The Role of Name, Origin, and Voice Accent in a Robot's Ethnic Identity.

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.

Siamese and triplet network-based pain expression in robotic avatars for care and nursing training.

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.

Development of Dual-Arm Human Companion Robots That Can Dance.

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.

ExTraCT - Explainable trajectory corrections for language-based human-robot interaction using textual feature descriptions.

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.

Human-Robot Interaction and Social Robot: The Emerging Field of Healthcare Robotics and Current and Future Perspectives for Spinal Care.

Recent advances in robotics technology and artificial intelligence (AI) have sparked increased interest in humanoid robots that resemble humans and social robots capable of interacting socially. Alongside this trend, a new field of robot research called human-robot interaction (HRI) is gaining prominence. The aim of this review paper is to introduce the fundamental concepts of HRI and social robots, examine their current applications in the medical field, and discuss the current and future prospects of HRI and social robots in spinal care. HRI is an interdisciplinary field where robotics, AI, social sciences, design, and various disciplines collaborate organically to develop robots that successfully interact with humans as the ultimate goal. While social robots are not yet widely deployed in clinical environments, ongoing HRI research encompasses various areas such as nursing and caregiving support, social and emotional assistance, rehabilitation and cognitive enhancement for the elderly, medical information provision and education, as well as patient monitoring and data collection. Although still in its early stages, research related to spinal care includes studies on robotic support for rehabilitation exercises, assistance in gait training, and questionnaire-based assessments for spinal pain. Future applications of social robots in spinal care will require diverse HRI research efforts and active involvement from spinal specialists.

BEATRIX: An open source humanoid head platform for robotics teaching and research.

This paper introduces BEATRIX, a novel robotic head designed to bridge the gap between theoretical knowledge and practical experience in the field of robotics at universities. The BEATRIX robot comprises a head actuated by a neck-like mechanism with three stepper motors, two cameras and two microphones for acquisition of visual and audio information from the environment. The robot can be connected to any external computer for the design and implementation of algorithms for applications in human-robot interaction. The proposed robotic platform has been used successfully with undergraduate and master students implementing tasks such as face detection and tracking, sound detection and tracking, robot control and graphical user interfaces. This paper includes lists of all the robot components, assembly instructions, and links to all CAD and software files, facilitating replication and further exploration. The robot design and integration of visual and audio sensors enables the development of engaging educational tutorials and robot experiments, enhancing the teaching and learning experience in robotics.

Humanoid patient robot for diagnostic training in medical and psychiatric education.

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.

HoLLiECares - Development of a multi-functional robot for professional care.

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).

Incremental learning of humanoid robot behavior from natural interaction and large language models.

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.

An Assessment of an Inpatient Robotic Nurse Assistant: A Mixed-Method Study.

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.

Telerobotic Intergroup Contact: Acceptance and Preferences in Israel and Palestine.

We explore telerobotics as a novel form of intergroup communication. In this form, remotely operated robots facilitate embodied and situated intergroup contact between groups in conflict over long distances, potentially reducing prejudice and promoting positive social change. Based on previous conceptual frameworks and design hypotheses, we conducted a survey on the acceptance and preferences of the telerobotic medium in Israel and Palestine. We analyzed the responses using a mixed-method approach. The results shed light on differences in attitudes between the groups and design considerations for telerobots when used for intergroup contact. This study serves as a foundation for the implementation of a novel method of technology-enhanced conflict resolution in the field.

Validity and reliability of the Chinese version of human-robot interaction self-efficacy scale in Chinese adults.

BACKGROUND: With the fast-paced advancements of robot technology, human-robot interaction (HRI) has become increasingly popular and complex, and self-efficacy in HRI has received extensive attention. Despite its popularity, this topic remains understudied in China. OBJECTIVE: In order to provide a psychometrically sound instrument in China, this study aimed to translate and validate the Self-Efficacy in Human-Robot Interaction Scale (SE-HRI) in two Chinese adult samples (N1 = 300, N2 = 500). METHODS: The data was analyzed by SPSS 26.0 and Amos 24.0. Item analysis and exploratory factor analysis were conducted using Sample 1 data. Confirmatory factor analysis, criterion-related validity analysis, and reliability analysis were then performed using Sample 2 data. RESULTS: The results revealed that the Chinese SE-HRI scale consisted of 13 items in a two-factor model, suggesting a good model fit. Moreover, general self-efficacy and willingness to accept the use of artificial intelligence (AI) were both positively correlated with self-efficacy in HRI, while negative attitudes toward robots showed an inverse correlation, proving the Chinese SE-HRI scale exhibited excellent criterion-related validity. CONCLUSION: The Chinese SE-HRI scale is a reliable assessment tool for evaluating self-efficacy in HRI in China. The study discussed implications and limitations, and suggested future directions.

Movement-Based Prosthesis Control with Angular Trajectory Is Getting Closer to Natural Arm Coordination.

Traditional myoelectric controls of trans-humeral prostheses fail to provide intuitive coordination of the necessary degrees of freedom. We previously showed that by using artificial neural network predictions to reconstruct distal joints, based on the shoulder posture and movement goals (i.e., position and orientation of the targeted object), participants were able to position and orient an avatar hand to grasp objects with natural arm performances. However, this control involved rapid and unintended prosthesis movements at each modification of the movement goal, impractical for real-life scenarios. Here, we eliminate this abrupt change using novel methods based on an angular trajectory, determined from the speed of stump movement and the gap between the current and the 'goal' distal configurations. These new controls are tested offline and online (i.e., involving participants-in-the-loop) and compared to performances obtained with a natural control. Despite a slight increase in movement time, the new controls allowed twelve valid participants and six participants with trans-humeral limb loss to reach objects at various positions and orientations without prior training. Furthermore, no usability or workload degradation was perceived by participants with upper limb disabilities. The good performances achieved highlight the potential acceptability and effectiveness of those controls for our target population.

Human-Robot Intimacy: Acceptance of Robots as Intimate Companions.

Depictions of robots as romantic partners for humans are frequent in popular culture. As robots become part of human society, they will gradually assume the role of partners for humans whenever necessary, as assistants, collaborators, or companions. Companion robots are supposed to provide social contact to those who would not have it otherwise. These companion robots are usually not designed to fulfill one of the most important human needs: the one for romantic and intimate contact. Human-robot intimacy remains a vastly unexplored territory. In this article, we review the state-of-the-art research in intimate robotics. We discuss major issues limiting the acceptance of robots as intimate partners, the public perception of robots in intimate roles, and the possible influence of cross-cultural differences in these domains. We also discuss the possible negative effects human-robot intimacy may have on human-human contact. Most importantly, we propose a new term "intimate companion robots" to reduce the negative connotations of the other terms that have been used so far and improve the social perception of research in this domain. With this article, we provide an outlook on prospects for the development of intimate companion robots, considering the specific context of their use.

Cocreative Development of Robotic Interaction Systems for Health Care: Scoping Review.

Background: Robotic technologies present challenges to health care professionals and are therefore rarely used. Barriers such as lack of controllability and adaptability and complex control functions affect the human-robot relationship. In addition to educational opportunities, the possibility of individual adaptation can improve the usability and practical implementation of robotics. Previous work has focused on developments from a technology-centered perspective and has included user interests too late in the process. Objective: This study addresses the following research question: What cocreative research approaches are used in the field of nursing robotics to improve the usability, intended use, and goal-directed application of robotic developments for nurses and to support the nursing process? Methods: This scoping review provides an overview of the topic and the research activities taking place within it. Five databases and the reference lists of the identified publications were searched for studies without further restrictions. Studies were included if they developed and evaluated interaction and control platforms for robotic systems in health care in a cocreative way with end users. Results: The search resulted in 419 hits, of which 3 publications were included. All publications were feasibility or user studies that were mainly carried out in the European Union. The 3 interaction and control platforms presented were all prototypes and not commercially available. In addition to those in need of care, all studies also included family carers and health care professionals. Conclusions: Robotic interaction and control platforms in health care are rarely, if ever, developed and evaluated with feasibility or user studies that include prototypes and end users. While the involvement of end users is crucial, this review emphasizes that all stakeholders, including health care professionals, should participate in the development process to ensure a holistic understanding of application needs and a focus on user experiences and practical health care needs. It is emphasized that the active involvement of end users in the development process is critical to effectively meeting the needs of the target group.

Overtrust in AI Recommendations About Whether or Not to Kill: Evidence from Two Human-Robot Interaction Studies.

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 agent's intelligence. The overall findings indicate a strong propensity to overtrust unreliable AI in life-or-death decisions made under uncertainty.

Operational space robust impedance control of the redundant surgical robot for minimally invasive surgery.

The motion accuracy, compliance, and control smoothness for the surgical robot are of great importance to improve the safety of human-robot interaction. However, the end effector that interacts with soft tissue during surgery affects the dynamics of the robot. The control performance of the controller may be decreased if the changing dynamics are not identified and updated in time. This paper proposes a robust impedance controller for the redundant remote center of motion manipulator influenced by external disturbances, including external torque, uncertainties, and unmodeled terms in the dynamics. To achieve the desired impedance, a continuously switching sliding manifold is proposed. When the sliding manifold is driven to zero, the motion error will converge to a bounded region. This can overcome the adverse effects of external disturbances while guaranteeing motion accuracy and compliance. Chattering of the sliding mode control is alleviated through the formulated continuously switching sliding manifold and integrated nonlinear disturbance observer. Simulations and experiments demonstrate that the proposed controller has excellent motion accuracy, compliance, and control smoothness. This provides potential application prospects for the redundant surgical robot to guarantee safe human-robot interaction.

Research on shared control of robots based on hybrid brain-computer interface.

BACKGROUND: With the arrival of the new generation of artificial intelligence wave, new human-robot interaction technologies continue to emerge. Brain-computer interface (BCI) offers a pathway for state monitoring and interaction control between human and robot. However, the unstable mental state reduce the accuracy of human brain intent decoding, and consequently affects the precision of BCI control. NEW METHODS: This paper proposes a hybrid BCI-based shared control (HB-SC) method for brain-controlled robot navigation. Hybrid BCI fuses electroencephalogram (EEG) and electromyography (EMG) for mental state monitoring and interactive control to output human perception and decision. The shared control based on multi-sensory fusion integrates the special obstacle information perceived by humans with the regular environmental information perceived by the robot. In this process, valid BCI commands are screened by mental state assessment and output to a layered costmap for fusion. RESULTS: Eight subjects participated in the navigation experiment with dynamically changing mental state levels to validate the effects of a hybrid brain-computer interface through two shared control modes. The results show that the proposed HB-SC reduces collisions by 37.50 %, improves the success rate of traversing obstacles by 25.00 %, and the navigation trajectory is more consistent with expectations. CONCLUSIONS: The HB-SC method can dynamically and intelligently adjust command output according to different brain states, helping to reduce errors made by subjects in a unstable mental state, thereby greatly enhancing the system's safety.

Workplace Well-Being in Industry 5.0: A Worker-Centered Systematic Review.

The paradigm of Industry 5.0 pushes the transition from the traditional to a novel, smart, digital, and connected industry, where well-being is key to enhance productivity, optimize man-machine interaction and guarantee workers' safety. This work aims to conduct a systematic review of current methodologies for monitoring and analyzing physical and cognitive ergonomics. Three research questions are addressed: (1) which technologies are used to assess the physical and cognitive well-being of workers in the workplace, (2) how the acquired data are processed, and (3) what purpose this well-being is evaluated for. This way, individual factors within the holistic assessment of worker well-being are highlighted, and information is provided synthetically. The analysis was conducted following the PRISMA 2020 statement guidelines. From the sixty-five articles collected, the most adopted (1) technological solutions, (2) parameters, and (3) data analysis and processing were identified. Wearable inertial measurement units and RGB-D cameras are the most prevalent devices used for physical monitoring; in the cognitive ergonomics, and cardiac activity is the most adopted physiological parameter. Furthermore, insights on practical issues and future developments are provided. Future research should focus on developing multi-modal systems that combine these aspects with particular emphasis on their practical application in real industrial settings.

Robotic navigation with deep reinforcement learning in transthoracic echocardiography.

PURPOSE: The search for heart components in robotic transthoracic echocardiography is a time-consuming process. This paper proposes an optimized robotic navigation system for heart components using deep reinforcement learning to achieve an efficient and effective search technique for heart components. METHOD: The proposed method introduces (i) an optimized search behavior generation algorithm that avoids multiple local solutions and searches for the optimal solution and (ii) an optimized path generation algorithm that minimizes the search path, thereby realizing short search times. RESULTS: The mitral valve search with the proposed method reaches the optimal solution with a probability of 74.4%, the mitral valve confidence loss rate when the local solution stops is 16.3% on average, and the inspection time with the generated path is 48.6 s on average, which is 56.6% of the time cost of the conventional method. CONCLUSION: The results indicate that the proposed method improves the search efficiency, and the optimal location can be searched in many cases with the proposed method, and the loss rate of the confidence in the mitral valve was low even when a local solution rather than the optimal solution was reached. It is suggested that the proposed method enables accurate and quick robotic navigation to find heart components.