Review of studies on NAO robot

Initial Response in HRI- a Case Study on Evaluation of Child with Autism Spectrum Disorders Interacting with a Humanoid Robot NAO

Perception tasks that require long and frequent testing can result in a loss of attention and focus, contributing to erroneous and inconsistent data. To maintain engagement and improve reliability of data, a more interactive interface could be used. This study aims to investigate if a humanoid NAO robot could provide such an interface to improve or maintain engagement during testing. More specifically, human-robot interaction (HRI) will be explored when performing various auditory perception tasks, played in the form of games, on the NAO robot. Evaluation of the HRI will be performed using questionnaires derived from the system usability, Godspeed, and similarity-attraction scales, as well as through video analysis. Future research will extend the evaluation of the HRI to children and aim to further improve the engagement and social acceptability of the NAO robot as a game interface.

Humanoid Robot NAO Interacting with Autistic Children of Moderately Impaired Intelligence to Augment Communication Skills

Robot-based Intervention Program for Autistic Children with Humanoid Robot NAO: Initial Response in Stereotyped Behavior

Aim/Purpose: Our study is focused on prototyping, development, testing, and deployment of a new knowledge primitive for the humanoid robot assistant NAO, in order to enhance student visual learning by establishing a human-robot interaction. Background: This new primitive, utilizing a convolutional neural network (CNN), enables real-time recognition of handwritten digits captured by the NAO robot, a humanoid robot assistant developed by SoftBank Robotics. It is equipped with advanced capabilities, including a wide range of sensors, cameras, and interactive features. By integrating the proposed primitive, the NAO robot gains the ability to accurately recognize handwritten digits, contributing to improved student visual learning experiences. Methodology: Our developed primitive consists of the use of a convolutional neural network (CNN) so that the robot is able to recognize the handwriting of the digits present in the input image received in real-time. The NAO robot establishes interaction with the learners through a scenario based on a predefined assignment. In this scenario, NAO captures the digit handwritten by the learner via its camera, recognizes the digit using the deep learning model generated by the MNIST dataset, and announces to the learner the handwritten digit in the input image. The prototype is realized using the concept of a distributed system allowing the distribution of tasks in four different computing nodes. Contribution: Our research makes a significant contribution by equipping the humanoid robot NAO with a cognitive intelligence system through the integration of a new knowledge primitive based on handwriting digit recognition (HWDR). Our approach used to create and implement this primitive in the NAO robot is interesting and innovative, and presents a promising provision for enhancing the visual learning experience of children and young students with special needs, based on the use of distributed systems that divide the work using various components distributed over several nodes, coordinating their efforts to perform tasks more efficiently than a single device besides the NAO robot. Findings: We designed our model using specific parameters and a fully convolutional neural network architecture, which includes three residual depthwise separable convolutions, each followed by batch normalization and ReLU activation. To evaluate the performance of our model, we tested it on the MNIST dataset, where we achieved a remarkable accuracy, F1 score, and recall of 99%. An experiment was conducted to test our implemented primitive and see the effectiveness of this invention for enhancing visual learning in children with special needs. We developed a visual learning strategy based on the creation of engaging activities mediated by the NAO robot in an educational context. The results showed that participants achieved a strong commitment to the NAO robot, appreciating its ability to recognize handwritten digits and highlighting its promising potential to enrich visual learning experiences. Participants expressed a strong preference for teaching methods integrating assistive learning technologies, demonstrating the positive impact of our humanoid assistant robot on improving learning and visual intelligence in an educational environment. Recommendations for Practitioners: Encourage creativity and innovation in the field of robotics and special needs. This can lead to new and effective solutions that improve the lives of students with special needs. Recommendation for Researchers: Test and evaluate the proposed robotics solutions to ensure they are effective and making a positive impact. Use feedback from users, educators, and parents to refine and improve your solutions. Also, ensure that the robotics solutions are accessible to students with a range of abilities. This may involve designing solutions that are adjustable or providing alternative means of access. Impact on Society: As there are several ways to educate, there are multiple forms of learning. With the help of this learning procedure and strategy, the human teacher collaborates with the robot assistance NAO to improve visual learning among students. The findings of this research can serve as an application for the implementation of various pedagogical methods that will assist in meeting the needs of the majority of learners. Future Research: Our future research will concentrate on addressing the educational needs of students with special needs, enabling them to overcome their challenges and reach academic excellence in an inclusive environment. To achieve this goal, we plan to leverage the capabilities of social robots, which have emerged as a significant contributor to the field of human-robot interaction, particularly in facilitating inclusive education. These agents have proven to be effective in providing support to students with special needs, thereby enabling them to receive the education they need to succeed.

Socially assistive robots can play an important role in the monitoring and training of health of older adults. But before their benefits can be reaped, proper usability and a positive user experience need to be ensured. In this study, we tested the usability and user experience of a socially assistive robot (the NAO humanoid robot) to monitor and train the health of frail older adults. They were asked to complete a set of health monitoring and physical training tasks, once provided by the NAO robot, and once provided by a Tablet PC application (as a reference technology). After using each technology, they completed the System Usability Scale for usability, and a set of rating scales for perceived usefulness, enjoyment, and control. Finally, we questioned the participants’ preference for one of the technologies. All interactions were recorded on video and scrutinized for usability issues. Twenty older adults participated. They awarded both technologies ‘average’ usability scores. Perceived usefulness and enjoyment were rated as very positive for both modalities; control was scored positively. Main usability issues for NAO for these tasks were related to speech interaction (e.g., NAO’s limited speech library, NAO’s difficulty to cope with Dutch dialect), older adults’ difficulties with taking their proper role in human-robot interaction, and a lack of affordances of NAO. Seven participants preferred NAO: it was easier to use and more personal. Social robots have the potential to monitor and train the health of frail older adults, but some critical usability challenges need to be overcome first.

Robot systems are developed using various hardware and software components. In conventional design methodology, each hardware component and its software are strongly coupled such that it is not easy to replace or expand them separately. For the independent development and replacement of hardware and software components, this paper proposes a novel robot development methodology based on the abstractions of software and hardware components in a multilayer architecture for cyber-physical robot systems which conjoin computational and physical resources. We introduce a context generator and a behavior translator for the abstractions in the multilayer architecture. The context generator converts sensory value data into contexts using context scripts. The behavior translator converts a behavior, selected by a software agent that is a computer program deciding an action of a robot, into a sequence of actuator commands for robots using behavior scripts. These together enable two levels of abstraction in which software and hardware components can be developed independently of each other. As a result, software agents can work flawlessly even if hardware components are replaced and vice versa. The effectiveness and applicability of the proposed methodology are demonstrated through experiments, and the related applications are provided.

This retrospective study presents and summarizes our long-term efforts in the popularization of robotics, engineering, and artificial intelligence (STEM) using the NAO humanoid robot. By a conservative estimate, over a span of 8 years, we engaged at least a couple of thousand participants: approximately 70% were preschool children, 15% were elementary school students, and 15% were teenagers and adults. We describe several robot applications that were developed specifically for this task and assess their qualitative performance outside a controlled research setting, catering to various demographics, including those with special needs (ASD, ADHD). Five groups of applications are presented: (1) motor development activities and games, (2) children’s games, (3) theatrical performances, (4) artificial intelligence applications, and (5) data harvesting applications. Different cases of human–robot interactions are considered and evaluated according to our experience, and we discuss their weak points and potential improvements. We examine the response of the audience when confronted with a humanoid robot featuring intelligent behavior, such as conversational intelligence and emotion recognition. We consider the importance of the robot’s physical appearance, the emotional dynamics of human–robot engagement across age groups, the relevance of non-verbal cues, and analyze drawings crafted by preschool children both before and after their interaction with the NAO robot.

There are a set of basic rules for stroke order in the traditional handwriting of Chinese characters, which may be listed as from left to right, from top to bottom, first inside then outside, and so on. Humanoid robot NAO is very famous in the world now, which has many sensors such as two cameras, infrared, IMU, bumper sensors and so forth. In this paper we explore how the humanoid robot NAO can write a Chinese character according to the traditional stroke order rules. As a matter of fact, the stroke order relation between any two strokes is very important in the whole stroke orders in order to lead a well writing. Therefore, the thinning algorithm is employed to propose an effective and useful method to determine the stroke order relation between any two strokes for Chinese character writing by robot NAO based on corner detection and depth-searching. Then the implementing algorithm is given for the determination of stroke order relation, and a series of experiments are carried out to validate its feasibility and effectiveness, the accuracy can achieve 90% so as to be a very satisfied result in the current stage.

The overall context proposed in this paper is part of our long-standing goal to contribute to a group of community that suffers from Autism Spectrum Disorder (ASD); a lifelong developmental disability. The objective of this paper is to present the development of our pilot experiment protocol where children with ASD will be exposed to the humanoid robot NAO. This fully programmable humanoid offers an ideal research platform for human-robot interaction (HRI). This study serves as the platform for fundamental investigation to observe the initial response and behavior of the children in the said environment. The system utilizes external cameras, besides the robot's own visual system. Anticipated results are the real initial response and reaction of ASD children during the HRI with the humanoid robot. This shall leads to adaptation of new procedures in ASD therapy based on HRI, especially for a non-technical-expert person to be involved in the robotics intervention during the therapy session.

Purpose This paper aims to deal with the problem of designing robot behaviors (mainly to robotic arms) to express emotions. The authors study the effects of robot behaviors from our humanoid robot NAO on the subject’s emotion expression in human–robot interaction (HRI). Design/methodology/approach A method to design robot behavior through the movement primitives is proposed. Then, a novel dimensional affective model is built. Finally, the concept of action semantics is adopted to combine the robot behaviors with emotion expression. Findings For the evaluation of this combination, the authors assess positive (excited and happy) and negative (frightened and sad) emotional patterns on 20 subjects which are divided into two groups (whether they were familiar with robots). The results show that the recognition of the different emotion patterns does not have differences between the two groups and the subjects could recognize the robot behaviors with emotions. Practical implications Using affective models to guide robots’ behavior or express their intentions is highly beneficial in human–robot interaction. The authors think about several applications of the emotional motion: improve efficiency in HRI, direct people during disasters, better understanding with human partners or help people perform their tasks better. Originality/value This paper presents a method to design robot behaviors with emotion expression. Meanwhile, a similar methodology can be used in other parts (leg, torso, head and so on) of humanoid robots or non-humanoid robots, such as industrial robots.

In the field of human-robot interaction (HRI), the robot's hand-eye coordination ability is an important basis for the robot to achieve a natural interaction with human being. How to endow a humanoid robot with hand-eye coordination ability is the main content of this paper. In this paper, the issue is studied from the perspective of uncalibrated hand-eye coordination. Since humanoid robot has the nature of uncertainty and instability, the method of Kalman filter based online identification of image Jacobian matrix is applied, and an uncalibrated hand-eye coordination controller is designed for humanoid robot. Simulation shows the effectiveness of the method as well as its robustness to image observation noise. The HRI experiments on the humanoid robot NAO further illustrate the practical application value of the method.

Walking position commanded NAO robot using nonlinear disturbance observer-based fixed-time terminal sliding mode

This paper shows the primary feedback of children with Autism Spectrum Disorder (ASD) interacting with Humanoid Robot NAO. Four autistic children from Society for the Welfare of Autistic Children (SWAC) is being picked based on their neurological and physical limitations which has been identified by specialist teachers, ASD specialist researchers, and faculty members. Their Intelligence Quotient depends on question-answer based Intelligence Test has been first carried out and they have undergone the autism diagnoses based on Autism Diagnostic Observation sessions by autism specialist teachers from Society for the Welfare of Autistic Children (SWAC). The four of Children with ASD will then involve in the Robot-based Interaction Program, which started from session 1 to session 4. The interaction between Children with ASD and Humanoid Robot NAO is being recorded with Android Phone Video Camera and one mini camera included on the middle of Humanoid Robot NAO for initial feedback analysis based on verbal and non-verbal communications. The interaction session between the children and robot has been developed by using the software named choreographe of Humanoid Robot NAO.

In human---robot interaction scenarios, an intelligent robot should be able to synthesize an appropriate behavior adapted to human profile (i.e., personality). Recent research studies discussed the effect of personality traits on human verbal and nonverbal behaviors. The dynamic characteristics of the generated gestures and postures during the nonverbal communication can differ according to personality traits, which similarly can influence the verbal content of human speech. This research tries to map human verbal behavior to a corresponding verbal and nonverbal combined robot behavior based on the extraversion---introversion personality dimension. We explore the human---robot personality matching aspect and the similarity attraction principle, in addition to the different effects of the adapted combined robot behavior expressed through speech and gestures, and the adapted speech-only robot behavior, on interaction. Experiments with the humanoid NAO robot are reported.