Bridging Requirements, Planning, and Evaluation: A Review of Social Robot Navigation.

Navigation lies at the core of social robotics, enabling robots to navigate and interact seamlessly in human environments. The primary focus of human-aware robot navigation is minimizing discomfort among surrounding humans. Our review explores user studies, examining factors that cause human discomfort, to perform the grounding of social robot navigation requirements and to form a taxonomy of elementary necessities that should be implemented by comprehensive algorithms. This survey also discusses human-aware navigation from an algorithmic perspective, reviewing the perception and motion planning methods integral to social navigation. Additionally, the review investigates different types of studies and tools facilitating the evaluation of social robot navigation approaches, namely datasets, simulators, and benchmarks. Our survey also identifies the main challenges of human-aware navigation, highlighting the essential future work perspectives. This work stands out from other review papers, as it not only investigates the variety of methods for implementing human awareness in robot control systems but also classifies the approaches according to the grounded requirements regarded in their objectives.

When Trustworthiness Meets Face: Facial Design for Social Robots.

As a technical application in artificial intelligence, a social robot is one of the branches of robotic studies that emphasizes socially communicating and interacting with human beings. Although both robot and behavior research have realized the significance of social robot design for its market success and related emotional benefit to users, the specific design of the eye and mouth shape of a social robot in eliciting trustworthiness has received only limited attention. In order to address this research gap, our study conducted a 2 (eye shape) × 3 (mouth shape) full factorial between-subject experiment. A total of 211 participants were recruited and randomly assigned to the six scenarios in the study. After exposure to the stimuli, perceived trustworthiness and robot attitude were measured accordingly. The results showed that round eyes (vs. narrow eyes) and an upturned-shape mouth or neutral mouth (vs. downturned-shape mouth) for social robots could significantly improve people's trustworthiness and attitude towards social robots. The effect of eye and mouth shape on robot attitude are all mediated by the perceived trustworthiness. Trustworthy human facial features could be applied to the robot's face, eliciting a similar trustworthiness perception and attitude. In addition to empirical contributions to HRI, this finding could shed light on the design practice for a trustworthy-looking social robot.

[New Technology, Warm Care: Social Robots in Nursing Practice and Research].

Recent, rapid advancements in technology have enabled the introduction and integration of robots into everyday life. Different from the traditional image of robots as cold and mechanical, social robots are designed to emulate human interaction patterns, improving the user experience and facilitating social interactivity. Thus, social robots represent a promising new care intervention. In this article, after defining social robots and explaining the factors influencing "human-robot interaction", the authors discuss the effectiveness of social robots in the context of providing care to patients with dementia and autism as well as to pediatric patients. Finally, current cases in which PARO, a social robot, has been used in nursing are described, and key challenges and suggestions for future social robot applications are given. Current evidence indicates social robots must be developed and designed to adhere to a people-centered approach to achieve better robot-assisted care outcomes, be better accepted by patients, and better enable patients to open up emotionally and maintain good physical, mental, and social well-being.

Revisiting the video deficit in technology-saturated environments: Successful imitation from people, screens, and social robots.

The "video deficit" is a well-documented effect whereby children learn less well about information delivered via a screen than the same information delivered in person. Research suggests that increasing social contingency may ameliorate this video deficit. The current study instantiated social contingency to screen-based information by embodying the screen within a socially interactive robot presented to urban Australian children with frequent exposure to screen-based communication. We failed to document differences between 22- to 26-month-old children's (N = 80) imitation of screen-based information embedded in a social robot and in-person humans. Furthermore, we did not replicate the video deficit with children imitating at similar levels regardless of the presentation medium. This failure to replicate supports the findings of a recent meta-analysis of video deficit research whereby there appears to be a steady decrease over time in the magnitude of the video deficit effect. We postulate that, should the video deficit effect be truly dwindling in effect size, the video deficit may soon be a historical artifact as children begin perceiving technology as relevant and meaningful in everyday life more and more. This research finds that observational-based learning material can be successfully delivered in person, via a screen, or via a screen embedded in a social robot.

Research Hotspots and Trends of Social Robot Interaction Design: A Bibliometric Analysis.

(1) Background: Social robot interaction design is crucial for determining user acceptance and experience. However, few studies have systematically discussed the current focus and future research directions of social robot interaction design from a bibliometric perspective. Therefore, we conducted this study in order to identify the latest research progress and evolution trajectory of research hotspots in social robot interaction design over the last decade. (2) Methods: We conducted a comprehensive review based on 2416 papers related to social robot interaction design obtained from the Web of Science (WOS) database. Our review utilized bibliometric techniques and integrated VOSviewer and CiteSpace to construct a knowledge map. (3) Conclusions: The current research hotspots of social robot interaction design mainly focus on #1 the study of human-robot relationships in social robots, #2 research on the emotional design of social robots, #3 research on social robots for children's psychotherapy, #4 research on companion robots for elderly rehabilitation, and #5 research on educational social robots. The reference co-citation analysis identifies the classic literature that forms the basis of the current research, which provides theoretical guidance and methods for the current research. Finally, we discuss several future research directions and challenges in this field.

The Godspeed Questionnaire Series in the Assessment of the Social Robot TIAGo by Older Individuals.

(1) Background: A robot in care for older adults requires solid research confirming its acceptance. The aim of this study was to present the Polish version of the Godspeed Questionnaire Series (GQS) and assess the perception of the social robot TIAGo; (2) Methods: The study involved older individuals living in the community and care homes and measured perception after interaction with TIAGo using five series of GQS (S1: Anthropomorphism, S2: Animacy, S3: Likeability, S4: Perceived intelligence, and S5: Perceived safety); (3) Results: We studied 178 individuals (age: 75.2 ± 9.6 years, 103 women). Good internal consistency was found. Cronbach's Alpha was 0.90 for the entire tool (from 0.75 to 0.94 for the individual series). Mean scores for S1 and S2 were comparable but lower than all others (p < 0.001). Average scores for S3 and S4 did not differ but were higher than those of S5. Age, gender and education did not impact the answers, as did the ease of use of technology and self-assessment of independence. Solely, the place of residence influenced the results of S3 and S5; people living in institutions scored higher (p < 0.05 and p < 0.001, respectively); (4) Conclusions: Acceptance does not go hand in hand with the perception of anthropomorphism and animacy.

Acceptability of Social Robots and Adaptation of Hybrid-Face Robot for Dementia Care in India: A Qualitative Study.

OBJECTIVES: This study aims to understand the acceptability of social robots and the adaptation of the Hybrid-Face Robot for dementia care in India. METHODS: We conducted a focus group discussion and in-depth interviews with persons with dementia (PwD), their caregivers, professionals in the field of dementia, and technical experts in robotics to collect qualitative data. RESULTS: This study explored the following themes: Acceptability of Robots in Dementia Care in India, Adaptation of Hybrid-Face Robot and Future of Robots in Dementia Care. Caregivers and PwD were open to the idea of social robot use in dementia care; caregivers perceived it to help with the challenges of caregiving and positively viewed a future with robots. DISCUSSION: This study is the first of its kind to explore the use of social robots in dementia care in India by highlighting user needs and requirements that determine acceptability and guiding adaptation.

The pilot study of group robot intervention on pediatric inpatients and their caregivers, using 'new aibo'.

The study on robot-assisted therapy in a pediatric field has not been applied sufficiently in clinical settings. The purpose of this pilot study is to explore the potential therapeutic effects of a group robot intervention (GRI), using dog-like social robot (SR) 'aibo' in pediatric ward. GRI by aibo was conducted for those children with chronic illness (127 in total) who are hospitalized in National Centre for Child Health and Development (NCCHD), and their caregivers (116 in total), from March to April 2018. The observer made structured behavioural observation records, based on which qualitative research on the features of their words and conducts, were carried out. As a result, first, during the GRI, about 2/3 of total expression by children were positive, while about 1/4 were negative or inappropriate. On the other hand, as seen in the 'change' group, those children who had originally responded with negative expression eventually came to express positive expression, while getting involved in a ternary relationship or participating in a session more than once. Secondly, as for the expression from the caregivers during the GRI, active expressions such as 'participation' and 'exploration' accounted for the 2/3, while 1/3 turned out to be rather placid expressions such as 'watch over' or 'encourage.'Conclusion: There has not been any precedent study on the features of words and conducts expressed by patients and their caregivers during the GRI by aibo. The outcome suggests that aibo could possibly be used as a tool for group robot-assisted therapy in the pediatric treatment setting. What is Known: • The study on robot-assisted therapy in a pediatric field has only just begun. • Though many kinds of social robot have been reportedly used so far, none has yet to be applied in clinical settings What is New: • Our study revealed the features of words and behaviour expressed by the patients and their caregivers, when dog-like social robot 'aibo' was used for a group robot intervention in the pediatric ward. • The outcome suggests that aibo could possibly be used as a tool for group robot-assisted therapy in the pediatric treatment setting.

Feasibility study protocol of the PainChek app to assess the efficacy of a social robot intervention for people with dementia.

AIM: This study aims to test the feasibility of the PainChek app to assess pain for people with dementia living in residential aged care facilities (RACFs). It will also identify the optimal dosage and efficacy of a social robot (personal assistant robot [PARO]) intervention on chronic pain for people with dementia. DESIGN: This is a feasibility randomized controlled trial with three groups. METHODS: Forty-five residents living with dementia and chronic pain will be recruited from one RACF. The intervention consists of an individual 15-min non-facilitated session with a PARO robot twice a day (Group 1), a PARO robot once a day (Group 2), or a Plush-Toy (non-robotic PARO) once a day (Group 3) from Monday to Friday for 4 weeks. Participants will be followed at 4 and 8 weeks after baseline assessments. The primary outcome will be the feasibility of using the PainChek app to measure changes in pain levels before and after each session. Secondary outcomes include staff-rated pain levels, neuropsychiatric symptoms, quality of life and changes in psychotropic and analgesic medication use. Participants, staff and family perceptions of using PARO and the PainChek app will be collected after the 4-week intervention. DISCUSSION: This study will test the use of the PainChek app and PARO to improve pain management for people with dementia. Results from this study will help determine its usefulness, feasibility and acceptability for pain management in people with dementia living in RACFs. IMPACT: As pain is a significant problem for people with dementia, this project will generate evidence on the use of the PainChek to measure the efficacy of a social robot intervention that has the potential to improve the quality of pain care in people with dementia. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry number (ACTRN12621000837820) date registered 30/06/2021.

Robots in Eldercare: How Does a Real-World Interaction with the Machine Influence the Perceptions of Older People?

(1) Background: Using autonomous social robots in selected areas of care for community-dwelling older adults is one of the promising approaches to address the problem of the widening care gap. We posed the question of whether a possibility to interact with the technology to be used had an impact on the scores given by the respondents in various domains of needs and requirements for social robots to be deployed in care for older individuals. (2) Methods: During the study, the opinions of older people (65+; n = 113; with no severe cognitive impairment) living in six social care institutions about a robot in care for older people were collected twice using the Users' Needs, Requirements and Abilities Questionnaire (UNRAQ): after seeing a photo of the robot only and after a 90−150 min interaction with the TIAGo robot. (3) Results: Mean total scores for both assistive and social functions were higher after the interaction (p < 0.05). A positive correlation was found between opinion changes in social and assistive functions (r = 0.4842; p = 0.0000). (4) Conclusions: Preimplementation studies and assessments should include the possibility to interact with the robot to provide its future users with a clear idea of the technology and facilitate necessary customisations of the machine.

Behavioral Data Analysis of Robot-Assisted Autism Spectrum Disorder (ASD) Interventions Based on Lattice Computing Techniques.

Recent years have witnessed the proliferation of social robots in various domains including special education. However, specialized tools to assess their effect on human behavior, as well as to holistically design social robot applications, are often missing. In response, this work presents novel tools for analysis of human behavior data regarding robot-assisted special education. The objectives include, first, an understanding of human behavior in response to an array of robot actions and, second, an improved intervention design based on suitable mathematical instruments. To achieve these objectives, Lattice Computing (LC) models in conjunction with machine learning techniques have been employed to construct a representation of a child's behavioral state. Using data collected during real-world robot-assisted interventions with children diagnosed with Autism Spectrum Disorder (ASD) and the aforementioned behavioral state representation, time series of behavioral states were constructed. The paper then investigates the causal relationship between specific robot actions and the observed child behavioral states in order to determine how the different interaction modalities of the social robot affected the child's behavior.

Understanding Emotions in Children with Developmental Disabilities during Robot Therapy Using EDA.

Recent technological advancements have led to the emergence of supportive robotics to help children with developmental disabilities become independent. In conventional research, in robot therapy, experiments are often conducted by operating the robot out of the subject's sight. In this paper, robot therapy using a system that can autonomously recognize the emotions of a child with developmental disabilities and provide feedback was developed. The aim was to quantitatively infer emotional changes in children using skin conductance (EDA) during robot therapy. It was demonstrated that the robot could recognize emotions autonomously and provide feedback to the subjects. Additionally, a quantitative evaluation was conducted using EDA. By analyzing the symptoms related to developmental disorders, it may be possible to improve the recognition rate and tailor therapy based on symptoms.

Gender Identification in a Two-Level Hierarchical Speech Emotion Recognition System for an Italian Social Robot.

The real challenge in Human-Robot Interaction (HRI) is to build machines capable of perceiving human emotions so that robots can interact with humans in a proper manner. Emotion varies accordingly to many factors, and gender represents one of the most influential ones: an appropriate gender-dependent emotion recognition system is recommended indeed. In this article, we propose a Gender Recognition (GR) module for the gender identification of the speaker, as a preliminary step for the final development of a Speech Emotion Recognition (SER) system. The system was designed to be installed on social robots for hospitalized and living at home patients monitoring. Hence, the importance of reducing the software computational effort of the architecture also minimizing the hardware bulkiness, in order for the system to be suitable for social robots. The algorithm was executed on the Raspberry Pi hardware. For the training, the Italian emotional database EMOVO was used. Results show a GR accuracy value of 97.8%, comparable with the ones found in the literature.

Dietary Nutritional Information Autonomous Perception Method Based on Machine Vision in Smart Homes.

In order to automatically perceive the user's dietary nutritional information in the smart home environment, this paper proposes a dietary nutritional information autonomous perception method based on machine vision in smart homes. Firstly, we proposed a food-recognition algorithm based on YOLOv5 to monitor the user's dietary intake using the social robot. Secondly, in order to obtain the nutritional composition of the user's dietary intake, we calibrated the weight of food ingredients and designed the method for the calculation of food nutritional composition; then, we proposed a dietary nutritional information autonomous perception method based on machine vision (DNPM) that supports the quantitative analysis of nutritional composition. Finally, the proposed algorithm was tested on the self-expanded dataset CFNet-34 based on the Chinese food dataset ChineseFoodNet. The test results show that the average recognition accuracy of the food-recognition algorithm based on YOLOv5 is 89.7%, showing good accuracy and robustness. According to the performance test results of the dietary nutritional information autonomous perception system in smart homes, the average nutritional composition perception accuracy of the system was 90.1%, the response time was less than 6 ms, and the speed was higher than 18 fps, showing excellent robustness and nutritional composition perception performance.

Digital media in intergenerational communication: Status quo and future scenarios for the grandparent-grandchild relationship.

Communication technologies play an important role in maintaining the grandparent-grandchild (GP-GC) relationship. Based on Media Richness Theory, this study investigates the frequency of use (RQ1) and perceived quality (RQ2) of established media as well as the potential use of selected innovative media (RQ3) in GP-GC relationships with a particular focus on digital media. A cross-sectional online survey and vignette experiment were conducted in February 2021 among N = 286 university students in Germany (mean age 23 years, 57% female) who reported on the direct and mediated communication with their grandparents. In addition to face-to-face interactions, non-digital and digital established media (such as telephone, texting, video conferencing) and innovative digital media, namely augmented reality (AR)-based and social robot-based communication technologies, were covered. Face-to-face and phone communication occurred most frequently in GP-GC relationships: 85% of participants reported them taking place at least a few times per year (RQ1). Non-digital established media were associated with higher perceived communication quality than digital established media (RQ2). Innovative digital media received less favorable quality evaluations than established media. Participants expressed doubts regarding the technology competence of their grandparents, but still met innovative media with high expectations regarding improved communication quality (RQ3). Richer media, such as video conferencing or AR, do not automatically lead to better perceived communication quality, while leaner media, such as letters or text messages, can provide rich communication experiences. More research is needed to fully understand and systematically improve the utility, usability, and joy of use of different digital communication technologies employed in GP-GC relationships.

Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model.

Social robot navigation in public spaces, buildings or private houses is a difficult problem that is not well solved due to environmental constraints (buildings, static objects etc.), pedestrians and other mobile vehicles. Moreover, robots have to move in a human-aware manner-that is, robots have to navigate in such a way that people feel safe and comfortable. In this work, we present two navigation tasks, social robot navigation and robot accompaniment, which combine machine learning techniques with the Social Force Model (SFM) allowing human-aware social navigation. The robots in both approaches use data from different sensors to capture the environment knowledge as well as information from pedestrian motion. The two navigation tasks make use of the SFM, which is a general framework in which human motion behaviors can be expressed through a set of functions depending on the pedestrians' relative and absolute positions and velocities. Additionally, in both social navigation tasks, the robot's motion behavior is learned using machine learning techniques: in the first case using supervised deep learning techniques and, in the second case, using Reinforcement Learning (RL). The machine learning techniques are combined with the SFM to create navigation models that behave in a social manner when the robot is navigating in an environment with pedestrians or accompanying a person. The validation of the systems was performed with a large set of simulations and real-life experiments with a new humanoid robot denominated IVO and with an aerial robot. The experiments show that the combination of SFM and machine learning can solve human-aware robot navigation in complex dynamic environments.

Feeling of Safety and Comfort towards a Socially Assistive Unmanned Aerial Vehicle That Monitors People in a Virtual Home.

Unmanned aerial vehicles (UAVs) represent a new model of social robots for home care of dependent persons. In this regard, this article introduces a study on people's feeling of safety and comfort while watching the monitoring trajectory of a quadrotor dedicated to determining their condition. Three main parameters are evaluated: the relative monitoring altitude, the monitoring velocity and the shape of the monitoring path around the person (ellipsoidal or circular). For this purpose, a new trajectory generator based on a state machine, which is successfully implemented and simulated in MATLAB/Simulink®, is described. The study is carried out with 37 participants using a virtual reality (VR) platform based on two modules, UAV simulator and VR Visualiser, both communicating through the MQTT protocol. The participants' preferences have been a high relative monitoring altitude, a high monitoring velocity and a circular path. These choices are a starting point for the design of trustworthy socially assistive UAVs flying in real homes.

A social robot intervention on depression, loneliness, and quality of life for Taiwanese older adults in long-term care.

OBJECTIVES: To investigate the effect of a social robot intervention on depression, loneliness, and quality of life of older adults in long-term care (LTC) and to explore participants' experiences and perceptions after the intervention. DESIGN: A mixed-methods approach consisting of a single group, before and after quasi-experimental design, and individual interview. PARTICIPANTS: Twenty older adults with depression from four LTC facilities in Taiwan were recruited. INTERVENTION: Each participant participated in 8 weeks of observation and 8 weeks of intervention. In the observation stage, participants received usual care or activities without any research intervention. In the intervention stage, each participant was given a Paro (Personal Assistive RobOt) to keep for 24 hours, 7 days a week. MEASUREMENTS: The Geriatric Depression Scale, the UCLA Loneliness Scale Version 3, and the World Health Organization Quality of Life Questionnaire for older adults were administered at four time points. Individual qualitative interviews with thematic analysis followed. RESULTS: A repeated multivariate analysis of variance and Friedman's test showed no significant changes during the observation stage between T1 and T2 for depression and quality of life (p >.5). For the intervention stage, statistically significant changes in decreasing depression and loneliness and improving quality of life over time were identified. Three themes emerged from the interviews: (i) humanizing Paro through referring to personal experiences and engagement; (ii) increased social interaction with other people; and (iii) companionship resulting in improved mental well-being. CONCLUSIONS: There were significant improvements in mental well-being in using Paro. Further research may help us to understand the advantages of using a Paro intervention as depression therapy.

Quality of Care Perceived by Older Patients and Caregivers in Integrated Care Pathways With Interviewing Assistance From a Social Robot: Noninferiority Randomized Controlled Trial.

BACKGROUND: Society is facing a global shortage of 17 million health care workers, along with increasing health care demands from a growing number of older adults. Social robots are being considered as solutions to part of this problem. OBJECTIVE: Our objective is to evaluate the quality of care perceived by patients and caregivers for an integrated care pathway in an outpatient clinic using a social robot for patient-reported outcome measure (PROM) interviews versus the currently used professional interviews. METHODS: A multicenter, two-parallel-group, nonblinded, randomized controlled trial was used to test for noninferiority of the quality of care delivered through robot-assisted care. The randomization was performed using a computer-generated table. The setting consisted of two outpatient clinics, and the study took place from July to December 2019. Of 419 patients who visited the participating outpatient clinics, 110 older patients met the criteria for recruitment. Inclusion criteria were the ability to speak and read Dutch and being assisted by a participating health care professional. Exclusion criteria were serious hearing or vision problems, serious cognitive problems, and paranoia or similar psychiatric problems. The intervention consisted of a social robot conducting a 36-item PROM. As the main outcome measure, the customized Consumer Quality Index (CQI) was used, as reported by patients and caregivers for the outpatient pathway of care. RESULTS: In total, 75 intermediately frail older patients were included in the study, randomly assigned to the intervention and control groups, and processed: 36 female (48%) and 39 male (52%); mean age 77.4 years (SD 7.3), range 60-91 years. There was no significant difference in the total patient CQI scores between the patients included in the robot-assisted care pathway (mean 9.27, SD 0.65, n=37) and those in the control group (mean 9.00, SD 0.70, n=38): P=.08, 95% CI -0.04 to 0.58. There was no significant difference in the total CQI scores between caregivers in the intervention group (mean 9.21, SD 0.76, n=30) and those in the control group (mean 9.09, SD 0.60, n=35): P=.47, 95% CI -0.21 to 0.46. No harm or unintended effects occurred. CONCLUSIONS: Geriatric patients and their informal caregivers valued robot-assisted and nonrobot-assisted care pathways equally. TRIAL REGISTRATION: ClinicalTrials.gov NCT03857789; https://clinicaltrials.gov/ct2/show/NCT03857789.

A Framework for User Adaptation and Profiling for Social Robotics in Rehabilitation.

Physical rehabilitation therapies for children present a challenge, and its success-the improvement of the patient's condition-depends on many factors, such as the patient's attitude and motivation, the correct execution of the exercises prescribed by the specialist or his progressive recovery during the therapy. With the aim to increase the benefits of these therapies, social humanoid robots with a friendly aspect represent a promising tool not only to boost the interaction with the pediatric patient, but also to assist physicians in their work. To achieve both goals, it is essential to monitor in detail the patient's condition, trying to generate user profile models which enhance the feedback with both the system and the specialist. This paper describes how the project NAOTherapist-a robotic architecture for rehabilitation with social robots-has been upgraded in order to include a monitoring system able to generate user profile models through the interaction with the patient, performing user-adapted therapies. Furthermore, the system has been improved by integrating a machine learning algorithm which recognizes the pose adopted by the patient and by adding a clinical reports generation system based on the QUEST metric.