markers, seems to be particularly important for more efficient learning.

Socially Assistive Robots are studied in different child–robot interaction settings. However, logistical constraints limit accessibility, particularly affecting timely support for mental wellbeing. In this work, we have investigated whether online interactions with a robot can be used for the assessment of mental wellbeing in children. The children (N = 40, 20 girls and 20 boys; 8–13 years) interacted with the Nao robot (30–45 mins) over three sessions, at least a week apart. Audio-visual recordings were collected throughout the sessions that concluded with the children answering user perception questionnaires pertaining to their anxiety toward the robot, and the robot’s abilities. We divided the participants into three wellbeing clusters (low, med, and high tertiles) using their responses to the Short Moods and Feelings Questionnaire (SMFQ) and further analyzed how their wellbeing and their perceptions of the robot changed over the wellbeing tertiles, across sessions and across participants’ gender. Our primary findings suggest that (I) online-mediated interactions with robots can be effective in assessing children’s mental wellbeing over time, and (II) children’s overall perception of the robot either improved or remained consistent across time. Supplementary exploratory analyses have also revealed that the gender of the children affected their wellbeing assessments with interactions effectively distinguishing between varying levels of wellbeing for both boys and girls for the first session and only for boys during the second session. The analyses have also revealed that girls have a higher opinion of the robot as a confidante as compared with boys. Findings from this work affirm the potential of using online-mediated interactions with robots for the assessment of the mental wellbeing of children.

This study introduces the REHAB-PAL system, an interactive solution for home-based rehabilitation in children with cerebral palsy. To verify its functioning and usability, the system was tested with physical therapists and rehabilitation specialists simulating patient roles. The system was evaluated for its ability to track users' movements during rehabilitation exercises and assess the quality of their performance. A look-up table was developed to correlate kinematic movement characteristics with clinical scores that quantify exercise quality. To enhance exercise performance and promote adherence, the system integrates NAO social robot, programmed to provide verbal instructions, demonstrate exercises, and deliver personalized feedback based on users' movement quality. Feedback delivery was guided by a decisional strategy, prioritizing specific kinematic variables tracked by the system. Participants were asked to perform a rehabilitation session under three experimental conditions: 1) Interaction with the physical NAO robot, 2) Interaction with a virtual robot displayed on a computer screen, and 3) Receiving instructions without the use of a robot. The REHAB-PAL system accurately detected participants' kinematics and automatically generated correct movement scores. Questionnaires revealed participants' preference for the interaction with the physical robot, associated with highest levels of engagement and seen as the most effective means of delivering exercise instructions.

Cognitive load refers to the mental resources used for executing simultaneous tasks. Since these resources are limited, individuals can only process a specific amount of information at a time. Daily activities often involve mentally demanding tasks, which is why social robots have been proposed to simplify them and support users. This study aimed to verify whether and how a social robot can enhance the performance and support the management of cognitive load. Participants completed a baseline where a cognitive activity was carried out without support, and three other conditions where similar activities of increasing difficulty were collaboratively made with the NAO robot. In each condition, errors, time, and perceived cognitive load were measured. Results revealed that the robot improved performance and perceived cognitive load when compared to the baseline, but this support was then thwarted by excessive levels of cognitive load. Future research should focus on developing and designing collaborative human–robot interactions that consider the user’s mental demand, to promote effective and personalized robotic help for independent living.

Robots are reducing the various workload of humans in numerous fields, shaping many new scientific areas. This review provides an overview of research and developments conducted on the humanoid robot NAO between the years 2020 and 2024. It encompasses a general examination from the robot's physical structure to its hardware and software components. The review categorizes studies related to NAO into three main areas: Human-Robot Interactions, Navigation, and Others. The explanation of recent developments in NAO robot aims to facilitate a deeper understanding of potential advancements in the future of robotics.

Children’s environments are radically modified by introducing artificial intelligence-based technology that can mimic human socio-emotional capabilities. Artificial intelligence facilitated the transition from computers to real-world embodied physical systems such as social robots, anthropomorphic artefacts with implications for child development and a distinctly radical innovation compared to all previous technologies in classrooms. Empirical research of child-learning process in an authentic classroom and teachers’ perception of how children perceive anthropomorphic robots is deficient. Teachers’ knowledge of students’ perception of technology is essential. Social robots are anticipated for future generations of teachers and students. A two-part survey applied ASOR ascription of mental capacities, socio-practical capacities and socio-moral status. Involved were two samples, elementary school students aged 11–12 and preservice teachers. In the first part, students’ perceptions of a social robot were examined. The NAO robot-assisted lesson was conducted in a regular classroom according to a regular curriculum addressing the role of technology in society, followed by a survey. In the second part, preservice teachers assess children’s perceptions of social robots. The study objective was (a) preservice teachers’ knowledge of which capacities and status students attribute to the NAO robot in an educational setting compared with (b) the capacities and status students attribute to the NAO robot. In data analysis, preservice teachers’ and students’ scores were compared. Our findings show that (a) preservice teachers don’t know students’ perceptions of NAO; (b) compared the perceptions of 11-year-old fifth graders and 12-year-old sixth graders showed no statistically significant difference. Examining gender differences three items were identified.

Humanoid robots, designed to resemble the human form, are increasingly becoming an integral technology used in healthcare and education. This paper focuses on the NAO robot, which is engineered with advanced capabilities such as speech recognition, facial expression analysis, and complex motor functions. These features enable NAO to interact with humans more naturally and intuitively. The NAO robot's versatility allows it to assist in therapeutic settings, enhance learning experiences, and provide emotional support. This scoping review describes the ways in which NAO robots' have developed for their application and implementation in healthcare and education from a human factors perspective. Findings revealed an increasing range of applications of the robot in healthcare for supporting well-being management, social communication with children, engagement and learning about health, as well as monitoring and supporting healthcare for the elderly and the frail.

Generating natural and expressive co-speech gestures for conversational virtual agents and social robots is crucial for enhancing their acceptability and usability in real-world contexts. However, this task is complicated by strong cultural and linguistic influences on gesture patterns, exacerbated by the limited availability of cross-cultural co-speech gesture datasets. To address this gap, we introduce the TED-Culture Dataset, a novel dataset derived from TED talks, designed to enable cross-cultural gesture generation based on linguistic cues. We propose a generative model based on the Stable Diffusion architecture, which we evaluate on both the TED-Expressive Dataset and the TED-Culture Dataset. The model is further implemented on the NAO robot to assess real-time performance. Our model surpasses state-of-the-art baselines in gesture naturalness and exhibits rapid convergence across languages, specifically Indonesian, Japanese, and Italian. Objective and subjective evaluations confirm improvements in communicative effectiveness. Notably, results reveal that individuals are more critical of gestures in their native language, expecting higher generative performance in familiar linguistic contexts. By releasing the TED-Culture Dataset, we facilitate future research on multilingual gesture generation for embodied agents. The study underscores the importance of cultural and linguistic adaptation in co-speech gesture synthesis, with implications for human-robot interaction design.

This work aims to advance the understanding of group dynamics in robot-child interactions, focusing on whether, during a motor-imitation task led by a Nao robot, children might be influenced in their action executions by other group members - human or robotic. After testing eighteen groups of four children and teenagers, our findings indicate that participants tend to disregard the robot when it performs atypical gestures, preferring instead to imitate the actions of a human peer. Moreover, we found evidence that, in this scenario, assigning a leadership role to the robot does not, by itself, guarantee compliance from human group members; broader group dynamics must also be taken into account. Further results show that participants are significantly more likely to imitate the robot's action when the "proactive" group members (i.e., those who initiate actions first) conform to Nao, compared to when they do not. Previous studies suggest that the mutual influence of group members can facilitate interaction with a robotic agent; however, our findings show that the presence of proactive members could also undermine the group's conformity to the robot. Additionally, these findings highlight the importance of personalizing robots to better integrate into specific group dynamics, enhancing their ability to influence different groups effectively.

To unlock more aspects of human cognitive structuring, human–AI and human–robot interactions require increasingly advanced communication skills on both the human and robot sides. This paper compares three methods of retrieving cultural heritage information in primary school education: search engines, large language models (LLMs), and the NAO humanoid robot, which serves as a facilitator with programmed answering capabilities for convergent questions. Human–robot interaction has become a critical aspect of modern education, with robots like the NAO providing new opportunities for engaging and personalized learning experiences. The NAO, with its anthropomorphic design and ability to interact with students, presents a unique approach to fostering deeper connections with educational content, particularly in the context of cultural heritage. The paper includes an introduction, extensive literature review, methodology, research results from student questionnaires, and conclusions. The findings highlight the potential of intelligent and embodied technologies for enhancing knowledge retrieval and engagement, demonstrating the NAO’s ability to adapt to student needs and facilitate more dynamic learning interactions.

For millions of deaf-mute individuals, sign language is the only means of communication; this creates barriers in daily interactions with non-signers, leading to the exclusion of these individuals in many areas of daily life. To address this, we propose a real-time sign language translation system using a Transformer model enhanced with a knowledge graph, designed for Human-Robot Interaction (HRI) with NAO robots. Our system bridges the communication gap by translating gestures into natural language (text). We used the RWTH-PHOENIX-Weather 2014T dataset for initial training, achieving a BLEU score of 29.1 and a Word Error Rate (WER) of 18.2% surpassing the baseline model. Due to the domain shift between human gestures and NAO robot gestures, we created a NAO-specific dataset and fine-tuned the model using transfer learning to accommodate an adapted environment and kinematic constraints that do not match the environment in which the robot was deployed. This reduced the WER to 17.6% and increased the BLEU score to 29.9. We tested our model’s capability with dynamic and practical HRI scenarios through comparative experiments in Webots. Integrating a knowledge graph into our model improved contextual disambiguation, significantly enhancing translation accuracy for gestures that weren't clear. Through effectively translating gestures into natural language, our system demonstrates strong potential for practical robotic applications that promote social accessibility.