Search

This research explores prospective determinants of trust in the recommendations of artificial agents regarding decisions to kill, using a novel visual challenge paradigm simulating threat-identification (enemy combatants vs. civilians) under uncertainty. In Experiment 1, we compared trust in the advice of a physically embodied versus screen-mediated anthropomorphic robot, observing no effects of embodiment; in Experiment 2, we manipulated the relative anthropomorphism of virtual robots, observing modestly greater trust in the most anthropomorphic agent relative to the least. Across studies, when any version of the agent randomly disagreed, participants reversed their threat-identifications and decisions to kill in the majority of cases, substantially degrading their initial performance. Participants subjective confidence in their decisions tracked whether the agent (dis)agreed, while both decision-reversals and confidence were moderated by appraisals of the agents intelligence. The overall findings indicate a strong propensity to overtrust unreliable AI in life-or-death decisions made under uncertainty.

Many firms are beginning to replace customer service employees with bots, from humanoid service robots to digital chatbots. Using real human–bot interactions in lab and field settings, we study consumers' evaluations of bot-provided service. We find that service evaluations are more negative when the service provider is a bot versus a human—even when the provided service is identical. This effect is explained by consumers' belief that service automation is motivated by firm benefits (i.e. cutting costs) at the expense of customer benefits (such as service quality). The effect is eliminated when firms share the economic surplus derived from automation with consumers through price discounts. The effect is reversed when service bots provide unambiguously superior service to human employees—a scenario that may soon become reality. Consumers' default reactions to service bots are therefore largely negative but can be equal to or better than reactions to human service providers if firms can demonstrate how automation benefits consumers. [ABSTRACT FROM AUTHOR]

Robotisation is increasing in warehouse operations, but human employment continues to be relevant. Traditionally manual activities, such as order picking, are being re-designed into collaborative human–robot tasks. This trend exemplifies the transition towards a human-centric Industry 5.0, focusing on synergy instead of seeking human replacement. However, human workers are increasingly hard to recruit and retain. We contribute to the underrepresented literature on human factors within the domain of operations and production management research and investigate the deployment of robotic technologies alongside human workers in a sustainable way. With a unique real-effort experiment, we investigate how the manipulation of picker's experienced levels of autonomy affects their job satisfaction and core self-evaluations, two key behavioural outcomes that determine employee turnover intentions. We establish that the introduction of human–robot collaboration positively affects job satisfaction for the contrasting collaboration dynamics of (i) gaining control (the human leading the robot) and (ii) ceding control (the human following the robot). This positive effect is larger when the human is following the robot. We additionally find that following the robot positively affects pickers' self-esteem and that self-efficacy related to human–robot interaction benefits from the introduction of collaborative robotics, regardless of the setup dynamics. [ABSTRACT FROM AUTHOR]

In the 21st-century era of globalization, language proficiency is a pivotal connector across cultures, with artificial intelligence (AI) revolutionizing educational paradigms through Robot-Assisted Language Learning (RALL). This systematic review examines the role of RALL in adult second language acquisition, focusing on its pedagogical strategies and learner engagement. Unlike the previous systematic reviews that explore the multifaceted roles of robots in language learning, including as teachers, tutors, assistants, and peer learners, we identify explicit and implicit instructional strategies within RALL, highlighting the unique learning landscape of adult learners characterized by self-regulation and self-direction. We assess the latest advancements in RALL for adult learners through three research questions, compare the effectiveness of explicit versus implicit instructions, and investigate affective factors enhancing RALL performance. Our review contributes a comprehensive status analysis, in-depth exploration of interaction modes, and insights for future research directions, providing a roadmap for academic research and practical guidance for educators and robot developers. This study aims to optimize RALL strategies to better meet the needs of adult learners, fostering a more efficient and engaging language learning experience. [ABSTRACT FROM AUTHOR]

A major research question in psycholinguistics relates to the phenomenon of linguistic relativity, which contends that the language one speaks influences how one thinks. Of particular interest is whether bilingual speakers shift cognitive paradigms when speaking different languages. Here, we conducted a human-agent interaction (HAI) study using a bilingual virtual avatar capable of autonomous speech during cooperative gameplay in two languages (Korean and English). We ran 40 participants, including 20 monolingual speakers (10 Korean, 10 English) and 20 Korean/English bilingual speakers, engaging the avatar during 30-minute game sessions. Comparison of speech patterns showed that bilingual speakers exhibited notable "cognitive shifts" in both languages while interacting with the avatar, which were markedly different from their monolingual counterparts. Interestingly, the virtual avatar's own speech behavior also significantly changed during interaction with bilingual speakers, despite identical programming. As evidenced here, such cognitive shifts appear to impact the way humans interact with artificial agents. [ABSTRACT FROM AUTHOR]

With the embodied interaction paradigm, research on human emotions has rapidly increased. In parallel, the advent of artificial intelligence and automated technologies has spurred research on trust towards interactive systems. However, little research has directly investigated the effects of emotions on trust in the context of technology use. The present paper surveyed empirical studies using the PRISMA framework. After briefly introducing emotional effects on cognitive processes, twenty-nine studies were systematically analyzed. In many papers positive emotions or empathically congruent systems led to higher trust. Some studies indicated that emotions can be a mediator between different factors and trust, whereas other studies showed only partial effects depending on different users or situations. Note that some research showed null effects or even negative effects (backfire effects) because emotional systems can be perceived as sarcastic or uncanny. In addition to the pervasive mood congruent effect or emotional contagion, various psychological mechanisms and theories were identified, such as entitativity, cognitive appraisal, or affect infusion model. Considerations for future design and research are discussed with results. This survey paper is expected to deepen the theoretical aspects of emotional effects on trust towards diverse technologies (robots, agents, or other interactive systems) and provide practical design directions. [ABSTRACT FROM AUTHOR]

It is extremely challenging for security guard robots to independently stop human line-cutting behavior. We propose addressing this issue by using humorous phrases. First, we created a dataset and built a humor effectiveness predictor. Using a simulator, we replicated 13,000 situations of line-cutting behavior and collected 500 humorous phrases through crowdsourcing. Combining these simulators and phrases, we evaluated each phrase's effectiveness in different situations through crowdsourcing. Using machine learning with this dataset, we constructed a humor effectiveness predictor. In the process of preparing this machine learning, we discovered that considering the situation and the discomfort caused by the phrase is crucial for predicting the effectiveness of humor. Next, we constructed a system to select the best humorous phrase for the line-cutting behavior using this predictor. We then conducted a video experiment in which we compared the humorous phrases selected using this proposed system with typical non-humorous phrases. The results revealed that humorous phrases selected by the proposed system were more effective in discouraging line-cutting behavior than typical non-humorous phrases. [ABSTRACT FROM AUTHOR]

Previous research has demonstrated the influence of commensal dining between humans on food choices, whereas we conducted two studies to examine how the presence of a robot might influence people's choices between meat‐heavy and vegetable‐forward meals in imaginary scenarios. In Study 1, participants were instructed to choose three desirable dishes from a set of two meat and two vegetable dishes while they imagined eating alone, with a human, or with a robot. Although the meat dishes were rated as more palatable and pleasant, the female participants chose fewer meat‐heavy meals when eating alone or with a robot than when eating with a human, whereas no such effect was observed for the male participants. We also replicated these patterns in Study 2, as the female participants chose fewer meat‐heavy meals when eating with a robot and a human than when eating with two humans. Collectively, these findings provide empirical evidence regarding how the presence of a certain robot can influence female consumers' food choices in imaginary scenarios, which has direct implications for the practice of promoting sustainable food choices. [ABSTRACT FROM AUTHOR]

As robotics and intelligence increasingly integrate into surgery, the pivotal role of human–robot interaction (HRI) in surgical procedures and outcomes becomes evident. However, debate rages over whether increasing robot autonomy will result in less human involvement. Some scholars assert that autonomy will reduce human participation, whereas others contend it will result in more complex interactions. To reveal the role of HRI in the evolution of autonomous surgery, this review systematically explores the HRI of robotic surgery with various levels of autonomy. The HRI is examined from both robotic science and clinical practice perspectives, incorporating relevant case studies. Two key components, intention detection and situation awareness, are especially concerned with a brief description of the interfaces and control strategies they rely on. Additional insights are drawn from analogous technologies in aviation, industrial robotics, and autonomous vehicles. The analysis suggests that HRI complexity tends to increase as the robot transitions from no autonomy to conditional autonomy and is predicted to subsequently decrease with a substantial shift in the interaction form when moving toward full autonomy. It is concluded by highlighting challenges from technical and clinical perspectives and delineating research trends in this rapidly evolving field. [ABSTRACT FROM AUTHOR]

The application of robotic devices is being used as Assistive Technology (AT) for improving rehabilitation interventions. The purposes of this research were to (1) test a novel low-cost robotic AT to support interventions for people with Cerebral Palsy (CP); (2) determine its usability; and (3) analyze its impact. It was a pilot study with prospective, longitudinal and analytical cohorts was done. Intervention was developed in one association (NGO) of people with CP. Participants were 6 women and 3 men with CP, with a mean age of 51.67. Intervention with LOLA2 (a robotic platform, not wearable, equipped with artificial intelligence) was implemented for training some activities of daily life (ADL) of participants. Functional Independence Measure (FIM), Psychosocial Impact of Assistive Technology Scale (PIADS), and Assistive Technology Device Predisposition Assessment (ATPA) were used for outcome measures. Level of participants' independence was high (FIM = 98). Psychosocial impact of the robotic platform in terms of competence (M = 0.25), adaptability (M = 0.33), and self-esteem (M = 0.25), was positive, but low. The mean in ATDPA (M = 3) reflects a moderate match. No significant variations concerning the changes in functional independence were detected. The robotic platform is applicable and complementary AT for rehabilitation. This study leads to implementing some improvements in its design, proposed activities, human-robot interaction, and system for registering information. IMPLICATIONS FOR REHABILITATION: Robotic devices such as LOLA2 show promise as complementary tools in rehabilitation for individuals with Cerebral Palsy (CP), highlighting the potential for improved outcomes in clinical practice. There is a clear need for enhancements in the usability, universal design, and technical specifications of robotic platforms like LOLA2 to ensure their full functionality and quality, underscoring the importance of ongoing development and refinement. Utilizing specific outcome measures tailored to assess the efficacy and applicability of assistive technologies (AT), including robotic devices, is crucial for comprehensive evaluation and informed decision-making in rehabilitation interventions. Further research with higher validity is necessary to expand the evidence base surrounding the use of robotic devices in assistive technology, guiding specialized technical decisions and enhancing their integration into rehabilitation practices. [ABSTRACT FROM AUTHOR]

The aim of this study was to design user-friendly off-line human–robot interactions based on a telephonic conversation database (TCD), and to provide the elderly living alone independently (ELAI) with psychological support. A TCD was established based on phone calls to 170 ELAI, made once every 2 weeks for 10 years. Based on the TCD, conversation flows were continuously analysed through a Question–answer-response format, which was centred on 366 ordinary topics, 291 physical topics, and 322 psychological topics. Usability was evaluated in a trial involving 15 ELAI in their homes over a 4-to-8-week period. Their emotional states were evaluated using the Profile of Mood States (2nd Edition Short Form) and Functional independence measure (FIM) pre- and post-trial. Eighty-seven per cent of the participants indicated that the robots helped them in their daily lives. A close examination of the changes in all participants showed statistically significant improvements in mood and 3 out of 5 FIM cognitive items. Eighty-seven per cent of study participants wished to continue the experiment. We determined that the communication robots, in conjunction with the TCD, improved the psychological well-being of the ELAI. [ABSTRACT FROM AUTHOR]

Object handover is one of the fundamental tasks of service robots. This paper focuses on a robot-to-human object handover controller applied to a domestic service robot. People in need often have manual operation constraints caused by different body postures or declined physical functions. The preplanned handover strategy used in previous studies potentially increases their cognitive burden and makes it difficult to meet their needs for flexible real-time control and intuitive interaction. It also remains challenging to deal with sensing and prediction errors, motion planning and coordination. The robot no longer provides assistance after an object is delivered to a preplanned handover location, and the receiver needs to make extra efforts to get the object released. Therefore, inspired by a human study, a handover controller is designed based on a manually guided handover strategy that solve problems from grasp adjustment to object release in dynamic retraction motion. Flexible control and intuitive interaction are enabled through motion with continuous support from the robot. Without additional sensors or modeling learning, changes in motion and energy consumption are taken into account to explore an appropriate release timing. The results of robot-to-human handover experiments and the user studies indicate that the receiver can freely pull an object to a suitable location as desired and smoothly obtain the object's control to complete a subsequent task using the proposed controller with proper release timing. [ABSTRACT FROM AUTHOR]

Human gaze is a crucial cue used in various applications such as human-robot interaction, autonomous driving, and virtual reality. Recently, convolution neural network (CNN) approaches have made notable progress in predicting gaze angels. However, estimating accurate gaze direction in-the-wild is still a challenging problem due to the difficulty of obtaining the most crucial gaze information that exists in the eye area which constitutes a small part of the face images. In this paper, we introduce a novel two-branch CNN architecture with a multi-loss approach to estimate gaze angles (pitch and yaw) from face images. Our approach utilizes separate fully connected layers for each gaze angle prediction, allowing explicit learning of discriminative features and emphasizing the distinct information associated with each gaze angle. Moreover, we adopt a multi-loss approach, incorporating both classification and regression losses. This allows for joint optimization of the combined loss for each gaze angle, resulting in improved overall gaze performance. To evaluate our model, we conduct experiments on three popular datasets collected under unconstrained settings: MPIIFaceGaze, Gaze360, and RT-GENE. Our proposed model surpasses current state-of-the-art methods and achieves state-of-the-art performance on all three datasets, showcasing its superior capability in gaze estimation. [ABSTRACT FROM AUTHOR]

Artificial intelligence in hospitality is increasingly transforming the way we travel. Service robots collect, store, analyze, and act upon a continuous stream of private information as a by-product of human-robot interaction. As such, they invade consumers' virtual and physical space and raise privacy challenges in AI settings. We conduct a survey study(n = 576) and validate a mediating contextualized model of consumers' adoption decisions on service robots in hospitality from privacy concerns. Our findings highlight the interplay between perceived risk and perceived benefit in shaping service robots' adoption decisions is partially mediated by privacy concerns. Our findings also highlight the mechanism of privacy concerns, conceptualized as psychological constructs of collection, control and awareness of privacy practices, as an important addition to the established multiple chain mediating effect. The insights explain how consumers calculate privacy between perceived risk and benefit, and help reconcile a fundamental tension among consumers, how to avail benefits of privacy by improving the privacy awareness and control associated with the collection of private information in hospitality. [ABSTRACT FROM AUTHOR]

This study investigates the relationships between firm characteristics (region, firm size, firm age, and product category) and Industry 4.0 technology adoption in the Vietnamese apparel manufacturing industry. As the key Industry 4.0 technologies, Artificial Intelligence (AI), Interactive Robotics, Smart Sensors, Big Data, 3D Printing, physical Cyber-Physical Systems (CPS), and human-robot interaction were included to examine technology adoption. The hypotheses were tested using a series of Chi-square analyses with data gathered from 210 Vietnamese apparel-manufacturing firms. The results indicate no clear pattern of associations between firm characteristics and technology adoption and provide empirical support for firms' strategic adoption of a certain set of Industry 4.0 technologies in line with their firm characteristics. On the other hand, the findings from scatter charts indicated that AI, Interactive Robotics, and 3D Printing are three key technologies that apparel firms applied the most and at a similar level. [ABSTRACT FROM AUTHOR]

It has been proven that robot-assisted rehabilitation training can effectively promote the recovery of upper-limb motor function in post-stroke patients. Increasing patients' active participation by providing assist-as-needed (AAN) control strategies is key to the effectiveness of robot-assisted rehabilitation training. In this paper, a greedy assist-as-needed (GAAN) controller based on radial basis function (RBF) network combined with 3 degrees of freedom (3-DOF) potential constraints was proposed to provide AAN interactive forces of an end-effect upper limb rehabilitation robot. The proposed 3-DOF potential fields were adopted to constrain the tangential motions of three kinds of typical target trajectories (one-dimensional (1D) lines, two-dimensional (2D) curves and three-dimensional (3D) spirals) while the GAAN controller was designed to estimate the motor capability of a subject and provide appropriate robot-assisted forces. The co-simulation (Adams-Matlab/Simulink) experiments and behavioral experiments on 10 healthy volunteers were conducted to validate the utility of the GAAN controller. The experimental results demonstrated that the GAAN controller combined with 3-DOF potential field constraints enabled the subjects to actively participate in kinds of tracking tasks while keeping acceptable tracking accuracies. 3D spirals could be better in stimulating subjects' active participation when compared to 1D and 2D target trajectories. The current GAAN controller has the potential to be applied to existing commercial upper limb rehabilitation robots. [ABSTRACT FROM AUTHOR]

This research report introduces a learning system designed to detect the object that humans are gazing at, using solely visual feedback. By incorporating face detection, human attention prediction, and online object detection, the system enables the robot to perceive and interpret human gaze accurately, thereby facilitating the establishment of joint attention with human partners. Additionally, a novel dataset collected with the humanoid robot iCub is introduced, comprising more than 22,000 images from ten participants gazing at different annotated objects. This dataset serves as a benchmark for human gaze estimation in table-top human-robot interaction (HRI) contexts. In this work, we use it to assess the proposed pipeline's performance and examine each component's effectiveness. Furthermore, the developed system is deployed on the iCub and showcases its functionality. The results demonstrate the potential of the proposed approach as a first step to enhancing social awareness and responsiveness in social robotics. This advancement can enhance assistance and support in collaborative scenarios, promoting more efficient human-robot collaborations. [ABSTRACT FROM AUTHOR]

This article delves into the dynamics of human interaction with artificial intelligence (AI), emphasizing the optimization of these interactions to enhance human productivity. Employing a Grounded Theory Literature Review (GTLR) methodology, the study systematically identifies and analyzes themes from literature published between 2018 and 2023. Data were collected primarily from the Scopus database, with the Web of Science used to corroborate findings and include additional sources identified through a snowball effect. At the heart of this exploration is the pivotal role of socio-emotional attributes such as trust, empathy, rapport, user engagement, and anthropomorphization—elements crucial for the successful integration of AI into human activities. By conducting a comprehensive review of existing literature and incorporating case studies, this study illuminates how AI systems can be designed and employed to foster deeper trust and empathetic understanding between humans and machines. The analysis reveals that when AI systems are attuned to human emotional and cognitive needs, there is a marked improvement in collaborative efficiency and productivity. Furthermore, the paper discusses the ethical implications and potential societal impacts of fostering such human-AI relationships. It argues for a paradigm shift in AI development—from focusing predominantly on technical proficiency to embracing a more holistic approach that values the socio-emotional aspects of human-AI interaction. This shift could pave the way for more meaningful and productive collaborations between humans and AI, ultimately leading to advancements that are both technologically innovative and human-centric. [ABSTRACT FROM AUTHOR]

This study aims to fill a gap in understanding how customising robots can affect how humans interact with them, specifically regarding human decision-making and robot perception. The study focused on the robot's ability to persuade participants to follow its suggestions within the Balloon Analogue Risk Task (BART), where participants were challenged to balance the risk of bursting a virtual balloon against the potential reward of inflating it further. A between-subjects design was used, involving 62 participants divided evenly between customised or non-customised robot conditions. Compliance, risk-taking, reaction time, and perceptions of the robot's likability, intelligence, trustworthiness, and ownership were measured using quantitative and qualitative methods. The results showed that there were no significant differences in compliance or risk-taking behaviours between customised and non-customised robots. However, participants in the customised condition reported a significant increase in perceived ownership. Additionally, reaction times were longer in the customised condition, particularly for the "collect" suggestion. These results indicate that although customisation may not directly affect compliance or risk-taking, it enhances cognitive engagement and personal connection with robots. Regardless of customisation, the presence of a robot significantly influenced risk-taking behaviours, supporting theories of over-trust in robots and the automation bias. These findings highlight the importance of carefully considering ethical design and effective communication strategies when developing socially assistive robots to manage user trust and expectations, particularly in applications where behavioural influence is involved. [ABSTRACT FROM AUTHOR]

Introduction: Assistive robots and human-robot interaction have become integral parts of sports training. However, existing methods often fail to provide real-time and accurate feedback, and they often lack integration of comprehensive multi-modal data. Methods: To address these issues, we propose a groundbreaking and innovative approach: CAM-Vtrans--Cross-Attention Multi-modal Visual Transformer. By leveraging the strengths of state-of-the-art techniques such as Visual Transformers (ViT) andmodels like CLIP, along with cross-attentionmechanisms, CAM-Vtrans harnesses the power of visual and textual information to provide athletes with highly accurate and timely feedback. Through the utilization of multi-modal robot data, CAM-Vtrans offers valuable assistance, enabling athletes to optimize their performance while minimizing potential injury risks. This novel approach represents a significant advancement in the field, offering an innovative solution to overcome the limitations of existing methods and enhance the precision and efficiency of sports training programs. [ABSTRACT FROM AUTHOR]

Natural-language dialog is key for an intuitive human-robot interaction. It can be used not only to express humans' intents but also to communicate instructions for improvement if a robot does not understand a command correctly. It is of great importance to let robots learn from such interaction experiences in an incremental way to allow them to improve their behaviors or avoid mistakes in the future. In this paper, we propose a system to achieve such incremental learning of complex high-level behavior from natural interaction and demonstrate its implementation on a humanoid robot. Our system deploys large language models (LLMs) for high-level orchestration of the robot's behavior based on the idea of enabling the LLM to generate Python statements in an interactive console to invoke both robot perception and action. Human instructions, environment observations, and execution results are fed back to the LLM, thus informing the generation of the next statement. Since an LLM can misunderstand (potentially ambiguous) user instructions, we introduce incremental learning from the interaction, which enables the system to learn from its mistakes. For that purpose, the LLM can call another LLM responsible for code-level improvements in the current interaction based on human feedback. Subsequently, we store the improved interaction in the robot's memory so that it can later be retrieved on semantically similar requests. We integrate the system in the robot cognitive architecture of the humanoid robot ARMAR-6 and evaluate our methods both quantitatively (in simulation) and qualitatively (in simulation and real-world) by demonstrating generalized incrementally learned knowledge. [ABSTRACT FROM AUTHOR]

Simulation-based learning is an integral part of hands-on learning and is often done through role-playing games or patients simulated by professional actors. In this article, we present the use of a humanoid robot as a simulation patient for the presentation of disease symptoms in the setting of medical education. In a study, 12 participants watched both the patient simulation by the robotic patient and the video with the actor patient. We asked participants about their subjective impressions of the robotic patient simulation compared to the video with the human actor patient using a self-developed questionnaire. In addition, we used the Affinity for Technology Interaction Scale. The evaluation of the questionnaire provided insights into whether the robot was able to realistically represent the patient which features still need to be improved, and whether the robot patient simulation was accepted by the participants as a learning method. Sixty-seven percent of the participants indicated that they would use the robot as a training opportunity in addition to the videos with acting patients. The majority of participants indicated that they found it very beneficial to have the robot repeat the case studies at their own pace. [ABSTRACT FROM AUTHOR]

Germany's healthcare sector suffers from a shortage of nursing staff, and robotic solutions are being explored as a means to provide quality care. While many robotic systems have already been established in various medical fields (e.g., surgical robots, logistics robots), there are only a few very specialized robotic applications in the care sector. In this work, a multi-functional robot is applied in a hospital, capable of performing activities in the areas of transport and logistics, interactive assistance, and documentation. The service robot platform HoLLiE was further developed, with a focus on implementing innovative solutions for handling non-rigid objects, motion planning for non-holonomic motions with a wheelchair, accompanying and providing haptic support to patients, optical recognition and control of movement exercises, and automated speech recognition. Furthermore, the potential of a robot platform in a nursing context was evaluated by field tests in two hospitals. The results show that a robot can take over or support certain tasks. However, it was noted that robotic tasks should be carefully selected, as robots are not able to provide empathy and affection that are often required in nursing. The remaining challenges still exist in the implementation and interaction of multi-functional capabilities, ensuring ease of use for a complex robotic system, grasping highly heterogeneous objects, and fulfilling formal and infrastructural requirements in healthcare (e.g., safety, security, and data protection). [ABSTRACT FROM AUTHOR]

The worldwide nursing shortage has led to the exploration of using robotics to support care delivery and reduce nurses' workload. In this observational, mixed-method study, we examined the implementation of a robotic nurse assistant (RNA) in a hospital ward to support vital signs measurements, medication, and item delivery. Human–robot interaction was assessed in four domains: usability, social acceptance, user experience, and its societal impact. Patients in a general medicine ward were recruited to participate in a one-time trial with the RNA and a post-trial 75-question survey. Patients' interactions with the RNA were video recorded for analysis including patients' behaviours, facial emotions, and visual attention. Focus group discussions with nurses elicited their perceptions of working with the RNA, areas for improvement, and scalability. Sixty-seven patients aged 21–79 participated in the trial. Eight in 10 patients reported positive interactions with the RNA. When the RNA did not perform to expectations, only 25% of patients attributed fault to the RNA. Video analysis showed patients at ease interacting with the RNA despite some technical problems. Nurses saw potential for the RNA taking over routine tasks. However, they were sceptical of real time savings and were concerned with the RNA's ability to work well with older patients. Patients and nurses suggested greater interactivity between RNA and patients. Future studies should examine potential timesaving and whether time saved translated to nurses performing higher value clinical tasks. The utility of improved RNA's social capability in a hospital setting should be explored as well. [ABSTRACT FROM AUTHOR]

As humans' additional arms or legs, supernumerary robotic limbs (SRLs) have gained great application prospects in many fields. However, current SRLs lack both rigidity/flexibility adaptability and arm/leg function conversion. Inspired by the muscular hydrostat characteristics of octopus tentacles, fiber-reinforced actuators (FRAs) were employed to develop SRLs simultaneously realizing flexible operation and stable support. In this paper, an SRL with FRAs was designed and implemented. The analytic model of the FRA was established to formulate the movement trajectory and stiffness profile of the SRL. A hierarchical hidden Markov model (HHMM) was proposed to recognize the wearer's motion intention and control the SRL to complete the specific working mode and motion type. Experiments were conducted to exhibit the feasibility and superiority of the proposed robot. [ABSTRACT FROM AUTHOR]

As gestures play an important role in human communication, there have been a number of service robots equipped with a pair of human-like arms for gesture-based human–robot interactions. However, the arms of most human companion robots are limited to slow and simple gestures due to the low maximum velocity of the arm actuators. In this work, we present the JF-2 robot, a mobile home service robot equipped with a pair of torque-controlled anthropomorphic arms. Thanks to the low inertia design of the arm, responsive Quasi-Direct Drive (QDD) actuators, and active compliant control of the joints, the robot can replicate fast human dance motions while being safe in the environment. In addition to the JF-2 robot, we also present the JF-mini robot, a scaled-down, low-cost version of the JF-2 robot mainly targeted for commercial use at kindergarten and childcare facilities. The suggested system is validated by performing three experiments, a safety test, teaching children how to dance along to the music, and bringing a requested item to a human subject. [ABSTRACT FROM AUTHOR]

In the field of rehabilitation engineering, wearable robots for upper limbs tend to help powerless arms of patients smoothly complete daily activities. Compared with traditional rigid exoskeleton, soft exosuit takes the advantage of high movement flexibility and relatively compact structure so that it could be a more adaptive and compliant alternative for the elder and the disabled. In this study, a novel dual-arm exosuit is proposed to provide active assistance for multiple joints through bidirectional cable-driven modules. Several Bowden cables achieve power transmission from actuators mounted on the back to shoulders and elbows. The actuator is designed with reference to the principle of differential mechanism, in order to automatically preload both cables wrapped around it, generate assistive torque in two opposite rotation directions of a joint, and keep the human-robot interaction as safe as possible. This study adopts the constant torque control to assist in the pre-tightening process, and develop a model-based control strategy for bidirectional joint motion enhancement according to mathematical models of the human-robot system. The experimental results demonstrate that the prototype can impose pretension on each cable, and offer enough support on the joint according to the wearers' demands. [ABSTRACT FROM AUTHOR]

The wearable exoskeleton system is a typical strongly coupled human–robotic system. Human–robotic is the environment for each other. The two support each other and compete with each other. Achieving high human–robotic compatibility is the most critical technology for wearable systems. Full structural compatibility can improve the intrinsic safety of the exoskeleton, and precise intention understanding and motion control can improve the comfort of the exoskeleton. This paper first designs a physiologically functional bionic lower limb exoskeleton based on the study of bone and joint functional anatomy and analyzes the drive mapping model of the dual closed-loop four-link knee joint. Secondly, an exoskeleton dual closed-loop controller composed of a position inner loop and a force outer loop is designed. The inner loop of the controller adopts the PID control algorithm, and the outer loop adopts the adaptive admittance control algorithm based on human–robot interaction force (HRI). The controller can adaptively adjust the admittance parameters according to the HRI to respond to dynamic changes in the mechanical and physical parameters of the human–robot system, thereby improving control compliance and the wearing comfort of the exoskeleton system. Finally, we built a joint simulation experiment platform based on SolidWorks/Simulink to conduct virtual prototype simulation experiments and recruited volunteers to wear rehabilitation exoskeletons to conduct related control experiments. Experimental results show that the designed physiologically functional bionic exoskeleton and adaptive admittance controller can significantly improve the accuracy of human–robotic joint motion tracking, effectively reducing human–machine interaction forces and improving the comfort and safety of the wearer. This paper proposes a dual-closed loop four-link knee joint exoskeleton and a variable admittance control method based on HRI, which provides a new method for the design and control of exoskeletons with high compatibility. [ABSTRACT FROM AUTHOR]

In human-robot collaboration (HRC), operators work side by side with collaborative robots (cobots), a new type of machines able to function safely on tasks shared with humans. Despite the increasing presence of cobots in factories, the quality of experience associated by workers with HRC is an underexplored topic. This review is focused on the mental workload (MWL) reported by operators interacting with cobots, its major sources, and the potential solutions to optimize it during HRC. Out of 165 papers identified, 23 were selected as specifically devoted to the exploration of workers' MWL in a HRC activity. Cobot motion, predictability, task organization and communication patterns emerged as the major factors contributing to operators' MWL during HRC. Endowing cobots with the capacity to meet both task demands and human needs through modulation of motion rhythm, flexible physical interaction, and more efficient communication patterns may contribute to mitigating workers' MWL. [ABSTRACT FROM AUTHOR]

Care and nursing training (CNT) refers to developing the ability to effectively respond to patient needs by investigating their requests and improving trainees' care skills in a caring environment. Although conventional CNT programs have been conducted based on videos, books, and role-playing, the best approach is to practice on a real human. However, it is challenging to recruit patients for continuous training, and the patients may experience fatigue or boredom with iterative testing. As an alternative approach, a patient robot that reproduces various human diseases and provides feedback to trainees has been introduced. This study presents a patient robot that can express feelings of pain, similarly to a real human, in joint care education. The two primary objectives of the proposed patient robot-based care training system are (a) to infer the pain felt by the patient robot and intuitively provide the trainee with the patient's pain state, and (b) to provide facial expression-based visual feedback of the patient robot for care training. [ABSTRACT FROM AUTHOR]

Introduction: In human-robot interaction (HRI), understanding human intent is crucial for robots to perform tasks that align with user preferences. Traditional methods that aim to modify robot trajectories based on language corrections often require extensive training to generalize across diverse objects, initial trajectories, and scenarios. This work presents ExTraCT, a modular framework designed to modify robot trajectories (and behaviour) using natural language input. Methods: Unlike traditional end-to-end learning approaches, ExTraCT separates language understanding from trajectory modification, allowing robots to adapt language corrections to new tasks-including those with complex motions like scooping-as well as various initial trajectories and object configurations without additional end-to-end training. ExTraCT leverages Large Language Models (LLMs) to semantically match language corrections to predefined trajectory modification functions, allowing the robot to make necessary adjustments to its path. This modular approach overcomes the limitations of pre-trained datasets and offers versatility across various applications. Results: Comprehensive user studies conducted in simulation and with a physical robot arm demonstrated that ExTraCT s trajectory corrections are more accurate and preferred by users in 80% of cases compared to the baseline. Discussion: ExTraCT offers a more explainable approach to understanding language corrections, which could facilitate learning human preferences. We also demonstrated the adaptability and effectiveness of ExTraCT in a complex scenarios like assistive feeding, presenting it as a versatile solution across various HRI applications. [ABSTRACT FROM AUTHOR]

In the last decades, robotic solutions have been introduced in agriculture to improve the efficiency of tasks such as spraying, plowing, and seeding. However, for a more complex task like soft‐fruit harvesting, the efficiency of experienced human pickers has not been surpassed yet by robotic solutions. Thus, in the immediate future, human labor will probably be still necessary for picking tasks while robotic platforms could be used as collaborators, supporting the pickers in the transportation of the harvested fruit. This cooperative harvesting strategy creates a human–robot interaction (HRI) that requires significant further development in human‐aware safe navigation and effective bidirectional communication of intent. In fact, although agricultural robots are considered small/medium size machinery, they still represent a risk of causing injuries to human collaborators, especially if people are not trained to work with robots or robot operations are not intuitive. Avoiding such injury is the aim of this work which contributes to the development, implementation, and evaluation of a human‐aware navigation (HAN) module that can be integrated into the autonomous navigation system of commercial agricultural robots. The proposed module is responsible for the detection and monitoring of humans working around the robot and uses this information to activate safety actions depending on whether the human presence is considered at risk or not. Apart from ensuring a physically safe HRI, the proposed module deals with the comfort level and psychological safety of human coworkers. The latter is possible by using an explicit human–robot communication strategy that lets both know of the other's intentions, increasing the level of trust and reducing inefficient pauses triggered by unnecessary safety actions. The proposed HAN solution was integrated into a commercial agricultural robot and tested in several situations that are expected to happen during cooperative harvesting operations. The results of an usability assessment illustrated the benefits of the proposal in terms of safety, efficiency, and ergonomics. [ABSTRACT FROM AUTHOR]

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

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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.)

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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.)

This study investigates the effects of humanoid robot appearance, emotional expression, and interaction skills on the uncanny valley phenomenon among university students using the social humanoid robot (SHR) Ameca. Two fundamental studies were conducted within a university setting: Study 1 assessed student expectations of SHRs in a hallway environment, emphasizing the need for robots to integrate seamlessly and engage effectively in social interactions; Study 2 compared the humanlikeness of three humanoid robots, ROMAN, ROBIN, and EMAH (employing the EMAH robotic system implemented on Ameca). The initial findings from corridor interactions highlighted a diverse range of human responses, from engagement and curiosity to indifference and unease. Additionally, the online survey revealed significant insights into expected non-verbal communication skills, continuous learning, and comfort levels during hallway conversations with robots. Notably, certain humanoid robots evoked stronger emotional reactions, hinting at varying degrees of humanlikeness and the influence of interaction quality. The EMAH system was frequently ranked as most humanlike before the study, while post-study perceptions indicated a shift, with EMAH and ROMAN showing significant changes in perceived humanlikeness, suggesting a re-evaluation by participants influenced by their interactive experiences. This research advances our understanding of the uncanny valley phenomenon and the role of humanoid design in enhancing human–robot interaction, marking the first direct comparison between the most advanced, humanlike research robots. [ABSTRACT FROM AUTHOR]

In recent years, many countries, including Japan, have rapidly aging populations, making the preservation of seniors' quality of life a significant concern. For elderly people with impaired physical abilities, support for toileting is one of the most important issues. This paper details the design, development, experimental assessment, and potential application of the gripper system, with a focus on the unique requirements and obstacles involved in aiding elderly or hemiplegic individuals in dressing and undressing trousers. The gripper we propose seeks to find the right balance between compliance and grasping forces, ensuring precise manipulation while maintaining a safe and compliant interaction with the users. The gripper's integration into a custom-built robotic manipulator system provides a comprehensive solution for assisting hemiplegic individuals in their dressing and undressing tasks. Experimental evaluations and comparisons with existing studies demonstrate the gripper's ability to successfully assist in both dressing and dressing of trousers in confined spaces with a high success rate. This research contributes to the advancement of assistive robotics, empowering elderly, and physically impaired individuals to maintain their independence and improve their quality of life. [ABSTRACT FROM AUTHOR]

Dressing is a critical component of Activity of Daily Living (ADLs). The development of assistive technology for dressing tasks is urgently needed from the viewpoint of privacy, for example, to prevent the wearer from being seen in the nude. Despite their importance, these technologies are underutilized in garment donning compared to other ADLs. The reasons for the lack of utilization of assistive technology are that the task is complex, as it involves handling individuals with large individual differences in height and symptoms and requires manipulation of flexible clothing. Addressing these variances presents a formidable academic challenge. Our previous study identified periodic patterns in human movements during robot-assisted dressing and noted individual variability in these patterns. Capitalizing on this finding, we propose a novel control strategy for a robot that adapts to individual needs during dressing, employing the 'Assist-As-Needed' (AAN) principle from physical therapy. The proposed control method uses Central Pattern Generators (CPG) that can be synchronously controlled in response to external forces, enabling model-free control without human modeling. We utilize the Rowat-Selverston CPG model, which is recognized for its adaptive response to human motions in human-robot interactions such as with handshaking robots. A simulator of the Rowat-Selverston CPG was created. The output of the CPG was confirmed by inputting the data set obtained in previous studies, and the parameters were adjusted. The CPG output was then applied to the recorded target joint trajectory for the dressing assistance. We prepared a replay of the default trajectory, a trajectory with simple harmonic motion applied, and a trajectory with CPG output applied, and confirmed whether individual adaptation according to the AAN was possible through subject experiments. The experimental results showed that the Rowat-Selverston CPG control method enables individual adaptive dressing assistance according to the AAN principle. This study summarizes these methods and results and contributes to the realization of an individually adaptive system that follows the AAN principle, especially in developing assistive technology. [ABSTRACT FROM AUTHOR]

Non-verbal communication, especially eye contact, plays a crucial role in human interaction. Integrating eye contact capabilities into robotic behavior presents challenges that require comprehensive interdisciplinary collaboration from the fields of vision science and robotics. This study introduces an innovative methodology to improve the user's perception of the tabletop social robot Haru's direct gaze by explaining how eye and head movements can be managed through simulating visual perception processing. By considering the mechanisms of eye and head movements during visual perception processing, the robot's eye and head gaze behaviors are intended to replicate human gaze movements with Vestibulo-Ocular Movement (VOM) during face-to-face interaction, ultimately enhancing the user's perception of the robot's direct gaze. [ABSTRACT FROM AUTHOR]

Dining with people improves our lives and brings a lot of benefits. Nonetheless, many people are compelled to eat by themselves due to the current social situation. We believe that robots can be good meal partners. In this paper, we describe the developed meal partner robots called Mamoru'21 and Mamoru'22. The design of these robots is inherited from a robot for watching over elderly people. Based on previous research that robot's eating behavior expression can improve the mealtime experience, they can perform motions that mimic eating and present food images by monitors. In the first experiment, we confirmed the developed robot could improve the co-eating experience compared to a conventional small humanoid robot, and investigated the keywords for preferable hardware design, such as 'smaller than a child.' In the second experiment, we studied the effects of the robot's moving around during mealtime. As a result, no statistically significant negative effects of robot movement during meals were observed in the developed robot's capability. Though this study has some limitations such as shorter experimentation time compared to regular meals, it contributes some insights and knowledge into the behavior and design of meal partner robots. [ABSTRACT FROM AUTHOR]

This paper presents the results of an experiment that was designed to explore whether users assigned an ethnic identity to the Misty II robot based on the robot's voice accent, place of origin, and given name. To explore this topic a 2 × 3 within subject study was run which consisted of a humanoid robot speaking with a male or female gendered voice and using three different voice accents (Chinese, American, Mexican). Using participants who identified as American, the results indicated that users were able to identify the gender and ethnic identity of the Misty II robot with a high degree of accuracy based on a minimum set of social cues. However, the version of Misty II presenting with an American ethnicity was more accurately identified than a robot presenting with cues signaling a Mexican or Chinese ethnicity. Implications of the results for the design of human-robot interfaces are discussed. [ABSTRACT FROM AUTHOR]

Proprioception is the "sixth sense" that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at low costs in mechanical design and algorithmic computation. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion-tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low material cost with more than one million use cycles for tasks such as sensitive and competitive grasping and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction. [ABSTRACT FROM AUTHOR]

There is an actual trend for humanizing technological artifacts, especially social robots. However, human‐like social robots trigger negative attitudes by threatening human uniqueness as well as humanness. The present paper presents the development of the Belief in Human Nature Uniqueness Scale (BHNUS) to assess the individual tendency to deny social robots the possibility to have human features considered to be the hallmarks of humanness. The validation of the BHNUS was completed along seven studies, with a total of 1044 Portuguese participants. Results showed that BHNUS had good structural qualities (Studies 1 and 2), as well as good convergent and discriminant validities. BHNUS was correlated with negative attitudes towards robots, religiosity, and interest for science fiction (Study 3), attribution of traits of warmth to robots (Study 4), positive and negative emotional appraisal (Study 5), perspective taking (Study 6), and attitudes towards the development of robots with human features (Study 7). The importance of the BHNUS regarding the development of social robots and human–robot interaction is discussed. If you were to insist I was a robot, you might not consider me capable of love in some mystic human sense, but you would not be able to distinguish my reactions from that which you would call love so what difference would it make? Isaac Asimov (1982) in Foundation's Edge, p. 420 [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has recently become a vital technology affecting several sectors, including hospitality. The expanding use of AI robots in functions previously performed by humans raises important issues — which are currently unknown — regarding how AI robotics may affect personnel and guests. This study examined guests' and staff's experiences with service robots in a 4-star southern Dutch hotel. The report offers the hospitality industry AI robot implementation advice. In-depth staff interviews and 4-star hotel guest surveys were completed. Data from customers and staff showed a need for service robots at the front office. This demand highlights the need for service robots at the front desk to serve leisure and business clients and to aid staff. However, both quantitative and qualitative studies imply that a tangible human welcome is still necessary, especially for leisure tourists. This study found that customers and personnel at the front office want service robots, with leisure and business guests preferring something different. These demographics perceive human connections as essential for a good visitor experience, emphasising the complexities of AI in hospitality. [ABSTRACT FROM AUTHOR]

New digital technologies such as social robots are embodied computers with human-like features and conversational capabilities that can socially interact with people. Social robots have been used in education as a learning tool to support second language learning. This essay discusses current research literature that has explored how social robots could be utilized to support second language (L2) international students studying at English-speaking universities. Insights into the potential application and limitations of using social robots to support L2 students outside their home countries to promote their social and academic well-being will also be discussed. Based on a synthesis of current and relevant research gathered from the literature, the affordances of using social robots for L2 students included learning L2 through human-robot interactions, enhanced motivation, and engagement in their learning environments. However, the limitations of the use of this technology included attitudes towards robot-assisted learning (e.g., unfamiliarity with learning with a social robot) and the novelty effect of social robots. Further research is needed to deepen our understanding of not only the role of social robots for supporting language learning, but also how they could aid L2 students in their successful transition to a foreign university, culture, and social context. [ABSTRACT FROM AUTHOR]