Software Development for Autonomous and Social Robotics Systems

We can expect the field of robot end-user applications to grow, and with time, we will also see more advanced and skilled humanoid robots. To ensure that this happens in a relevant, human-friendly, and sustainable way, we need to study robots and the way they can interact with humans. We need to study both the evolving technology and human behavior, i.e., the end users (customers, clients). Understanding the usefulness and potential of social and service robots requires studying them in specific real-world contexts. This paper we disucss an experiment where we investigate the deployment of a social service robot in a retail setting. More specifically we focus on customer interactions and experiences with humanoid robots in a grocery store. By investigating real-world retail contexts, the research aims to understand the practical utility and potential of social and service robots.We used structured observation top collect data and preliminary findings reveal a range of customer reactions, from joy to fear, and highlights the novelty effect, where customers take photos and follow the robot. Findings suggest that all types of users are likely to interact with the robot, though many observe it with curiosity. The paper contributes to Human-Robot Interaction (HRI) theory and suggests further long-term and also quantitative studies to enhance understanding of trust and social-emotional reactions in real-world contexts.

The Retail Service Robot (RSR) is a humanoid robot that uses AI service automation to provide customized shopping assistance. Based upon Computers-Are-Social-Actors theory, this study investigates: (1) The way RSRs’ usefulness, social capability, and appearance facilitate Human-Robot Interaction (HRI); (2) whether anxiety toward robots inhibits the relations between RSRs’ facilitators and HRI, and (3) whether HRI affects anticipated service quality and ultimately the acceptance of RSRs. The study incorporates interviews, video clip stimuli, and empirical data collection (n = 1362) in fashion, technology, and food-service. The results reveal that RSRs’ facilitators such as usefulness, social capability, and appearance influence attitudes toward HRI positively, which in turn, predict anticipation of better service quality and greater acceptance of RSRs, and demonstrate the moderating role of anxiety toward robots between RSRs’ facilitators and attitudes toward HRI. The findings provide theoretical and practical implications for the adoption of RSRs and development of effective HRI.

A large literature describes the use of robots' physical bodies to support communication with people. Touch is a natural channel for physical interaction, yet it is not understood how principles of interpersonal touch might carry over to a robot. Exploring how interpersonal rules surrounding body accessibility and touch apply to a robot is critical toward understanding the extent to which people treat the act of touching body regions as a sign of closeness---even if the body belongs to a robot---and is important to the field of humanoid social robotics. Thirty-one students participated in an interactive anatomy lesson with a small, humanoid robot. Participants either touched or pointed to an anatomical region of the robot in each of 26 trials while their skin conductance response was measured. Touching less accessible regions of a robot's body (e.g., its buttocks and genitals) was more physiologically arousing than touching more accessible regions (e.g., its hands and feet). No differences in physiological arousal were found when just pointing to those same anatomical regions. A social robot elicited tactile responses in human physiology, a result that signals people treat touching body parts as an act of closeness in itself that does not require a human recipient. The power of touching a humanoid body with identifiable body parts should caution mechanical and interaction designers about the positive and negative effects of human-robot interaction.

A wearable sensor vest for social humanoid robots with GPGPU, IoT, and modular software architecture

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.

Source credibility is known as an important prerequisite to ensure effective communication (Pornpitakpan, 2004). Nowadays not only humans but also technological devices such as humanoid robots can communicate with people and can likewise be rated credible or not as reported by Fogg and Tseng (1999). While research related to the machine heuristic suggests that machines are rated more credible than humans (Sundar, 2008), an opposite effect in favor of humans’ information is supposed to occur when algorithmically produced information is wrong (Dietvorst, Simmons, and Massey, 2015). However, humanoid robots may be attributed more in line with humans because of their anthropomorphically embodied exterior compared to non-human-like technological devices. To examine these differences in credibility attributions a 3 (source-type) x 2 (information’s correctness) online experiment was conducted in which 338 participants were asked to either rate a human’s, humanoid robot’s, or non-human-like device’s credibility based on either correct or false communicated information. This between-subjects approach revealed that humans were rated more credible than social robots and smart speakers in terms of trustworthiness and goodwill. Additionally, results show that people’s attributions of theory of mind abilities were lower for robots and smart speakers on the one side and higher for humans on the other side and in part influence the attribution of credibility next to people’s reliance on technology, attributed anthropomorphism, and morality. Furthermore, no main or moderation effect of the information’s correctness was found. In sum, these insights offer hints for a human superiority effect and present relevant insights into the process of attributing credibility to humanoid robots.

From neuroscience, brain imaging and the psychology of memory, we are beginning to assemble an integrated theory of the brain subsystems and pathways that allow the compression, storage and reconstruction of memories for past events and their use in contextualizing the present and reasoning about the future—mental time travel (MTT). Using computational models, embedded in humanoid robots, we are seeking to test the sufficiency of this theoretical account and to evaluate the usefulness of brain-inspired memory systems for social robots. In this contribution, we describe the use of machine learning techniques—Gaussian process latent variable models—to build a multimodal memory system for the iCub humanoid robot and summarize results of the deployment of this system for human–robot interaction. We also outline the further steps required to create a more complete robotic implementation of human-like autobiographical memory and MTT. We propose that generative memory models, such as those that form the core of our robot memory system, can provide a solution to the symbol grounding problem in embodied artificial intelligence.This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

Social robots have shown considerable potential in supporting children’s learning. However, most child-robot interaction (CRI) research has been conducted in supervised laboratory settings, leaving questions about how children engage with robots autonomously in real-world learning environments. We developed a fully autonomous social robotic system and deployed it in summer camp classrooms without direct adult supervision. Our study spanned two months across seven summer camps (each lasting 3–5 days) and examined children’s interactions with a humanoid robot through game-based learning. A total of 100 children from two age groups (6–8 and 9–12 years) engaged with ten educational games tailored to their developmental stages, guided by a QTrobot. Our findings show that autonomous social robots can be integrated into classroom settings without direct supervision and are well accepted by children across age groups, while revealing age-specific differences in perception, engagement, and social behavior, alongside practical challenges related to time, distraction, and technical robustness.

Enhancing Humans Trust in Robots through Explanations

With the development of technology, humanoid robots gradually enter our life, not only for education, but helping people with housework and many other tasks, some of which are inevitable for human. But only few people know how to control and interact with a humanoid robot, which hinders the development of humanoid robot. So a human-robot interactive demonstration system is designed to help non-expert users to control the humanoid robot, Aldebaran humanoid robot Nao in this case. Users just need to use the most natural body gestures to interact with the robot. Microsoft Kinect is applied in this system to recognize different body gestures and generate visual Human-Robot interaction interface, then the controlling signals of different body gesture modules are sent to Nao through wifi, which can stimulate Nao to complete tasks. This kind of system aims to enrich the interactive way between human and robots and help non-expert users to control the robot freely, making Human-Robot interaction much easier.

Many researches about humanoid robot are performed for last decade. There exist outstanding results about hardware platforms and software algorithms and some results become commercial products. Humanoid robot engineering is synthetic study which includes mechanism design, sensor system, control algorithm and so on. But humanoid robot engineering has just symbol of technology, practical usage is very low compare with other robot shapes. Main object of this research is to develop humanoid robot which can be used in industrial or social environment. HUBO FX-1 is humanoid robot which is used for transportation system in social or industrial environment. It can carry luggage or a person according to shape of it's upper body.

Modern robotics seems to have taken root from the theories of Isaac Asimov, in 1941. One area of research that has become increasingly popular in recent decades is the study of artificial intelligence or A.I., which aims to use machines to solve problems that, according to current opinion, require intelligence. This is related to the study on “Social Robots”. Social Robots are created in order to interact with human beings; they have been designed and programmed to engage with people by leveraging a "human" aspect and various interaction channels, such as speech or non-verbal communication. They therefore readily solicit social responsiveness in people who often attribute human qualities to the robot. Social robots exploit the human propensity for anthropomorphism, and humans tend to trust them more and more. Several issues could arise due to this kind of trust and to the ability of “superintelligence” to "self-evolve", which could lead to the violation of the purposes for which it was designed by humans, becoming a risk to human security and privacy. This kind of threat concerns social engineering, a set of techniques used to convince users to perform a series of actions that allow cybercriminals to gain access to the victims' resources. The Human Factor is the weakest ring of the security chain, and the social engineers exploit Human-Robots Interaction to persuade an individual to provide private information.An important research area that has shown interesting results for the knowledge of the possibility of human interaction with robots is "cyberpsychology". This paper aims to provide insights into how the interaction with social robots could be exploited by humans not only in a positive way but also by using the same techniques of social engineering borrowed from "bad actors" or hackers, to achieve malevolent and harmful purposes for man himself. A series of experiments and interesting research results will be shown as examples. In particular, about the ability of robots to gather personal information and display emotions during the interaction with human beings. Is it possible for social robots to feel and show emotions, and human beings could empathize with them? A broad area of research, which goes by the name of "affective computing", aims to design machines that are able to recognize human emotions and consistently respond to them. The aim is to apply human-human interaction models to human-machine interaction. There is a fine line that separates the opinions of those who argue that, in the future, machines with artificial intelligence could be a valuable aid to humans and those who believe that they represent a huge risk that could endanger human protection systems and safety. It is necessary to examine in depth this new field of cybersecurity to analyze the best path to protect our future. Are social robots a real danger? Keywords: Human Factor, Cybersecurity, Cyberpsychology, Social Engineering Attacks, Human-Robot Interaction, Robotics, Malicious Artificial Intelligence, Affective Computing, Cyber Threats

Humanoid robots can achieve increasingly complex functions and adapt to more complex environments. To boost the development of humanoid robot technology, a team at Beijing Institute of Technology initiated the research on humanoid robots from 2000. Their research primarily focuses on stable walking, whole-body complex motion, human-robot interaction, and multimodal motion of humanoid robots. Thus far, the team has developed 6 generations of humanoid robots. The latest humanoid robot, BHR-6P, can achieve multi-mode motions (for example, walk, jump, fall protection, crawl and roll), which will significantly improve the ability of robot to adapt to the environment. This paper presented the historical evolution of BHR humanoid robots and outlined their functions and features.

Social Assistive Robotics is increasingly being used in care settings to provide psychosocial support and interventions for the elderly with cognitive impairments. Most of these social robots have provided timely stimuli to the elderly at home and in care centres, including keeping them active and boosting their mood. However, previous investigations have registered shortcomings in these robots, particularly in their ability to satisfy an essential human need: the need for companionship. Reports show that the elderly tend to lose interests in these social robots after the initial excitement as the novelty wears out and the monotonous familiarity becomes all too familiar. This paper presents our research facilitating conversations between a social humanoid robot, Nadine, and cognitively impaired elderly at a nursing home. We analysed the effectiveness of human–humanoid interactions between our robot and 14 elderly over 29 sessions. We used both objective tools (based on computer vision methods) and subjective tools (based on observational scales) to evaluate the recorded videos. Our findings showed that our subjects engaged positively with Nadine, suggesting that their interaction with the robot could improve their well-being by compensating for some of their emotional, cognitive, and psychosocial deficiencies. We detected emotions associated with cognitively impaired elderly during these interactions. This study could help understand the expectations of the elderly and the current limitations of Social Assistive Robots. Our research is aligned with all the ethical recommendations by the NTU Institutional Review Board.

Interaction between humanoid robots and humans is a complex process. Speech, gestures, and recognition of communication partners are important aspects in a well-defined interaction. To seem more natural, a humanoid robot should not be stationary. It should be able to be part of a crowd and wander around a specific area. Therefore, pathfinding is important to give a humanoid robot the ability to connect with people in more than one place. In addition, the recognition of communication partners is the backbone of social interaction. This chapter demonstrates how OpenCV, a well-known computer vision library, supports the robot Pepper in the recognition of communication partners and in addition, how this is the starting point to different types of small talk as the basis for a prototypical interaction process of humanoid robots and humans. Additionally, the navigation functions that allow the robot to move autonomously and enable a better human–robot interaction will be discussed.