Field Of Human-robot Interaction Research Articles

Human Perception of the Emotional Expressions of Humanoid Robot Body Movements: Evidence from Survey and Eye-Tracking Measurements.

The emotional expression of body movement, which is an aspect of emotional communication between humans, has not been considered enough in the field of human-robot interactions (HRIs). This paper explores human perceptions of the emotional expressions of humanoid robot body movements to study the emotional design of the bodily expressions of robots and the characteristics of the human perception of these emotional body movements. Six categories of emotional behaviors, including happiness, anger, sadness, surprise, fear, and disgust, were designed by imitating human emotional body movements, and they were implemented on a Yanshee robot. A total of 135 participants were recruited for questionnaires and eye-tracking measurements. Statistical methods, including K-means clustering, repeated analysis of variance (ANOVA), Friedman's ANOVA, and Spearman's correlation test, were used to analyze the data. According to the statistical results of emotional categories, intensities, and arousals perceived by humans, a guide to grading the designed robot's bodily expressions of emotion is created. By combining this guide with certain objective analyses, such as fixation and trajectory of eye movements, the characteristics of human perception, including the perceived differences between happiness and negative emotions and the trends of eye movements for different emotional categories, are described. This study not only illustrates subjective and objective evidence that humans can perceive robot bodily expressions of emotions through only vision but also provides helpful guidance for designing appropriate emotional bodily expressions in HRIs.

Human-Robot Interaction (HRI) using Machine Learning (ML): a Survey and Taxonomy

Human-robot interaction (HRI) which has become the fundamental need of the hour is born out of the necessity for studying the relation between humans and robots. This cutting-edge discipline is a multidisciplinary field that draws from computer science, robotics along with human-computer interaction and psychology. It focuses mainly on designing and programming machines, best known as automated machines or robots, which are used by humans to perform specific tasks in a timely manner and with higher quality. The key problem in HRI is to realize, shape, tune, and modelling the humanrobot interaction in a flexible manner. For the sake of reflecting and shaping the interactions between humans and robots, HRI is based on the fusion of the two areas: the people's behaviour and attitudes towards using these robots, as well as the physical, technological, and interactive features of the robots. As the robot has tightly integrated from a set of sensors that collect the data from the environment and send them to the processor which in turn translates the collected data into information that can be used in the robot itself, machine learning (ML) is a well-known research area that focuses on the building of well-stocked knowledge systems by using supervised and unsupervised algorithms. From a conceptual standpoint, this research survey and taxonomy pursue to present an in-depth evaluation and review of the most current state-of-the-art papers that have already been published so far and encompass the use of ML algorithms in the HRI field. Thus, a total of 30 research papers devoted to HRI were examined and analysed to give the most ML algorithms implemented in the field of HRI. Evidently, this study shows that the Neural and Reinforcement learning machine algorithms that are used mostly in the recent studies that have an interest in HRI use a machine learning algorithm with a supervised technique in a physical application. There are many challenges facing HRI using ML algorithms, which reduce the use of other ML algorithms such as deep and SVM learning algorithm. Unfortunately, these challenges limit use in social and mobile applications.

Human–robot interaction: predicting research agenda by long short-term memory

The article addresses the identification and prediction of research topics in human–robot interaction (HRI), fundamental in Industry 4.0 (I4.0) and future Industry 5.0 (I5.0). In the absence of research agendas in the scientific literature, the study proposes a multilayered model to create a precise agenda to guide the scientific community in new developments in collaborative robotics and HRI technologies. The methodology is divided into four stages, which make up the three layers of the model. In the first two stages, scientific articles on HRI for the period 2020–2021 were collected and analyzed using data mining techniques together with VantagePoint and Gephi software to identify keywords and their relationships. These initial stages form layer 1 of the model, where the main scientific themes are recognized. In the third stage, article titles and abstracts are cleaned and processed using natural language processing (NLP) techniques, generating word embeddings models that highlight relevant HRI-related terms, forming layer 2. The fourth and final stage uses Recurrent Neural Networks (RNN) with long short-term memory (LSTM) architecture to predict future topics, consolidating the previously identified terms and forming layer 3 of the model. The results show that in layer 1 HRI has intensive application in various sectors through advanced computational algorithms, with trust as a key feature. In layer 2, terms such as vision, sensors, communication, collaboration and anthropomorphic aspects are fundamental, while layer 3 anticipates future topics such as design, performance, method and controllers, essential to improve robot interaction. The study concludes that the methodology is effective in defining a robust and relevant research agenda. By identifying future trends and needs, this work fills a gap in the scientific literature, providing a valuable tool for the research community in the field of HRI.

Body Movement Mirroring and Synchrony in Human–Robot Interaction

This review article provides an overview of papers that have studied body movement mirroring and synchrony within the field of human-robot interaction. The papers included in this review cover system studies, which focus on evaluating the technical aspects of mirroring and synchrony robotic systems, and user studies, which focus on measuring particular interaction outcomes or attitudes towards robots expressing mirroring and synchrony behaviors. We review the papers in terms of the employed robotic platforms and the focus on parts of the body, the techniques used to sense and react to human motion, the evaluation methods, the intended applications of the human-robot interaction systems and the scenarios utilized in user studies. Finally, challenges and possible future directions are considered and discussed.

Enhancing emotional expression in cat-like robots: strategies for utilizing tail movements with human-like gazes.

In recent years, there has been a significant growth in research on emotion expression in the field of human-robot interaction. In the process of human-robot interaction, the effect of the robot's emotional expression determines the user's experience and acceptance. Gaze is widely accepted as an important media to express emotions in human-human interaction. But it has been found that users have difficulty in effectively recognizing emotions such as happiness and anger expressed by animaloid robots that use eye contact individually. In addition, in real interaction, effective nonverbal expression includes not only eye contact but also physical expression. However, current animaloid social robots consider human-like eyes as the main emotion expression pathway, which results in a dysfunctional robot appearance and behavioral approach, affecting the quality of emotional expression. Based on retaining the effectiveness of eyes for emotional communication, we added a mechanical tail as a physical expression to enhance the robot's emotional expression in concert with the eyes. The results show that the collaboration between the mechanical tail and the bionic eye enhances emotional expression in all four emotions. Further more, we found that the mechanical tail can enhance the expression of specific emotions with different parameters. The above study is conducive to enhancing the robot's emotional expression ability in human-robot interaction and improving the user's interaction experience.

What Can Robots Do For You?

You have probably seen movies in which robots do things like drive cars, deliver groceries, and fight space battles. But have you ever thought about what a robot could do for you? In the field of human-robot interaction, scientists study how robots can help people and what people think of robots. In this article, we meet three children in a classroom of the future and find out what robots do for them and how those robots know what to do. At school, Mia works with a robot friend who helps her learn a foreign language. Noah has special needs, and a robot in a hospital helps him learn about feelings and how to get along with other people. Last, Ari has a robot at home that helps her read, chats with her siblings, and helps her parents.

AI and human-robot interaction: A review of recent advances and challenges

The integration of artificial intelligence (AI) into human-robot interaction (HRI) has witnessed significant advancements in recent years, revolutionizing the way humans and robots collaborate and coexist. This review provides a comprehensive overview of the latest breakthroughs in AI-driven HRI and identifies the challenges that lie ahead. Recent years have seen a surge in AI-driven capabilities that enhance human-robot interaction. Machine learning algorithms enable robots to adapt to user preferences and behaviors, creating personalized and intuitive interactions. Natural language processing (NLP) facilitates seamless communication between humans and robots, enabling voice commands and context-aware responses. Computer vision advancements empower robots with enhanced perception, enabling them to recognize and interpret human gestures, emotions, and facial expressions. Reinforcement learning has played a pivotal role in enabling robots to learn from human feedback and optimize their actions in real-time. Socially assistive robots leverage AI to provide emotional support and companionship, particularly in healthcare and elderly care settings. Despite these advancements, challenges persist in the field of AI-driven HRI. Ethical considerations, including privacy concerns and the responsible use of AI in influencing human behavior, demand careful attention. Ensuring the safety and security of AI-driven robotic systems remains paramount, requiring robust measures against malicious attacks and unintended consequences. Human-robot trust remains a critical challenge, necessitating transparent AI algorithms and effective communication strategies. Interdisciplinary collaboration between AI researchers, roboticists, psychologists, and ethicists is essential to address the complex socio-technical aspects of HRI. The fusion of AI and human-robot interaction holds immense potential to redefine various facets of our daily lives. This review highlights recent strides in AI-driven HRI, emphasizing the need for interdisciplinary efforts to address challenges and ensure the responsible development and deployment of AI-powered robotic systems. As researchers continue to innovate, the dynamic landscape of AI and human-robot interaction promises a future where seamless collaboration and coexistence between humans and robots become an integral part of our societal fabric.

Opportunities for Generative Artificial Intelligence to Accelerate Deployment of Human-Supervised Autonomous Robots

Autonomous robots have the potential to supplement human capabilities while reducing cognitive and physical burden. However, deploying such systems in natural settings is currently a time-consuming process that revolves around a human’s ability to research, design, test, and evaluate the robot – thereby introducing unnecessary bottlenecks and significant delays to technology adoption. The current work in the field of human-robot interaction (HRI) has historically focused on robot use even though humans play a critical role during autonomous system design and deployment. We argue that the scope of HRI must be expanded beyond that of the current views within the scientific community, to include all phases of system development, deployment, and use. Furthermore, to facilitate the pursuit of this new expanded scope, we present eight opportunities for technological advances in HRI and autonomy using Generative AI that, if realized, could have transformational impact on the fielding of human-supervised autonomous robots. Broadly speaking, our identified opportunities relate to interaction and trustworthiness, collaboration and cooperation, robot motion, robot perception, synthetic scenario generation, testing and evaluation, failure detection, and robot design.

Этические проблемы в области ассистивной робототехники для пожилых людей: философские vs эмпирические исследования

Assistive robotics can provide technological support to meet the growing demand for health and social care as people's physical and cognitive functions decline with age. Companion robots can help elderly people with tasks such as mobility, communication and self-care. But do we have the moral right to delegate the duties of medical personnel to robots? Can robots provide the proper quality of medical care? Such reflections on the future of “machine” healthcare raise serious ethical questions in the field of human-robot interaction and determine not only priority developments in the field of social robotics, but also technological culture as a whole. Contemporary research on ethical issues in the field of assistive robotics for elderly people can be distinguished from two directions - traditional philosophical speculation and applied research with an emphasis on the participation of various stakeholders. The article discusses these two directions in detail, as well as criticism of philosophical and theoretical attitudes from the “empiricists”. In particular, they argue that along with ethical analysis, social expertise and philosophical reasoning, new forms of reflection, dialogue and experimentation are needed that will be much closer to innovative practices and contexts of use. The methodological basis of the article is the principles of Technology Assessment (TA), transdisciplinary research strategies such as the RRI approach, as well as Value-Sensitive Design (VSD). The article describes new options for value-based design, such as Integrative social robotics (ISR), Care-Centered Value-Sensitive Design (CCVSD). These approaches are quite promising for solving philosophical and ethical problems in the field of social robotics for caring for the elderly. But more direct and indirect stakeholders need to be involved in the design and development of medical robots. Based on the Stakeholder Tokens method, it is shown how some ethical problems can be solved in a situation of value conflict between autonomy, human dignity and security.

Enhancing Human-Robot Collaboration in Industry 4.0 with AI-driven HRI

Human-robot interaction (HRI) is an important consideration in mechatronic design to ensure safe and intuitive operation of robotic systems. With advancements in artificial intelligence (AI), new opportunities have emerged to enhance HRI through learned models that can adapt to human behavior and preferences. This paper provides a comprehensive review of techniques to integrate AI into HRI for mechatronic systems. An overview is first provided of challenges and objectives in integrating intelligence into robotics for effective HRI. Modern approaches utilizing neural networks, reinforcement learning, and graph neural networks are then discussed for robotic perception, decision-making, motion control, and interaction adaptation. Additionally, hybrid approaches combining rule-based methods with learned models are highlighted. Guidelines are provided for collecting human interaction data, evaluating integrated system performance, and considering adjustability, explainability, and safety. Multiple tables summarize key studies on AI-enhanced user interfaces, interactive task learning, socially aware navigation, bio-inspired sensorimotor control, and personalized robots. Finally, open issues and future outlook are discussed. This paper aims to support mechatronic designers through an structured analysis of the emerging field of intelligent HRI with insights into current best practices for integration.

ENHANCING HUMAN-ROBOT INTERACTION THROUGH GROUP EMOTION RECOGNITION

Abstract - This article explores within the field of Human-Robot Interaction (HRI), focusing on the complicated relationship between emotions, decision-making, and robot behaviors. Emotions are essential to effective communication and interaction, requiring the development of emotion recognition systems in robots. The article explores both individual and group emotion recognition, including microexpressions and macroexpressions. Group emotion dynamics, encompassing phenomena like emotional contagion, convergence, and social influence, are separated to understand how emotions combine within collective settings. A concept, Group Emotion Recognition (GER), is introduced, providing a framework for recognizing emotions within groups. GER involves proximity metrics, emotion classification, and entropy-based analysis to quantify emotion diversity. The article also outlines how GER can enhance user engagement, personalize interactions, improve group dynamics, and foster social acceptance in various human-robot interaction scenarios. Decision-making based on GER, driven by positive or negative emotion labels, is discussed, highlighting the adaptability and sensitivity required for effective human-robot interactions. Ethical considerations regarding the use of emotion recognition technology are addressed throughout the article, emphasizing responsible implementation. Overall, this work lays a solid foundation for advancing the field of HRI by integrating emotion recognition and decision-making to create emotionally intelligent and socially aware robots.

Emotion Recognition in Psychology of Human-robot Interaction

The field of Human-Robot Interaction (HRI) has garnered significant attention in recent years, with researchers and practitioners seeking to understand the psychological aspects underlying the interactions between humans and robots. One crucial area of focus within HRI is the psychology of emotion recognition, which plays a fundamental role in shaping the dynamics of human-robot interaction. This paper provides an overview of the background of psychology in the context of human-robot interaction, emphasizing the significance of understanding human emotions in this domain. The concept of emotion recognition, a key component of human psychology, is explored in detail, highlighting its relevance in the context of human-robot interaction. Emotion recognition allows robots to perceive and interpret human emotions, enabling them to respond appropriately and enhance the quality of interaction. The role of emotion recognition in HRI is examined from a psychological standpoint, shedding light on its implications for the design and development of effective human-robot interfaces. Furthermore, this paper delves into the application of machine learning techniques for emotion recognition in the context of human-robot interaction. Machine learning algorithms have shown promise in enabling robots to recognize and respond to human emotions, thereby contributing to more natural and intuitive interactions. The utilization of machine learning in emotion recognition reflects the intersection of psychology and technological advancements in the field of HRI. Finally, the challenges associated with emotion recognition in HRI are discussed, encompassing issues such as cross-cultural variations in emotional expression, individual differences, and the ethical implications of emotion detection. Addressing these challenges is pivotal in advancing the understanding and implementation of emotion recognition in human-robot interaction, underscoring the interdisciplinary nature of this endeavor. In conclusion, this paper underscores the critical role of emotion recognition in the psychology of human-robot interaction, emphasizing its potential to revolutionize the way humans and robots engage with each other. By integrating insights from psychology, machine learning, and technology, advancements in emotion recognition have the potential to pave the way for more empathetic and responsive human-robot interactions, offering new avenues for research and practical applications in this burgeoning field.

A novel design of series elastic actuator using tensile springs array

Series elastic actuator (SEA) has already been used in robotics, especially the human-robot interaction field, to acquire compliance and force sensing ability. However, achieving a SEA design with linear and consistent elastic properties while having low friction, minor hysteresis, and good compliance is still challenging. In addition, the inherent conflict between good compliance and high bandwidth must be appropriately handled in the design. This paper proposes a newly designed compliant actuator with a novel elastic component using a tensile springs array. The unique geometry of the spring array enables SEA's consistent rotary stiffness within the whole working range while having trivial friction and hysteresis. We adopt PID and sliding mode control to evaluate the device's performance. It can be verified that the SEA has a constant low rotary stiffness of 1.13 Nm/deg bringing good compliance while having a torque tracking bandwidth of 7 Hz. These desirable properties enable our device to provide compliant, smooth, and predictable output to meet human-robot interaction requirements.

A tool to evaluate industrial cobot safety readiness from a system-wide perspective: An empirical validation

The emergence of collaborative robots (cobots) has transformed the interaction between humans and robots in industrial workspaces. While cobots offer significant advantages in productivity and flexibility, their unique interaction model with humans introduces additional safety considerations. Moreover, the adoption of cobots remains limited, partly due to a lack of awareness and knowledge about their safety.To address these challenges, the Cobot Safety Readiness Assessment Tool (CSRAT) evaluates cobot safety readiness, the combination of awareness and knowledge, from a system-wide perspective. The CSRAT is grounded in maturity grid models evaluating the readiness of technological and other safety risk factors such as operator trust, privacy, and training. It allows practitioners to self-assess their cobot safety readiness and perceived importance of risk factors. Additionally, the tool aims to facilitate alignment among internal stakeholders on cobot safety. The CSRAT prototype underwent iterative development and validation by experts from academia and industry. Empirical validation results highlight the tool's usefulness, effectiveness, and usability. It successfully enhances users' awareness and knowledge of safety risk factors and serves as a conversation starter among internal stakeholders.For industry practitioners, the CSRAT offers practical relevance in enhancing safety readiness, guiding decision-making, and addressing overlooked risk factors. Academically, this research contributes to the fields of human-robot interaction, safety evaluation, usability research, and cobot adoption.In conclusion, this research provides a comprehensive approach to evaluating cobot safety readiness and promoting informed decision-making for safer human-robot collaborations. While acknowledging CSRAT's value and limitations, the paper also points towards potential future research directions.

A high-sensitivity wearable flexible strain sensor based on three-dimensional twist-like network structure

With the rapid development of the flexible electronics field, flexible pressure sensors are widely used inwearable devices, medical monitoring and other fields. However, it is still a huge challenge to design a sensor with highsensitivity, low cost and a simple manufacturing process. In this work, we report a flexible strain sensor based on discarded mask straps (MS) with a unique three-dimensional (3D) twist-like network structure. Multi-walled carbon nanotubes (MWCNTs)/carbon nanoparticles (CN)/MS composites were prepared by a simple dip-drying method and encapsulated using a medical Polyurethane (PU) film. It is founded that the fabricated MWCNTs/CN/MS/PU flexible strain sensor (MCMP-FSS) exhibits a high gauge factor (GF) of 13,928 and advanced performance with a large sensing range of 0%–72%. Moreover, good water-repellent harmony and various types of stimuli over 1000 cycles were obtained for the MCMP-FSS. Benefiting from the unique 3D twist-like network structure and the MWCNTs/CN synergistic conductive network, MCMP-FSS also has broad application prospects in the fields of human motion detection, encrypted communication, and natural human-robot interaction.

Danger-zone and maximum entropy deep inverse reinforcement learning for human-robot navigation

As a result of the development of artificial intelligence in recent years, scientists have gone further in the field of human-robot interaction (HRI), and one of the remaining problems is how to achieve a safe and human comfort-satisfying navigation when it comes to crowd-robot interaction (CRI). This article explores two existing deep learning reinforcement (DRL) methods, Danger-zone (DZ) and Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL). The former method proposes a Danger-Zone to predict the trajectory of pedestrians, using the DRL network to achieve obstacle avoidance and combining advanced algorithms with it to summarize the most effective combination. The latter method collects expert demonstrations in an uncontrolled environment. It uses DNN networks to predict human behavior and compares the results with the actual trajectories to demonstrate their validity. This article summarizes and evaluates these two methods. Moreover, this article also gives various outlooks on the direction of human-computer interaction.

Can the robot "see" what I see? Robot gaze drives attention depending on mental state attribution.

Mentalizing, where humans infer the mental states of others, facilitates understanding and interaction in social situations. Humans also tend to adopt mentalizing strategies when interacting with robotic agents. There is an ongoing debate about how inferred mental states affect gaze following, a key component of joint attention. Although the gaze from a robot induces gaze following, the impact of mental state attribution on robotic gaze following remains unclear. To address this question, we asked forty-nine young adults to perform a gaze cueing task during which mental state attribution was manipulated as follows. Participants sat facing a robot that turned its head to the screen at its left or right. Their task was to respond to targets that appeared either at the screen the robot gazed at or at the other screen. At the baseline, the robot was positioned so that participants would perceive it as being able to see the screens. We expected faster response times to targets at the screen the robot gazed at than targets at the non-gazed screen (i.e., gaze cueing effect). In the experimental condition, the robot's line of sight was occluded by a physical barrier such that participants would perceive it as unable to see the screens. Our results revealed gaze cueing effects in both conditions although the effect was reduced in the occluded condition compared to the baseline. These results add to the expanding fields of social cognition and human-robot interaction by suggesting that mentalizing has an impact on robotic gaze following.

Pocketable-Bones: Self-Augment Mobile Robot Mediating our Sociality

Discussions on human-robot interactions abound in the fields of human-agent interaction and human-robot interaction. Although studies on communication through conversations and physical actions have been conducted, many of these studies focus on robot gaze while neglecting human gaze. In this study, we developed Pocketable-Bones, a mobile robot that can move with the human gaze in mind. This robot can fit in a breast pocket and follows and turns in the same direction a person faces. Notably, we have been investigating the realization of joint gazing in which two persons share interests and concerns through gazing. This study’s experiment results showed that Pocketable-Bones’ gazing behaviors satisfied the components of self-determination theory in well-being, notably autonomy, competence, and relationship.

A membership-function-based broad learning system for human-robot interaction force estimation under drawing task.

Estimating interaction force is of great significance in the field of human-robot interaction (HRI) thanks to its guarantee of interaction safety. To this end, this paper proposes a novel estimation method by leveraging broad learning system (BLS) and human surface electromyography (sEMG) signals. Since the previous sEMG may also contain valuable information of human muscle force, it would cause the estimation to be incomplete and abate the estimation accuracy in the case of neglecting the previous sEMG. To remedy this thorn, a new linear membership function is first developed to calculate contributions of sEMG at different sampling times in the proposed method. Subsequently, the contribution values calculated by the membership function are integrated with features of sEMG to be considered as the input layer of BLS. For extensive studies, five different features extracted from sEMG signals and their combination are explored to estimate the interaction force by the proposed method. Lastly, the performance of the proposed method is compared with those of three well-known methods through experimental test regarding the drawing task. The experimental results confirm that combining the time domain (TD) with frequency domain (FD) features of sEMG can enhance the estimation quality. Moreover, the proposed method outperforms its contenders with respect to estimation accuracy.

AAAI 2023 Spring Symposium on HRI in Academia and Industry: Bridging the Gap

AbstractOn March 27–29, 2023, the AAAI symposium on “HRI in Academia and Industry: Bridging the Gap” was held in a hybrid format, with both in‐person and remote participants, gathering Human‐Robot Interaction (HRI) researchers and practitioners from academia, industry, and national research laboratories to find common ground, understand the different constraints at play, and determine how to work together. The use of robots that operate in spaces in which humans are physically co‐present is growing at a dramatic rate. We are seeing more and more robots in our warehouses, on our streets, and even in our homes. All of these robots will interact with humans in some way, whether intentionally or unintentionally. To be successful, their interactions with humans will have to be carefully designed. For more than a decade, the field of HRI has been growing at the intersection of robotics, Artificial Intelligence (AI) , human‐computer interaction (HCI), psychology, and other fields; however, until quite recently, it has been a largely academic area, with university researchers proposing, implementing, and reporting on experiments at a limited scale. With the current increase of commercially‐available robots, HRI is starting to make its way into the robotics industry in a meaningful way. This symposium brought together HRI researchers and practitioners from academia, industry, and national research laboratories to find common ground, understand the different constraints at play, and determine how to effectively work together.