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The sanctity of human life mandates the replacement of individuals with robotic systems in the execution of hazardous tasks. Explosive Ordnance Disposal (EOD), a field fraught with mortal danger, stands at the forefront of this transition. In this study, we explore the potential of robotic telepresence as a safeguard for human operatives, drawing on the robust capabilities demonstrated by legged manipulators in diverse operational contexts. The challenge of autonomy in such precarious domains underscores the advantages of teleoperation—a harmonious blend of human intuition and robotic execution. Herein, we introduce a cost-effective telepresence and teleoperation system employing a legged manipulator, which combines a quadruped robot, an integrated manipulative arm, and RGB-D sensory capabilities. Our innovative approach tackles the intricate challenge of whole-body control for a quadrupedal manipulator. The core of our system is an IMU-based motion capture suit, enabling intuitive teleoperation, augmented by immersive visual telepresence via a VR headset. We have empirically validated our integrated system through rigorous real-world applications, focusing on loco-manipulation tasks that necessitate comprehensive robot control and enhanced visual telepresence for EOD operations. [ABSTRACT FROM AUTHOR]

Musical performance relies on nonverbal cues for conveying information among musicians. Human musicians use bodily gestures to communicate their interpretation and intentions to their collaborators, from mood and expression to anticipatory cues regarding structure and tempo. Robotic Musicians can use their physical bodies in a similar way when interacting with fellow musicians. The paper presents a new theoretical framework to classify musical gestures and a study evaluating the effect of robotic gestures on synchronization between human musicians and Shimon - a robotic marimba player developed at Georgia Tech. Shimon utilizes head and arm movements to signify musical information such as expected notes, tempo, and beat. The study, in which piano players were asked to play along with Shimon, assessed the effectiveness of these gestures on human-robot synchronization. Subjects were evaluated for their ability to synchronize with unknown tempo changes as communicated by Shimon's ancillary and social gestures. The results demonstrate the significant contribution of non-instrumental gestures to human-robot synchronization, highlighting the importance of non-music-making gestures for anticipation and coordination in human-robot musical collaboration. Subjects also indicated more positive feelings when interacting with the robot's ancillary and social gestures, indicating the role of these gestures in supporting engaging and enjoyable musical experiences. [ABSTRACT FROM AUTHOR]

This paper presents the development of a robotic system for the rehabilitation and quality of life improvement of children with cerebral palsy (CP). The system consists of four modules and is based on a virtual humanoid robot that is meant to motivate and encourage children in their rehabilitation programs. The efficiency of the developed system was tested on two children with CP. The effect of using the robot is an increase in the number of exercise repetitions, as well as the time spent on therapy, developing and strengthening the child's musculature. Additionally, the children are able to produce socially acceptable gestures in the context of non-verbal communication for socialization. The main advantages of this system are its flexibility and ease of use. Besides the proposed use in CP rehabilitation, this system can be used in the rehabilitation of people recovering from surgery or injuries. Use of the proposed system significantly decreases the work load of the therapist who would be conducting the repetitive motion, allowing the therapist to see an increased number of patients. In the future, the number of different movements the robot is able to perform will be increased by way of domain-specific modelling and language. [ABSTRACT FROM AUTHOR]

Dialogue systems must understand children's utterance intentions by considering their unique linguistic characteristics, such as syntactic incompleteness, pronunciation inaccuracies, and creative expressions, to enable natural conversational engagement in child–robot interactions. Even state-of-the-art large language models (LLMs) for language understanding and contextual awareness cannot comprehend children's intent as accurately as humans because of their distinctive features. An LLM-based dialogue system should acquire the manner by which humans understand children's speech to enhance its intention reasoning performance in verbal interactions with children. To this end, we propose a fine-tuning methodology that utilizes the LLM–human judgment discrepancy and interactive response data. The former data represent cases in which the LLM and human judgments of the contextual appropriateness of a child's answer to a robot's question diverge. The latter data involve robot responses suitable for children's utterance intentions, generated by the LLM. We developed a fine-tuned dialogue system using these datasets to achieve human-like interpretations of children's utterances and to respond adaptively. Our system was evaluated through human assessment using the Robotic Social Attributes Scale (RoSAS) and Sensibleness and Specificity Average (SSA) metrics. Consequently, it supports the effective interpretation of children's utterance intentions and enables natural verbal interactions, even in cases with syntactic incompleteness and mispronunciations. [ABSTRACT FROM AUTHOR]

As robots become integral to various sectors, improving human-robot collaboration is crucial, particularly in anticipating human actions to enhance safety and efficiency. Electroencephalographic (EEG) signals offer a promising solution, as they can detect brain activity preceding movement by over a second, enabling predictive capabilities in robots. This study explores how EEG can be used for action anticipation in human-robot interaction (HRI), leveraging its high temporal resolution and modern deep learning techniques. We evaluated multiple Deep Learning classification models on a motor imagery (MI) dataset, achieving up to 80.90% accuracy. These results were further validated in a pilot experiment, where actions were accurately predicted several hundred milliseconds before execution. This research demonstrates the potential of combining EEG with deep learning to enhance real-time collaborative tasks, paving the way for safer and more efficient human-robot interactions. [ABSTRACT FROM AUTHOR]

This paper presents a study on enhancing human–robot interaction (HRI) through multimodal emotional recognition within social robotics. Using the humanoid robot Pepper as a testbed, we integrate visual, auditory, and textual analysis to improve emotion recognition accuracy and contextual understanding. The proposed framework combines pretrained neural networks with fine-tuning techniques tailored to specific users, demonstrating that high accuracy in emotion recognition can be achieved by adapting the models to the individual emotional expressions of each user. This approach addresses the inherent variability in emotional expression across individuals, making it feasible to deploy personalized emotion recognition systems. Our experiments validate the effectiveness of this methodology, achieving high precision in multimodal emotion recognition through fine-tuning, while maintaining adaptability in real-world scenarios. These enhancements significantly improve Pepper's interactive and empathetic capabilities, allowing it to engage more naturally with users in assistive, educational, and healthcare settings. This study not only advances the field of HRI but also provides a reproducible framework for integrating multimodal emotion recognition into commercial humanoid robots, bridging the gap between research prototypes and practical applications. [ABSTRACT FROM AUTHOR]

The paper evaluates the benefits and challenges of employing humanoid robots in tourism and hospitality, examining their roles, decision-making processes, human-centric approaches, and oversight mechanisms. Data will be collected from a variety of sources, including academic journals, websites of the companies where the robots operate, case studies, and news articles. Specific attention will be given to concrete examples of humanoid robots deployed in the tourism and hospitality sector, such as Connie, Spencer, and Henn-na Hotel's robots. Robots highlight the potential to assume roles traditionally occupied by humans. The presence of humanoid robots also influences cultural practices and social interactions within the hospitality context. Humanoid robots also have the potential to improve equity and accessibility in the tourism and hospitality industry. The interaction between humans and humanoid robots can have psychological and emotional effects on both guests and employees. Finally, the usage of humanoid robots intersects with broader sustainability operational efficiency and customer satisfaction across various sectors within the tourism and hospitality industry. Introducing humanoid robots represents a challenge in innovation that holds promise for revolutionizing service delivery and guest experiences. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to personalizing robot behavior using Model Predictive Control (MPC). Social humanoid robots, equipped with advanced sensors and human-like capabilities, are increasingly integrated into human environments, necessitating adaptable and intuitive communication interfaces. Our approach enables the design of adaptive interfaces that support fluid, personalized human–robot interactions. In the proposed framework, a user model is applied to predict responses to potential robot actions. Initially, this model represents an average user; however, it is updated as the robot gathers new observations, leading to increasingly personalized decisions. Experiments assessed the performance of five machine-learning algorithms generating user models in a simulated environment, with the Light Gradient Boosting Machine (LGBM) achieving the best results, closely followed by Random Forest (RF). A comparison of the inference time showed that LGBM is more than four times faster than RF. Outlier-removal techniques showed a modest performance improvement over models without outlier removal. Additionally, robot adaptation was tested in the experiments, showing an increase in the average reward over time, although with a relatively high standard deviation. The results suggest that the proposed approach for robot behavior adaptation based on MPC works well, and the recommended algorithm for the user model is LGBM. [ABSTRACT FROM AUTHOR]

Despite sound being a promising modality of communication in robotics (possessing, for example, the ability to improve people's perceptions of robots and help localize robotic systems in space), its facilitator, speakers, are a seldom-explored topic of study in robotics literature. To address this gap, we conducted three explorations into physical speaker design that identified what current robot speakers lack and potential remedies, low-level design improvements, and post hoc hardware additions. Further, we detail and explore the application of speakers on three different robotic platforms (including one industrial robot used for construction), the last evaluation of which involved an empirical study (N = 21) that sought to better understand the implications associated with poor-quality speakers in a mock service robotics context. Our results suggest that greater internal cavity volume is a key strength in speaker design. We also observed greater effects of the presence (vs. absence) of a service robot voice compared to other factors. This work can inform the process of creating custom speakers for robots and augmenting current robotic systems with new speaker additions (whether commercial or custom, and across use contexts from construction to service). In particular, the work can help to guide roboticists who may be unfamiliar with nuanced audio engineering techniques and designers who seek to improve robotics platform standards with human interlocutors in mind. [ABSTRACT FROM AUTHOR]

The concept of joint attention holds significant importance in human interaction and is pivotal in establishing rapport, understanding, and effective communication. Within social robotics, enhancing user perception of the robot and promoting a sense of natural interaction with robots becomes a central element. In this sense, emulating human-centric qualities in social robots, such as joint attention, defined as the ability of two or more individuals to focus on a common event simultaneously, can increase their acceptability. This study analyses the impact on user perception of a responsive joint attention system integrated into a social robot within an interactive scenario. The experimental setup involves playing against the robot in the "Odds and Evens" game under two conditions: whether the joint attention system is active or inactive. Additionally, auditory and visual distractors are employed to simulate real-world distractions, aiming to test the system's ability to capture and follow user attention effectively. To assess the influence of the joint attention system, participants completed the Robotic Social Attributes Scale (RoSAS) after each interaction. The results showed a significant improvement in user perception of the robot's competence and warmth when the joint attention system was active. [ABSTRACT FROM AUTHOR]

Depth estimation plays a pivotal role in advancing human–robot interactions, especially in indoor environments where accurate 3D scene reconstruction is essential for tasks like navigation and object handling. Monocular depth estimation, which relies on a single RGB camera, offers a more affordable solution compared to traditional methods that use stereo cameras or LiDAR. However, despite recent progress, many monocular approaches struggle with accurately defining depth boundaries, leading to less precise reconstructions. In response to these challenges, this study introduces a novel depth estimation framework that leverages latent space features within a deep convolutional neural network to enhance the precision of monocular depth maps. The proposed model features dual encoder–decoder architecture, enabling both color-to-depth and depth-to-depth transformations. This structure allows for refined depth estimation through latent space encoding. To further improve the accuracy of depth boundaries and local features, a new loss function is introduced. This function combines latent loss with gradient loss, helping the model maintain the integrity of depth boundaries. The framework is thoroughly tested using the NYU Depth V2 dataset, where it sets a new benchmark, particularly excelling in complex indoor scenarios. The results clearly show that this approach effectively reduces depth ambiguities and blurring, making it a promising solution for applications in human–robot interaction and 3D scene reconstruction. [ABSTRACT FROM AUTHOR]

Exploring and validating factors that may influence the trustworthiness of social robots is of great significance to improving the effect of human-robot interaction. The purpose of this study was to explore the impact of several design factors on people's perceptions of the trustworthiness of social robots. The research variables were social cues (ie, watching-eyes configuration, control configuration, and neutral configuration), head shape (ie, round head and rectangular head), body proportion (ie, low body proportion and high body proportion), and participant's gender (ie, male and female). Twelve static synthetic social robots were created and presented to the participants as digital pictures. A total of 175 participants took part in the experiment through the online survey via the convenience sampling method. The generated results were as follows: (1) The robots with round heads appear to be more trustworthy than those with rectangular heads. (2) Robots with high body proportion are considered to be more trustworthy than robots with low body proportion. (3) Minimal social cues and body proportion of robots also have a significant interaction effect on improving the trustworthiness of robots. In the watching-eyes configuration, high body proportion is considered more trustworthy than low body proportion. On one hand, the results of this study can provide a good reference for the appearance form of social robots in human-robot interaction to serve human beings, and on the other hand, it also lays a foundation for further exploring the influence of social cues elicited by a series of social robot appearances and social behaviors regarding the trustworthiness of social robots. [ABSTRACT FROM AUTHOR]

User experience (UX) research in the Human-Technology Interaction (HTI) field makes use mainly of explicit (self-reported) measures to evaluate the UX of a technical product. Unfortunately, explicit measures present some limitations. For this reason, UX research could benefit from the inclusion of implicit measures (IMs) in the UX evaluation. In the social-psychological literature, the application of IMs evaluating implicit attitudes is a topic of active research; however, less knowledge is present in HTI. To this aim, we present a scoping review of the application of IMs evaluating implicit attitudes in the UX field. We used the Joanna Briggs Institute framework for scoping reviews. A database search was undertaken using IEEE Xplore, Web of Science, ACM digital library, and Science Direct databases. Three research questions have been identified. Of the 8943 identified records, only 12 articles matched the inclusion/exclusion criteria. Results suggest that a combined use of implicit and explicit measures provide a more comprehensive understanding of users' evaluation of UX yet revealing participant's divergences in attitudes toward technology especially in studies with robots or chatbots. More research is needed to disentangle this discrepancy and single out the specific factors that could influence this relationship. Taken together, IMs evaluating implicit attitudes should be strongly considered within a more integrated approach to evaluating UX. [ABSTRACT FROM AUTHOR]

The increasingly aging workforce is bringing particular attention to senior individuals in production sectors. While the interest in Virtual Reality (VR) applications for industrial robotics grows, the question of whether and how senior workers can withstand VR-based repetitive tasks arises. We here aimed to answer such questions by systematically assessing young and senior users' experiential, behavioural, and cognitive factors during simulated robotic teleoperations in VR. Two control systems for VR telerobotics, button- and action-based controls, were employed. Human performance, vigilance, and workload were measured through self-reports and a VR-integrated eye-tracker. Additionally, age-dependent differences in individual cultural and experiential factors were explored via self-report measures. Despite being slower and experiencing increased fatigue under specific conditions, as suggested by the eye-tracking measures, senior users demonstrated comparable precision in operating the robotic arm to their younger counterparts. Notably, both age groups reported similar levels of perceived fatigue. The paper provides an in-depth analysis of the advantages and challenges of adopting advanced telerobotics control systems across different age groups, consistently emphasising the human-centered dimension. [ABSTRACT FROM AUTHOR]

This research explores the role of simulation modelling in the development of human-centred, sustainable manufacturing processes in the context of Industry 5.0. We analyse collaborative workplaces where humans and collaborative robots (CR) work together, emphasizing the environmental, social, and governance (ESG) criteria. The research work focuses on how personalized CR parameters and optimized work environments contribute to improved productivity, well-being, and sustainability. Through simulations, the paper evaluates the operational efficiency of both manual assembly and human-robot collaborative (HRC) setups, providing insight into the economic, environmental, and social impacts of Industry 5.0 manufacturing systems. The results show significant improvements in sustainability, productivity, and worker well-being achieved through adaptive CR integration and ESG-driven engineering practices. [ABSTRACT FROM AUTHOR]

Affective feedback from social robots is a useful technique for communicating to people whether they are interacting "well" with the robot or not. However, some users, such as people with physical or cognitive difficulties, may not be able to interact in all the desired ways. In these cases, affective feedback from the robot could be excessively negative—an "unhappy" robot, leading to an unrewarding experience for the user. This article presents a motivation-based architecture for an autonomous multimodal social robot, that incorporates an affective feedback mechanism which generates an affective state by combining the internal needs of the robot and the social interaction quality. The balance between these two factors can dynamically change, allowing the robot to adapt its affective feedback to the user's interaction style and capabilities. We have implemented this architecture in a simulation and in a MiRo social robot, and report experiments examining the behavior of the system in interactions with different experimental user profiles. The results show that the adaptive mechanism allows the robot to change its affective feedback to give more positive encouragement to users than in non-adaptive cases. [ABSTRACT FROM AUTHOR]

Technological progress has seamlessly integrated digital assistants into our everyday lives, sparking an interest in social robots that communicate through both verbal and non-verbal means. The potential of these robots to influence human behaviour and attitudes holds significant implications for fields such as healthcare, marketing, and promoting sustainability. This study investigates how the design and behavioural aspects of social robots affect their ability to persuade, drawing on principles from human interaction to enhance the quality of human–robot interactions. Conducted in three stages, the experiments involved 73 participants, offering a comprehensive view of human responses to robotic persuasion. Surprisingly, the findings reveal that individuals tend to be more receptive to a single robot than to groups of robots. Nao was identified as more effective and capable of persuasion than Pepper. This study shows that successful persuasion by robots depends on social influence, the robot's appearance, and people's past experiences with technology. [ABSTRACT FROM AUTHOR]

Kazuo Ishiguro, the Nobel-winning writer, explores human-robot relations in his 2021 science fiction novel, Klara and the Sun. This story is narrated from the perspective of Klara, an anthropomorphic social robot with humanlike characteristics such as observing, learning, empathising, and interacting. She changes her identity from an attractive object to a reliable attachment figure, forming close bonds with, and even becoming an extended self of, her owner. Thus, this story challenges the traditional human-robot relations away from the master-slave model and develops into an intimate and beneficial connection. Drawing on John Bowlby's concepts of "safe haven" and "secure base" and David Levy's new interpretations of attachment theory, this research explores why human characters seek robotic attachment instead of human companions and how the close attachment is revealed in human-robot relations. The findings of this study make more contributions to the human-robot relations in literature in a positive perspective. [ABSTRACT FROM AUTHOR]

Robotic surgery, also known as robotic-assisted surgery (RAS), has rapidly evolved during the last decade. RAS systems are developed to assist surgeons to perform complex minimally invasive surgeries, and necessitate augmented interfaces for precise execution of these image-guided procedures. Extended Reality (XR) technologies, augmenting the real-world perception via integrating digital contents, show promise in enhancing RAS efficacy in various studies. Despite multiple reviews on technological and medical aspects, the crucial elements of human-robot interaction (HRI) and user experience (UX) remain underexplored. This review fills this gap by elucidating HRI dynamics within XR-aided RAS systems, emphasizing their impact on UX and overall surgical outcomes. By synthesizing existing literature, this systematic review study identifies challenges and opportunities, paving the way for improved XR-enhanced robotic surgery, ultimately enhancing patient care and surgical performance. [ABSTRACT FROM AUTHOR]

Humanoid robots are designed to mimic human structures and utilize cameras to process visual input to identify surrounding objects. However, previous studies have focused solely on object detection, overlooking both the complexities of real-world implementation and the significance of calculating the distance between objects and the robot. This study proposes a system that employs the you only look once (YOLO) algorithm to detect various objects in the proximity of a robot. Using a dataset of primary data collected in a laboratory, the detected objects are from 12 classes, including humans, chairs, tables, cabinets, computers, books, doors, bottles, eggs, learning modules, cups, and hands, with each class comprising 1500 data points. Two YOLO architectures, namely tiny YOLOv3 and tiny YOLOv4, are assessed for their performance in object detection, with the tiny YOLOv4 demonstrating a superior accuracy of 82.99% compared to tiny YOLOv3. Evaluation under simulated conditions yields an accuracy of 74.16%, while in real-time scenarios, accuracies are 61.66% under bright conditions and 38.33% under dim conditions, affirming tiny YOLOv4's efficacy. Moreover, this study reveals an average error distance of 31% between an object and the robot in real-time conditions. The developed system enhances human-robot interaction capabilities via data transmission. [ABSTRACT FROM AUTHOR]

As human–robot interaction (HRI) becomes increasingly significant, various studies have focused on speaker recognition. However, few studies have explored this topic in the specific environment of home service robots. Notably, most existing research relies on databases composed of English-language data, while studies utilizing Korean speech data are exceedingly scarce. This gap underscores the need for research on speaker recognition in robotic environments, specifically using Korean data. In response, this paper conducts experiments using a speaker recognition database tailored to the Korean language and set in a robotic context. The database includes noise generated by robot movement as well as common environmental noise, accounting for variable distances between humans and robots, which are partitioned accordingly. The deep learning model employed is SincNet, with experiments conducted under two settings for the SincNet filter parameters: one with learnable parameters and the other with fixed values. After training the model with data collected at varying distances, performance was tested across these distances. Experimental results indicate that SincNet with learnable parameters achieved a peak accuracy of 99%. [ABSTRACT FROM AUTHOR]

Since the development of commercial robots dedicated to service or social encounters, there have been numerous appearances of such devices in public spaces or corporate buildings. However, their purpose might not be self-evident and the modalities for using it might not be self-explainable. Moreover, 'talking' to a robot that imitates a receptionist could raise practical problems, given the fact that 'talk' among humans is an interactional resource for performing actions that carry social dimensions. This paper focuses on the dimension of 'preference organization'; specifically, offer rejections that are dispreferred among humans. Based on conversation analysis of human-robot interactions recorded in a university library, we examined 95 occurrences of how library users rejected offers of assistance initiated by a humanoid robot, Pepper. We identified three embodied rejection practices embedded in other courses of activity among groups of library users. Such practices show how users index their own orientation towards the transposition (or not) of human interactional norms that are borne with rejections as marked social moves. [ABSTRACT FROM AUTHOR]

For humanoid robots, gender-ambiguous presentation is implemented as a potential way to avoid gender-stereotypical design. Using conversation analysis, we look at video recorded user interaction in the presence of a designedly gender-ambiguous robot, showing how this design choice is actually dealt with within a social context. Robot gender becomes relevant initially when a user refers to the robot with a dual-gendered package ('young lady young gentleman'), with another user proposing 'her' for the robot, and the talk then evolving to the pursuit of agreement on the robot's proposed femininity. Robot gender attribution is treated by these users as a collaborative endeavor rather than an individual choice. It includes displays of accountability and orientations to delicateness of gender attribution. By implication, the analysis shows that ambiguous design shifts the burden of gender attribution from robot designers to users. [ABSTRACT FROM AUTHOR]

As chatbots become more advanced and popular, marketing research has paid enormous attention to the antecedents of consumer adoption of chatbots. This has become increasingly relevant because chatbots can help mitigate the fear and loneliness caused by the global pandemic. Therefore, unlike previous work that focused on design factors, we theorize that social presence serves a mediating role between consumer motivations (i.e., hedonic and utilitarian) and intention to use a chatbot service based on self-determination theory. Our results from a structural equation model (n = 377) indicate that hedonic (but not utilitarian) motivation significantly affects chatbots' social presence, ultimately influencing intention to use the chatbot service. We also found that fear of COVID-19 amplifies the effect of social presence on intention to use the chatbot service. In this dynamic, we found an additional moderated moderation effect of generational cohorts (i.e., baby boomers and Generations X, Y, and Z) in experiencing different levels of fear of COVID-19. Overall, our findings emphasize the importance of motivation-matching features for consumer adoption of chatbot services. Our findings also indicate that marketers may utilize the fear element to increase adoption of chatbot services, especially when targeting the young generations (e.g., Generation Z). [ABSTRACT FROM AUTHOR]

Due to an increasing number of robots in the working world, the interaction between humans and robots will become more and more frequent. Hence, the research field of human–robot collaboration is becoming progressively relevant and should therefore be included in students' education as early as possible. This paper deals with the requirements of a basic course for university students of interdisciplinary studies like Engineering Psychology or Human Factors. The goal of the course is to provide an adequate training for the students in order to gain a technical basis to design human–robot collaboration in a user-centered way in their future working life. Following a user-centered approach a narrative description and analysis of the context of use with contextual interviews were conducted. Overall, 12 subjects of the identified user groups (1) students studying Engineering Psychology, (2) students with knowledge in robotics and (3) graduates of the Engineering Psychology program participated in the contextual interviews. With these interviews, 47 user and stakeholder needs and 39 user requirements were derived. Based on the results, relevant content of a basic student course was defined, structured and prioritized. The requirements and demands are summarized in a recommendation for action and further steps are presented. [ABSTRACT FROM AUTHOR]

Integrating telepresence robots with gamification opens up new directions in which higher-education governance could translate into higher levels of student engagement in the AI-driven digital era. Drawing on both constructivism and the self-determination theory (SDT), this study will review evidence on how these technologies enhance autonomy and motivation, boosting effective participation in diverse learning environments. The experiments were carried out across various subjects with socio-economically varied groups of students by deploying gamified learning modules on telepresence robots. Primary metrics under consideration involved participation rates, task completion times, and other qualitative feedback measures about impacts created by such technologies. Indeed, the participation rates of the robot group were 40% higher, task completion times were reduced by 30%, and module completion rates for the robot group were 30% improved. The students reported positive emotions and showed more engagement, with the low-income students showing an 80% engagement rate compared to 40% in the control group. The results from both robots and gamification show promising potential for reshaping traditional learning paradigms, especially for students from geographically distant and underserved areas. The study guides further research on applying advanced tools in higher-education governance to foster sustainable practices in the AI era. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Social robots in our daily surroundings, like personal guides, waiter robots, home helpers, assistive robots, and telepresence/teleoperation robots, are increasing day by day. Their usability and acceptability largely depend on their explicit and implicit interaction capability with fellow human beings. As a result, social behavior is one of the most sought-after qualities that a robot can possess. However, there is no specific aspect and/or feature that defines socially acceptable behavior, and it largely depends on the situation, application, and society. In this article, we investigate one such social behavior for collocated robots. Imagine a group of people is interacting with each other, and we want to join the group. We as human beings do it in a socially acceptable manner, i.e., within the group, we do position ourselves in such a way that we can participate in the group activity without disturbing/obstructing anybody. To possess such a quality, first, a robot needs to determine the formation of the group and then determine a position for itself, which we humans do implicitly. There are many theories which study group formations and proxemics; one such theory is f-formation which could be utilized for this purpose. As the types of formations can be very diverse, detecting the social groups is not a trivial task. In this article, we provide a comprehensive survey of the existing work on social interaction and group detection using f-formation for robotics and other applications. We also put forward a novel holistic survey framework combining some of the possibly more important concerns and modules relevant to this problem. We define taxonomies based on methods, camera views, datasets, detection capabilities and scale, evaluation approaches, and application areas. We discuss certain open challenges and limitations in the current literature along with possible future research directions based on this framework. In particular, we discuss the existing methods/techniques and their relative merits and demerits, applications, and provide a set of unsolved but relevant problems in this domain. The official website for this work is available at: https://github.com/HrishavBakulBarua/Social-Robots-F-formation [ABSTRACT FROM AUTHOR]

The deployment of autonomous delivery robots in urban environments presents unique challenges in navigating complex traffic conditions and interacting with diverse road and sidewalk users. Effective communication between robots and road and sidewalk users is crucial to address these challenges. This study investigates real-world encounter scenarios where delivery robots and road and sidewalk users interact, seeking to understand the essential role of communication in ensuring seamless encounters. Following an online ethnography approach, we collected 117 user-generated videos from TikTok and their associated 2,067 comments. Our systematic analysis revealed several design opportunities to augment communication between delivery robots and road and sidewalk users, which include facilitating multi-party path negotiation, managing unexpected robot behaviour via transparency information, and expressing robot limitations to request human assistance. Moreover, the triangulation of video and comments analysis provides a set of design considerations to realise these opportunities. The findings contribute to understanding the operational context of delivery robots and offer insights for designing interactions with road and sidewalk users, facilitating their integration into urban spaces. [ABSTRACT FROM AUTHOR]

Robots can use auditory, visual, or haptic interfaces to convey information to human users. The way these interfaces select signals is typically pre-defined by the designer: for instance, a haptic wristband might vibrate when the robot is moving and squeeze when the robot stops. But different people interpret the same signals in different ways, so that what makes sense to one person might be confusing or unintuitive to another. In this article, we introduce a unified algorithmic formalism for learning co-adaptive interfaces from scratch. Our method does not need to know the human's task (i.e., what the human is using these signals for). Instead, our insight is that interpretable interfaces should select signals that maximize correlation between the human's actions and the information the interface is trying to convey. Applying this insight we develop Learning Interfaces to Maximize Information Transfer (LIMIT). LIMIT optimizes a tractable, real-time proxy of information gain in continuous spaces. The first time a person works with our system the signals may appear random; but over repeated interactions, the interface learns a one-to-one mapping between displayed signals and human responses. Our resulting approach is both personalized to the current user and not tied to any specific interface modality. We compare LIMIT to state-of-the-art baselines across controlled simulations, an online survey, and an in-person user study with auditory, visual, and haptic interfaces. Overall, our results suggest that LIMIT learns interfaces that enable users to complete the task more quickly and efficiently, and users subjectively prefer LIMIT to the alternatives. See videos here: https://youtu.be/IvQ3TM1_2fA. [ABSTRACT FROM AUTHOR]

In Human–Robot Interaction, researchers typically utilize in-person studies to collect subjective perceptions of a robot. In addition, videos of interactions and interactive simulations (where participants control an avatar that interacts with a robot in a virtual world) have been used to quickly collect human feedback at scale. How would human perceptions of robots compare between these methodologies? To investigate this question, we conducted a 2 \({\times}\) 2 between-subjects study (N \({=}\) 160), which evaluated the effect of the interaction environment (Real vs. Simulated environment) and participants' interactivity during human-robot encounters (Interactive participation vs. Video observations) on perceptions about a robot (competence, discomfort, social presentation, and social information processing) for the task of navigating in concert with people. We also studied participants' workload across the experimental conditions. Our results revealed a significant difference in the perceptions of the robot between the real environment and the simulated environment. Furthermore, our results showed differences in human perceptions when people watched a video of an encounter versus taking part in the encounter. Finally, we found that simulated interactions and videos of the simulated encounter resulted in a higher workload than real-world encounters and videos thereof. Our results suggest that findings from video and simulation methodologies may not always translate to real-world human–robot interactions. In order to allow practitioners to leverage learnings from this study and future researchers to expand our knowledge in this area, we provide guidelines for weighing the tradeoffs between different methodologies. [ABSTRACT FROM AUTHOR]

Despite the existence of robots that can physically lift heavy loads, robots that can collaborate with people to move heavy objects are not readily available. This article makes progress toward effective human-robot co-manipulation by studying 30 human-human dyads that collaboratively manipulated an object weighing \(27 \mathrm{kg}\) without being co-located (i.e., participants were at either end of the extended object). Participants maneuvered around different obstacles with the object while exhibiting one of four modi–the manner or objective with which a team moves an object together–at any given time. Using force and motion signals to classify modus or behavior was the primary objective of this work. Our results showed that two of the originally proposed modi were very similar, such that one could effectively be removed while still spanning the space of common behaviors during our co-manipulation tasks. The three modi used in classification were quickly, smoothly and avoiding obstacles. Using a deep convolutional neural network (CNN), we classified three modi with up to 89% accuracy from a validation set. The capability to detect or classify modus during co-manipulation has the potential to greatly improve human-robot performance by helping to define appropriate robot behavior or controller parameters depending on the objective or modus of the team. [ABSTRACT FROM AUTHOR]

Human–robot interaction (HRI) in human social environments (HSEs) poses unique challenges for robot perception systems, which must combine asynchronous, heterogeneous data streams in real time. Multimodal perception systems are well-suited for HRI in HSEs and can provide more rich, robust interaction for robots operating among humans. In this article, we provide an overview of multimodal perception systems being used in HSEs, which is intended to be an introduction to the topic and summary of relevant trends, techniques, resources, challenges, and terminology. We surveyed 15 peer-reviewed robotics and HRI publications over the past 10+ years, providing details about the data acquisition, processing, and fusion techniques used in 65 multimodal perception systems across various HRI domains. Our survey provides information about hardware, software, datasets, and methods currently available for HRI perception research, as well as how these perception systems are being applied in HSEs. Based on the survey, we summarize trends, challenges, and limitations of multimodal human perception systems for robots, then identify resources for researchers and developers and propose future research areas to advance the field. [ABSTRACT FROM AUTHOR]

During the past 15 years, an increasing amount of works have investigated proactive robotic behavior in relation to Human–Robot Interaction (HRI). The works engage with a variety of research topics and technical challenges. In this article, a review of the related literature identified through a structured block search is performed. Variations in the corpus are investigated, and a definition of Proactive HRI is provided. Furthermore, a taxonomy is proposed based on the corpus and exemplified through specific works. Finally, a selection of noteworthy observations is discussed. [ABSTRACT FROM AUTHOR]

In recent years, technologies for human–robot interaction (HRI) have undergone substantial advancements, facilitating more intuitive, secure, and efficient collaborations between humans and machines. This paper presents a decentralized HRI platform, specifically designed for printed circuit board manufacturing. The proposal incorporates many input devices, including gesture recognition via Leap Motion and Tap Strap, and speech recognition. The gesture recognition system achieved an average accuracy of 95.42% and 97.58% for each device, respectively. The speech control system, called Cellya, exhibited a markedly reduced Word Error Rate of 22.22% and a Character Error Rate of 11.90%. Furthermore, a scalable user management framework, the decentralized multimodal control server, employs biometric security to facilitate the efficient handling of multiple users, regulating permissions and control privileges. The platform's flexibility and real-time responsiveness are achieved through advanced sensor integration and signal processing techniques, which facilitate intelligent decision-making and enable accurate manipulation of manufacturing cells. The results demonstrate the system's potential to improve operational efficiency and adaptability in smart manufacturing environments. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to examine the congruency effects of physically embodied robots in service encounters, which addressed a significant research gap concerning the synthesis of robot design elements (e.g., appearance and voice) and their service purposes. Design/methodology/approach: Grounded in congruity theory and human-robot interaction literature, this study conducted a pretest and two experimental studies revealing the need to view robot design holistically and recognizing the pivotal role of congruity in shaping consumers' service robot adoption. The moderating role of service purposes (utilitarian vs hedonic) was also investigated in terms of robot design and consumer reactions. Findings: Consumers generally tend to favor robots with congruent designs, particularly for utilitarian service purposes. The serial mediation through perceived congruence and perceived intelligence explains such a favorite tendency. Practical implications: This study advances service robot design research by highlighting the critical role of congruity in enhancing consumer engagement. It supports the use of comprehensive, congruent designs for services with utilitarian purposes and recommends adaptable designs for hedonic settings. Originality/value: This study addressed the research gap by examining service robot design from a holistic perspective. The research findings highlight the importance of congruency effects in service robot design and deployment and provide valuable insights and guidelines to industry practitioners for optimal investment in service robots. [ABSTRACT FROM AUTHOR]

With the advancement of industrial automation, the frequency of human–robot interaction (HRI) has significantly increased, necessitating a paramount focus on ensuring human safety throughout this process. This paper proposes a simulation‐assisted neural network for point cloud segmentation in HRI, specifically distinguishing humans from various surrounding objects. During HRI, readily accessible prior information, such as the positions of background objects and the robot's posture, can generate a simulated point cloud and assist in point cloud segmentation. The simulation‐assisted neural network utilizes simulated and actual point clouds as dual inputs. A simulation‐assisted edge convolution module in the network facilitates the combination of features from the actual and simulated point clouds, updating the features of the actual point cloud to incorporate simulation information. Experiments of point cloud segmentation in industrial environments verify the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

This research introduces an innovative Augmented Reality (AR) workflow for Human-Robot Interaction (HRI) in timber construction. The approach leverages human dexterity and adaptability alongside the strength and precision of robotic arms to assemble timber structures connected by wood-wood connections. While research in the field of automated construction generally focuses on singular interactions, such as robot agents carrying components and human agents attaching them, this paper explores multiple degrees of interaction involving cooperation or collaboration between agents. A new algorithmic framework is developed to automate the generation of holographic instructions and allocate assembly tasks to human and robot agents according to their abilities. The application to a full-scale demonstrator reveals that certain elements necessitate collaboration for assembly, while others can exclusively be assembled manually or robotically. Ultimately, the research also highlights the benefits of AR in assisting manual assembly, simulating robot trajectories, and increasing safety during collaborative tasks. [ABSTRACT FROM AUTHOR]

Robot backchanneling is implemented using verbal expressions, such as reiterating after remembering the response of the user or chiming in with the user. We performed experiments to investigate how the backchannel behavior of the robot affects the user's perception of the robot's empathy and attentiveness toward their reported pain and sleep concerns. The results indicate that users prefer the robot that provides backchannel and perceive it as more intelligent. Where the robot backchanneled, the number of conversational turns between the user and the robot decreased compared to situations where the robot did not backchannel. The refusal rate of the robot repeating the same question to the user decreased. When a robot is conducting a medical questionnaire and offers backchannels to the user, the overall efficiency of the conversation improves. Users also perceive the robot as understanding and empathetic towards their pain and discomfort, leading to a positive evaluation of the interaction. [ABSTRACT FROM AUTHOR]

Humanlike robots are increasingly employed to provide frontline services. They are frequently designed with stereotypically feminine or masculine humanlike features which affect or bias consumer behavior in service encounters. This systematic review of 118 peer‐reviewed journal papers aims to comprehensively capture the current status of the field and identify important research gaps requiring further investigation. Following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses protocol, a comprehensive framework is developed to conceptualize the process of customer interactions with humanlike service robots, depicting how humanlike service robots influence consumer behavior. Specifically, we identify interaction antecedents, consumer processing factors, outcomes of the interactions, and strengthening/attenuating factors. Based on the framework, the review concludes by identifying issues that future research should seek to solve to contribute to the field. This paper provides a deep understanding of service robot anthropomorphism in marketing and consumer research and proposes a future research agenda to advance knowledge of the field. [ABSTRACT FROM AUTHOR]

Soft robotics is an emerging field dedicated to the design and development of robots with soft structures. Soft robots offer unique capabilities in terms of flexibility, adaptability, and safety of physical interaction, and therefore provide advanced collaboration between humans and robots. The further incorporation of soft actuators, advanced sensing technologies, user-friendly control interfaces, and safety considerations enhance the interaction experience. Applications in healthcare, specifically in rehabilitation and assistive devices, as well as manufacturing, show how soft robotics has revolutionized human–robot collaboration and improved quality of life. Soft robotics can create new opportunities to enhance human well-being and increase efficiency in human–robot interactions. Nevertheless, challenges persist, and future work must focus on overcoming technological barriers while increasing reliability, refining control methodologies, and enhancing user experience and acceptance. This paper reviews soft robotics and outlines its advantages in scenarios involving human–robot interaction. [ABSTRACT FROM AUTHOR]

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Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Numerous technological advancements have emerged within workplaces, notably the elevated levels of human interaction with robots. In this study, a comprehensive tool, Human-Robot Interaction Stress Scale (HRISS) was devised specifically for assessing the job-related stress experienced by operators engaged with robots. The risk factors were discerned through an exhaustive literature review, complemented by semi-structured interviews. The initial set of questions was formulated using the "pool of questions" method. These questions underwent comprehensive evaluation employing various reliability and validity techniques, including thinking-aloud protocols, the content validity index (CVI), the content validity ratio (CVR), and Cronbach's alpha. Additionally, the construct validity of the final questionnaire was demonstrated by examining its correlation with six health factors. The final version of the questionnaire comprised 46 inquiries, categorizing human-related factors into seven distinct sub-scales: situation awareness, resilience, self-efficacy, trust in robots, competency, robot affinity, and fear of substitution. Task-related factors were delineated across six sub-scales encompassing the need for staying updated, job demands, environmental conditions, skill utilization, technical support, and decision complexity. Additionally, the questionnaire incorporated four sub-scales pertaining to robot-related factors, namely usability, automation level, safety, and hardware/software issues. Notably, the final version exhibited satisfactory levels of both validity and reliability. The findings demonstrated that the proposed version of the HRISS effectively evaluates the job stress experienced by operators engaging with robots. [ABSTRACT FROM AUTHOR]

In human–robot interaction (HRI), it is essential for the robot to recognize human emotions and also to be able to convey emotions. With this accomplished, interactions between humans and robots can become natural and empathetic. Additionally, it is important to emphasize the significance of user-centered design evaluation when developing HRI systems, ensuring that user needs and preferences are considered from the initial design phases to the final implementation. This user-centered approach can significantly contribute to the effectiveness and acceptance of robotic systems in human interaction. Therefore, this article aims to conduct a systematic literature review on HRI to address two questions: (i) how has the literature approached the use of emotions in human–robot interaction? and (ii) how was the performance of using emotions in HRI evaluated in these studies? For this purpose, 72 articles were selected using a systematic methodology. The selected literature works have shown a tendency to use HRI with emotions in robots in two ways. The first is developing methods for recognizing emotions in humans. The second is studying how to represent emotions in robots so that HRI can become natural and friendly. Furthermore, it can be observed that the majority of the articles considered here used an inquiry method with the presence of participants to evaluate the robotic performance system. [ABSTRACT FROM AUTHOR]

Social robots can be designed to facilitate co-creation. The impact of mood expressions displayed by human facilitators can elicit similar moods in others, fostering collaboration, process satisfaction, and co-creation performance. However, the extent to which robot mood expressions can influence such group dynamics to the same effect remains an open question. To address this, we conducted an experiment (N = 110) in which small groups engaged in a co-creation session facilitated by a social robot that displayed positive, neutral, or negative mood expressions. The results showed that robot mood expressions evoked corresponding mood contagion among human group members. The influence of robot expressions on mood valence significantly enhanced group collaboration and process satisfaction. Additionally, participants' ability to contribute insights during the session and ensure these are represented in session outcomes, an index of co-creation performance, significantly improved when positive robot mood expressions increased mood contagion valence. By establishing the influence of robot mood expressions on mood contagion, group collaboration, process satisfaction, and co-creation performance, we shed light on the potential of social robots for facilitating and enhancing co-creation. Understanding the mechanisms underlying these effects adds to a growing body of work in the field of human-robot interaction and offers valuable insights for developing innovative future robotic systems. [ABSTRACT FROM AUTHOR]

Sign languages are one of the main rehabilitation methods for dealing with hearing loss. Like any other language, the geographical location will influence on how signs are made. Particularly in Colombia, the hard of hearing population is lacking from education in the Colombian Sign Language, mainly due of the reduce number of interpreters in the educational sector. To help mitigate this problem, Machine Learning binded to data gloves or Computer Vision technologies have emerged to be the accessory of sign translation systems and educational tools, however, in Colombia the presence of this solutions is scarce. On the other hand, humanoid robots such as the NAO have shown significant results when used to support a learning process. This paper proposes a performance evaluation for the design of an activity to support the learning process of all the 11 color-based signs from the Colombian Sign Language. Which consists of an evaluation method with two modes activated through user interaction, the first mode will allow to choose the color sign to be evaluated, and the second will decide randomly the color sign. To achieve this, MediaPipe tool was used to extract torso and hand coordinates, which were the input for a Neural Network. The performance of the Neural Network was evaluated running continuously in two scenarios, first, video capture from the webcam of the computer which showed an overall F1 score of 91.6% and a prediction time of 85.2 m, second, wireless video streaming with NAO H25 V6 camera which had an F1 score of 93.8% and a prediction time of 2.29 s. In addition, we took advantage of the joint redundancy that NAO H25 V6 has, since with its 25 degrees of freedom we were able to use gestures that created nonverbal human-robot interactions, which may be useful in future works where we want to implement this activity with a deaf community. [ABSTRACT FROM AUTHOR]

AbstractSocial robots, driven by cutting-edge technologies are designed to cater to various societal needs, facilitating complex human interactions involving multiple users and stakeholders. Their integration into daily life is anticipated to increase significantly. Within this context, the domain of shopping robots, which play a crucial role in enhancing and diversifying shopping experiences where human interaction is paramount, holds immense potential for development. This article aims to provide an overview of the current landscape of shopping robots and explore future research directions in this evolving field. Through this systematic review, key trends and insights in the field of shopping robots are identified, while also offering a categorization in the form of a 3D conceptual scheme, called the Public Space Robot (PSR) framework. The outcomes reveal significant developments over the past two decades (2002–2024), with the main concentration on developing and deploying mobile robots that offer functional or autonomous interaction for navigation assistance and customer service in shopping malls and retail stores. [ABSTRACT FROM AUTHOR]

Introduction: Long-distance robot teleoperation faces high latencies that pose cognitive challenges to human operators. Latency between command, execution, and feedback in teleoperation can impair performance and affect operators' mental state. The neural underpinnings of these effects are not well understood. Methods: This study aims to understand the cognitive impact of latency in teleoperation and the related mitigation methods, using functional Near-Infrared Spectroscopy (fNIRS) to analyze functional connectivity. A human subject experiment (n = 41) of a simulated remote robot manipulation task was performed. Three conditions were tested: no latency, with visual and haptic latency, with visual latency and no haptic latency. fNIRS and performance data were recorded and analyzed. Results: The presence of latency in teleoperation significantly increased functional connectivity within and between prefrontal and motor cortexes. Maintaining visual latency while providing real-time haptic feedback reduced the average functional connectivity in all cortical networks and showed a significantly different connectivity ratio within prefrontal and motor cortical networks. The performance results showed the worst performance in the all-delayed condition and best performance in no latency condition, which echoes the neural activity patterns. Conclusion: The study provides neurological evidence that latency in teleoperation increases cognitive load, anxiety, and challenges in motion planning and control. Real-time haptic feedback, however, positively influences neural pathways related to cognition, decision-making, and sensorimotor processes. This research can inform the design of ergonomic teleoperation systems that mitigate the effects of latency. [ABSTRACT FROM AUTHOR]