Human-robot interaction - what it means for human resource management

From smart factories to service applications, human-robot interaction is crucial to the development of collaborative settings in which humans and robots operate side by side. Since robots in shared spaces must take into consideration variables such as human motion unpredictability and potential workplace risks, effective risk management is essential. When building these systems, striking a balance between factors such as safety, ergonomics, and operational flexibility becomes crucial. Misalignment between human purpose and robot behaviors might result in accidents or increased injury risks, especially in environments with high physical demands. One of the key challenges in human-robot interaction is handling uncertainty. The main aim of the current study is to introduce a conceptual framework for safety/risk analysis, including a hierarchy tree of the risk criteria and risks. Both human- and robot-related factors are considered. The multi-criteria decision-making procedure developed for autonomous vehicle systems is adapted for risk analysis in human-robot interaction. As a final result, the prioritized risk criteria and risks are identified. These results lay the foundation for reducing risks in the future.

This article provides a comprehensive and comparative overview of the main types of robots, including social, educational, entertainment, industrial, collaborative, mobile, service, and humanoid robots, focusing on their technical, functional, and social characteristics. A key objective is to explore how anthropomorphic design, through human-like appearance, voice, and behavior, affects robot classification and shapes humanrobot interaction in different domains, including healthcare, education, retail, and entertainment. The study utilizes a structured literature review methodology that integrates recent peer-reviewed research in engineering, human-computer interaction, and the behavioral sciences. Through a comparative analysis, the paper identifies overarching topics such as the merging of technology capabilities, the increasing fluidity of robot classifications, and the ethical implications of emotional engagement with robots. Factors related to user trust, perceived agency, and consumer acceptance such as emotional intelligence, contextual adaptability, design aesthetics, and interaction intensity are examined. The results show that the increasing anthropomorphism of robots can significantly enhance user engagement, emotional attachment, and acceptance. However, it also raises important ethical challenges. These include the risks of emotional manipulation, stereotype reinforcement, and psychological dependence. The paper highlights that successful robot integration depends not only on technical efficiency, but also on ethical considerations, context-aware design, and the alignment of robot behavior with human social expectations. This paper provides original value by connecting technical classifications with socio-psychological dimensions of human-robot interaction, offering a conceptual framework for understanding how design elements and application context interact to influence societal acceptance.

Enhancing Humans Trust in Robots through Explanations

Towards establishing future-oriented relations between humans and robots, not only verbal, but also nonverbal robot functions have been studied. However, it is not clear how these different functions should be combined and implemented in robots in order to achieve ‘natural’ human-robot communication or interaction. In this paper, we focus on the timing for the coordination between utterance as a verbal function and body motion as a nonverbal function. To begin with, we investigated the temporal order and correlation between utterance and gesture in an interpersonal (human-human) interaction scenario as a preliminary experiment (Experiment 1). Then, we controlled the robot's response timing based on findings of the first experiment and evaluated the effectiveness of the robot's timing control in human-robot interaction (Experiment 2). It was shown that elderly participants significantly preferred the controlled robot's response. In addition, the timing of utterance and gestures affected the impression of the robot and interpretation of the verbal message. These results are important findings for designing socially interactive robots for the aging society.

The future of AI-powered robotics promises a major transformation across various industries, offering remarkable potential to automate complex processes, boost human capabilities, and redefine numerous sectors. By utilizing machine learning, computer vision, and natural language processing, AI-driven robots can interact with their surroundings, make informed decisions, and learn from their experiences, all without direct human oversight. This fusion of robotics and AI enables machines to tackle delicate tasks such as performing surgeries in healthcare, as well as advancing manufacturing and logistics operations. In the near future, AI-powered robots are expected to become increasingly autonomous, adaptable, and proficient in managing tasks within ever-changing environments. Breakthroughs in machine perception will allow robots to better comprehend and respond to the world around them, enhancing safety and effectiveness. Furthermore, advancements in human-robot collaboration may lead to robots working alongside humans in sectors like education, hospitality, and services, improving productivity and user interactions. However, the rise of AI-powered robotics also raises important ethical, legal, and social concerns, such as job loss and privacy issues. To ensure these technologies benefit society, careful integration will be essential. Ultimately, AI-powered robots are set to play a crucial role in shaping the future, transforming how we live and work. Research significance: The future of AI-driven robotics has the potential to revolutionize various industries, including healthcare, manufacturing, logistics, and agriculture. Research in this area concentrates on improving robot autonomy, decision-making, and collaboration with humans through advanced AI technologies. As robots become smarter, they will be able to carry out intricate tasks with greater accuracy, flexibility, and efficiency, boosting productivity and safety. This research also tackles issues such as ethical concerns, reliability, and human-robot emotional interaction. Progress in AI robotics promises innovative, practical, and sustainable solutions, transforming industries and enhancing overall quality of life. Methodology: The approach to studying the future of AI-driven robotics focuses on exploring how artificial intelligence integrates with robotic systems. This involves examining progress in machine learning, computer vision, and natural language processing, which allow robots to complete intricate tasks independently. Researchers investigate technological innovations such as enhanced sensors, edge computing, and human-robot interactions. They also address ethical, societal, and economic implications, such as the effects of automation, labor markets, and safety. To forecast trends, challenges, and opportunities in AI robotics, experts use case studies, simulations, and interviews, offering a well-rounded view of its future potential Alternative: AI Algorithm Performance, Energy Efficiency, Human-Robot Interaction, Hardware Advancements, Regulation and Ethics Evaluation preference: AI Algorithm Performance, Energy Efficiency, Human-Robot Interaction, Hardware Advancements, Regulation and Ethics Results: Hardware advancements are rising to the top, while regulation and ethics are being pushed to the bottom

The integration of Natural Language Processing (NLP) in Human-Robot Interaction (HRI) represents a significant advancement towards achieving more natural and effective communication between humans and robots. This research explores the application of state-of-the-art NLP techniques to enhance HRI, focusing on improving robots' abilities to understand and generate human language. Key components of our approach include advanced speech recognition, natural language understanding (NLU), dialogue management, and natural language generation (NLG). We designed and implemented an HRI system that leverages models such as BERT for language understanding and GPT-3 for generating contextually appropriate responses. Our methodology involves integrating these NLP models with a robotics platform, ensuring real-time interaction capabilities while maintaining a high level of accuracy and context awareness. The system was evaluated through a series of user studies, measuring performance metrics such as accuracy, latency, and user satisfaction. Results indicate that our NLP-enhanced HRI system significantly improves the quality of interactions, demonstrating superior understanding and responsiveness compared to traditional systems. This paper discusses the implementation challenges, including computational constraints and ambiguity resolution, and provides insights into user feedback and system performance. Future work will focus on enhancing context management, exploring multimodal interaction, and addressing ethical considerations in deploying advanced HRI systems. Our findings underscore the potential of NLP to transform human-robot communication, paving the way for more intuitive and effective robotic assistants in various domains. Keywords: Human-Robot Interaction (HRI), Natural Language Processing (NLP), Conversational AI, Speech Recognition, Natural Language Understanding (NLU), Natural Language Generation (NLG), Multimodal Interaction, Dialogue Systems, Context Awareness, Emotion Recognition, Machine Learning in HRI, Personalized Interaction, User Experience (UX) in HRI, Human-Centered Design, Collaborative Robots (Cobots)

Human-robot communication is an important subject for housekeeping, elderly care and entertainment robots. To make a natural communication entrainment between human and robot, emotion plays a vital role. From this view point we have developed a KANSEI communication system based on emotional synchronization. The robotic emotion was entrained to human emotion by using a vector field of dynamics, and then the robot made a facial expression to express the robot emotion. In this paper we investigate the effect of the emotional synchronization in human-robot KANSEI communications. We conducted experiments to evaluate the effects of the proposed system based on emotional synchronization. In the experiments of human-robot interaction using the emotional synchronization, we found that human feeling became comfortable when the robot made the synchronized facial expression to human emotion. Then it was confirmed that emotional synchronization in human-robot interaction could be effective to keep a comfortable state.

Actually, the research on human interactive robot (HIR) has been a topic of both science fiction and academic speculation for a long time. The origin of HIR as a discrete issue was stated by 20th century author Isaac Asimov in 1941, in his novel “I, Robot”. He stated the Three Laws of Robotics1 as, “ a. A robot may not injure a human being or, through inaction, allow a human being to come to harm. b. A robot must obey any orders given to it by human beings, except where such orders would conflict with the First Law. c. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. ” The three laws of robotics determine the idea of safe interaction which constitutes the basic rules of HIR. With the advances of artificial intelligence (AI), the HIR could eventually have more proactive behaviours, planning their motion in complex unknown environments. Nowadays, HIR are artificial agents with capacities of perception and action in the human’s environment. Their use has been tended to be found in the most technologically advanced societies in critical domains as search and rescue, military battle, law enforcement, entertainment, hospital care, etc. These domains of applications imply a closer interaction with human. The concept of closeness is to be taken in its full meaning, HIR and humans not only share the workspace but also share goals in terms of task achievement. The HIR has to adapt itself to human’s way of expressing desires and fulfill its task. Taking lifting up human in elder care for example, the human interactive robot RI-MAN, designed by the RIKEN Bio-Mimetic Control Research Center, communicates with human by listening and speaking, which makes it understand the human will (Onish, Luo et al. 2007). To fulfil the task, it also estimates the attitude of human body in real-time by tactile sense (Mukai, Onishi et al. 2008). This example contains two aspects of HIR, one is to understand the human mind and the other is to accomplish the manipulation. The former is based on AI techniques, like language comprehension, and the latter is relied on force control. On the other hand, human’s environments are much more complex. Thus, the HIR needs perceiving and understanding capacities to build dynamic models of its surroundings. It

How Much Do Robots Understand Rudeness? Challenges in Human-Robot Interaction

In a world where conversational Artificial Intelligence (AI) is playing an increasingly large role in our daily lives, it is important to understand how conversations between humans and AI or robots can become more personal, and stay useful over a prolonged period of time. The way two humans talk about other people is shaped by their degree of shared knowledge, or common ground. These referring expressions tend to become shorter and more personal over time, as codified, shared ways of making reference called conventions start to form. This process could make conversations with AI such as robots more personal, but there has been little research on this topic. In this dissertation, I examine how common ground plays in a role in making reference in this Human-Robot Interaction (HRI), addressing the research gap that exists in this space, and providing insights into both human-human and human-robot use of referring expressions in longitudinal interactions. My investigation has three main goals: examining how useful data can be obtained to study referring expressions in HRI; examining how referring expressions in HRI are influenced by the common ground, conventions, and contextual factors such as ambiguity; and examining the role of the robot in shaping, using and understanding referring expressions and conventions. I carry out this investigation through a number of experiments using data sets, field experiments with a physical robot, and online experiments with a chatbot. My approach draws from fields in linguistics related to (human-human) common ground and pragmatics, as well as fields in Natural Language Processing related to Natural Language Understanding and Referring Expression Generation. An important element in my research is the introduction of an inner circle of familiar entities, for which common ground is built up, and an outer circle of unfamiliar entities. By comparing the use of referring expressions for the inner and outer circle, I create a contrastive analysis which allows me to draw conclusions about the impact of common ground on referring expressions. This contrast is implemented in an experimental framework called SPOTTER, a game in which referring expressions to the inner and outer circle are elicited. I use this framework to analyse the use of referring expressions and the role of the robot in shaping them. One of the main findings of this dissertation is that, although present, the role of common ground is less pronounced in human-robot reference-making compared to human-human interactions. However, I do find a large influence of the contextual setting within each round of the SPOTTER game, as shaped by the outer circle. This influence is shown by longer, more detailed referring expressions when ambiguity is high, as well as through longer referring expressions for the outer circle compared to the inner circle. The findings also suggest that the robot's use of referring expressions has a large impact on which referring expressions humans use. However, though both humans and robots form conventions, they rarely truly converge on a shared convention. Naturally, the findings are influenced by design choices for the experimental framework and the conversational model, as well as by the complicated nature of field studies in HRI. The dissertation lays the groundwork for further investigations of reference-making and common ground in HRI, providing experimental tools and models.

We are excited to welcome you to the Twelfth Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI 2017). The HRI Conference is a highly selective, single track, international meeting showcasing the best research in human-robot interaction, with roots in and broad participation from various communities of scholars, including but not limited to robotics, human-computer interaction, artificial intelligence, engineering, social and behavioral sciences, and design. The theme of this year's conference is "Smart Interaction," following our host city Vienna's ongoing "Smart City" initiative. Designed with increasingly intelligent capabilities, robots must utilize such intelligence also in their interactions with people to improve the effectiveness, naturalness, and intuitiveness of these interactions as well as their moral and ethical consequences. Achieving intelligence in interaction requires the multidisciplinary convergence of technical, social, and design perspectives. To represent this multidisciplinarity, we solicited and reviewed papers under four submission themes: "Human-Robot Interaction User Studies," "Technical Advances in Human-Robot Interaction," "Human-Robot Interaction Design," and "Theory and Methods in Human-Robot Interaction." Each submission theme was overseen by a dedicated theme chair and reviewed by a dedicated group of program committee members, who worked together with the program chairs to define and apply review criteria appropriate to each of the four contribution types. The conference attracted 211 submissions from 38 unique countries from Asia-Pacific, Europe, the Middle East, and North America. Each full paper was aligned with a theme-appropriate subcommittee, and subsequently reviewed through a double-blind process, which was followed by a rebuttal phase, and shepherding where found appropriate by the program committee. As a result of the review process, the program committee selected 51 (24.2%) of the submissions for presentation as full papers at the conference. As the conference is jointly sponsored by IEEE and ACM, papers are archived in both the ACM Digital Library and the IEEE Xplore. Along with the full papers, the conference program and proceedings include the Late Breaking Reports, Videos, Demos, and an alt.HRI section. Out of 144 total submissions, 142 (98.6%) Late Breaking Reports (LBRs) were accepted and will be presented as posters at the conference. A new peer-review process ensured that authors of LBR submissions received detailed feedback on their work. Sixteen (80.0%) short videos were accepted for presentation during a dedicated video session from 20 submissions. The program also includes 12 demos of robot systems that participants will have an opportunity to interact with during the conference. We continue to include an alt.HRI session in this year's program, consisting of 4 papers (selected out of 19 submissions) that push the boundaries of thought and practice in the field. This year, we have reintroduced the Student Design Competition event to encourage student participation in the conference and enrich the program with novel ideas and insights developed by student teams. Finally, we have the pleasure of presenting three inspiring keynote speakers who will discuss topics relevant to HRI: Dr. Cynthia Breazeal from the MIT Media Lab and Jibo Inc. in the USA, Dr. Lucia Jacobs from the University of California, Berkeley in the USA, and Dr. Danica Kragic from the Royal Institute of Technology (KTH) in Sweden. HRI 2017 was made possible through the significant volunteer efforts of the organizing committee, program committee, reviewers, and the steering committee. We thank the keynote speakers, financial supporters, and international reviewers for their support and participation. The conference is sponsored by ACM SIGCHI, ACM SIGAI, IEEE Robotics and Automation Society, and is in cooperation with AAAI and HFES.

The development of contemporary robotics—ranging from social assistive robotics (SAR), carebots, and human–robot interaction (HRI) to brain–computer interfaces (BCI) that control robotic arms—is increasingly intertwined with the human body, emotions, and social life, thereby heightening the risk of ethical violations at both the design and research levels. At the same time, the literature on robot ethics and general AI ethics is growing very rapidly but tends to be fragmented and difficult to operationalize for the engineering community. This article aims to present a critical narrative review of how ethical violations emerge in contemporary robotics research and how AI/robot ethics frameworks can be used to interpret and address them. Methodologically, this review adopts a narrative synthesis approach to fifteen selected articles (2020–2025), which are mapped into three relational categories: robot ethics (R), general AI ethics (A), and the intersection of robot–AI ethics (R ∩ A). The analysis is carried out thematically and comparatively by linking the columns on rationale, aims–scope, and main ethical focus to answer three research questions (RQ1–RQ3) related to cases, frameworks, and an integrative model of ethical violations.The findings show that violations and high-risk practices manifest in the form of deception and manipulation of trust in carebots and HRI, problematic BCI testing designs, and structural harm to vulnerable groups through AI policies and infrastructures. On the other hand, frameworks such as disability justice, Sustainable AI, eco-relational ethics, critiques of principled ethics and benchmarking, as well as the concept of care robot literacy provide powerful lenses to classify and critique these cases beyond the micro level of interaction. In conclusion, ethical violations in robotics research must be understood as multi-layered phenomena that connect micro-level interaction, meso-level governance, and macro-level structures. The implication is that engineers and researchers need to integrate contextual, participatory, and structural ethical evaluation into the design, experimentation, and reporting cycles of robotics research.

The convergence of Robotics and artificial intelligence (AI) has transformed engineering by enabling the design of intelligent systems capable of learning, adapting, and performing complex tasks. These synergies are driving innovation across multiple engineering disciplines, including mechanical, materials, electrical, industrial, civil, and aerospace engineering. This review provides a comprehensive overview of the knowledge structure and emerging research directions of Robotics and AI in engineering, with the aim of identifying research trends, influential authors, leading institutions, and emerging thematic areas. Data were collected from the Web of Science and Scopus databases, covering the period from 2020 to 2025, and analyzed using bibliometric mapping techniques and performance indicators. The results reveal a sustained growth in research on autonomous systems, collaborative robots, and human–robot interaction within engineering contexts, with a strong emphasis on AI-driven optimization. Bibliometric analyses show that deep learning, reinforcement learning, and computer vision constitute the core enabling technologies structuring the field. In addition, the results highlight a high degree of international collaboration and a concentration of scientific output and impact in a limited number of leading countries, institutions, and journals.

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

The integration of Artificial Intelligence (AI) in Human-Robot Interaction (HRI) has significantly improved automation in the modern manufacturing environments. This paper proposes a new framework of using Retrieval-Augmented Generation (RAG) together with fine-tuned Transformer Neural Networks to improve robotic decision making and flexibility in group working conditions. Unlike the traditional rigid rule based robotic systems, this approach retrieves and uses domain specific information and responds dynamically in real time, thus increasing the performance of the tasks and the intimacy between people and robots. One of the significant findings of this research is the application of regret-based learning, which helps the robots learn from previous mistakes and reduce regret in order to improve the decisions in the future. A model is developed to represent the interaction between RAG based knowledge acquisition and Transformers for optimization along with regret based learning for predictable improvement. To validate the effectiveness of the proposed system, a numerical case study is carried out to compare the performance with the conventional robotic systems in a production environment. Furthermore, this research offers a clear approach for implementing such a system, which includes the system architecture and parameters for the AI-based human-robot manufacturing systems. This research solves some of the major issues including the problems of scalability, specific fine-tuning, multimodal learning, and the ethical issues in the integration of AI in robotics. The outcomes of the study are important in Industry 5.0, intelligent manufacturing and collaborative robotics, and the advancement of highly autonomous, flexible and intelligent production systems.