Robotic moment: The influence mechanism of frequency of socializing on willingness to interact with ai robots

Past research has discovered that the shape design and interaction process design of AI robots, as well as the users' constant features, are the major factors that affect users' willingness to interact with AI robots. Currently, AI robots that play a vital part in the daily activities of our society are becoming increasingly prevalent, thus things about AI robots have gone from mythic to prosaic. But when and where people are more likely to adopt AI robots remains an important research topic. With the development of online technology and the long-term impact of COVID-19, there has been a recent trend in the lower frequency of socializing. To investigate whether a state of low socializing frequency is a robotic moment and whether it affects people's willingness to interact with AI robots, we conducted two-wave questionnaire surveys to collect data from 300 participants from 23 provinces in China. The results showed that the frequency of socializing had a significant negative correlation with the willingness to interact with the AI robots via the mediation role of social compensation. Furthermore, the relationship between social compensation and willingness to interact with the AI robots was demonstrated to be stronger, when participants had a lower anthropomorphic tendency. These findings have theoretical implications for the human-computer interaction literature and managerial implications for the robotics industry. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Comparative Analysis of Different Systematic Odometry Error Correction Methods on a Differential Drive Indoor Service Robot

The COVID-19 pandemic has led to a surge of interest in research work involving the development of robotic systems that reduce human-to-human interaction, as such a technology can greatly benefit healthcare industries in preventing the spread of highly infectious diseases. An indoor service robot is built and equipped with wheel odometry and a 2D LiDAR. However, the presence of the systematic odometry errors is evident during field testing. Hence, the possibility of minimizing systematic odometry errors is inspected using various methods of calculation, namely: UMBmark, Lee's and Jung's. The methods all use the Bidirectional Square Path test, performed together with ROS. It is found that Jung's method is the most appropriate method showing a 20.4% improvement compared to the uncalibrated dead reckoning accuracy. Moreover, it is found that the presence of slippage, a nonsystematic error, greatly affects the return position errors of the robot. Consequently, it is recommended to improve the design of the wheelbase to minimize the effects of nonsystematic errors. © 2022 IEEE.

Characterization and Optimization of Trajectory Errors on Remote-Controlled Differential Drive Robot through Speed Calibration

An increase in interest in research projects which involves the design of robotic systems that minimizes interactions between humans has been caused by the COVID-19 outbreak, as such technology can greatly benefit healthcare industries in preventing the spread of highly infectious diseases. The utilization of remote-controlled robots in many different fields, especially in the medical field is becoming more and more necessary. However, mobile robots are susceptible to both systematic and nonsystematic errors that cause deviations in its trajectory. In view thereof, the researchers explored the possibility of minimizing the trajectory errors through speed calibration. The differential drive robot was navigated to finish a five-meter linear path of forward and backward motion. The test was conducted with a default linear speed of 0.5 m/s in which a high trajectory error was observed. Upon changing the speed of the robot, the same trajectory test was conducted at four additional different speeds, namely;0.25 m/s, 0.35 m/s, 0.65m/s and 0.75 m/s. With the gathered data, the researchers conducted a linear least-squares regression model using MATLAB wherein there is only one predictor variable (speed of the robot) and one response variable (deviation). Based on the results, the researchers concluded that the speed of 0.35 m/s is the optimal speed in which the trajectory error of the robot is minimal. The researchers recommend improving the design of the caster wheels to minimize the effects of nonsystematic errors. © 2022 IEEE.

Online Availability of a Robot Therapy for Children with Autism

In this paper we will present a case in which a robot therapy for children with autism was transferred from clinic to home conditions. The developed application enables the children to continue with the interventions in home conditions. This proved especially important in the COVID-19 pandemic. The application also allows monitoring of the child's activities, through which the therapist can later analyze the patient's behavior and offer appropriate therapy. The application shows reliable results and gives promise to develop beyond the user case we are considering. © 2023 IEEE.

Improvement of Trajectory Errors on Remote- Controlled Differential Drive Robot via Mobilebased GUI through Bluetooth Connection

There has been an increase of interest and demand in the usage of logistic indoor service robots that are designed to minimize interactions between humans due to the occurrence of the COVID-19 outbreak. The application of the rising technology in the medical sector has great benefits in the industry, such as the prevention of the spread of highly infectious diseases using distance as a factor. Rooting from the purpose of the said robot, the main focus should be the ease of navigation through achieving the desired trajectory, in order to maximize the functionality, prevent collision, reduce user maneuvering difficulties, and such. Hence, this paper is focused on improving the trajectory errors on the robot navigation performance based on different control system designs specifically, a physical joystick controller and a mobile-based Bluetooth application controller. The design of the joystick is based on a pivot as its base which is directed to all angles and the design of the Bluetooth app is based on fourdirectional buttons that will operate upon clicking, and switching to other buttons to change commands. With this, the researchers conducted linear path and rotational tests using both remote control modes that are based on five varying speed values of 0.75 m/s, 0.5m/s, 0.35m/s, 0.25m/s, and 0.15 m/s. Based on the data analysis, the yielded results showed that using the Bluetooth app lowers the robot's trajectory error by 50% to 60% compared to using ajoystick to navigate to the desired point. Thus, the researchers concluded that the design of a control system greatly affects the robot navigation in achieving the desired trajectory. Considering the nonsystematic errors, a calibration based on the hardware structure design specifically on the caster wheel is recommended. © 2023 IEEE.

Evaluation of an online human-robot interaction competition platform based on virtual reality – case study in RCAP2021

Due to the COVID-19 pandemic, many robot competitions have been canceled in the past years. To address this problem, we proposed a cloud-based VR platform for the crowdsourcing of embodied human-robot interactions. However, this system only suggested the feasibility of the competition application, and actual competitions had not yet been held and implemented. Therefore, through demonstration experiments in the RoboCup Asia Pacific (RCAP) conducted in a hybrid format with on-site and remote participation, we evaluated the usefulness of using cloud computing on AWS from whether the latency time causes problems in human-robot interaction in a virtual reality environment. [ FROM AUTHOR] Copyright of Advanced Robotics is the property of Taylor & Francis Ltd 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 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 . (Copyright applies to all s.)

Implications from Responsible Human-Robot Interaction with Anthropomorphic Service Robots for Design Science

Accelerated by the COVID-19 pandemic, anthropomorphic service robots are continuously penetrating various domains of our daily lives. With this development, the urge for an interdisciplinary approach to responsibly design human-robot interaction (HRI), with particular attention to human dignity, privacy, compliance, and transparency, increases. This paper contributes to design science, in developing a new artifact, i.e., an interdisciplinary framework for designing responsible HRI with anthropomorphic service robots, which covers the three design science research cycles. Furthermore, we propose a multi-method approach by applying this interdisciplinary framework. Thereby, our finding offer implications for designing HRI in a responsible manner. © 2022 IEEE Computer Society. All rights reserved.

A Mobile Social Hand Disinfection Robot for Use in Hospitals

Prevention of infectious diseases as the Covid-19 is of essential importance for the well-being of humanity. This is especially so at hospitals, where many vulnerable individuals frequent. Hand disinfection is one of the methods for preventing communicable diseases. In this paper we introduce a new modular mobile service robot designed for hand disinfection in hospitals and other public spaces. It consists of two separable parts: the driving base and the disinfection stand. The base was made in a horseshoe shape which allows it to lift its payload (the stand) near its center of gravity and distribute the weight evenly on its four wheels. The stand is able to function both in conjunction with the base and also autonomously. The whole robot was designed with social interaction in mind to achieve better hand sanitization compliance, which is of essential importance in hospitals for preventing infectious diseases. We conducted a test of how well the robot is able to find and approach people in its vicinity who face different directions. Even though the robot does not achieve its goal position ideally, it always ends up facing the user, which is even more important for starting an interaction than reaching its goal position very precisely. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Prototyping a VR Sandbox for Scene Customization without 3D Authoring Skills

Virtual Reality (VR) and simulation continue po-sitioning as suitable tools for fine-tuning processes otherwise impossible in real life. Such is the case of Aether, a mobile service robot for elderly care developed during the COVID-19 pandemic. Aether's development was negatively impacted due to restrictions placed on accessing long-term care facilities that impeded testing object tracking, elderly tracking, fall detection, and human-robot interactions. Our efforts to maximize Aether's development led us to create a digital twin where the core functionality is replicated to train the machine learning modules to optimize the robot's responses before real-world deployment. However, the digital twin creation requires significant authoring to ensure the virtual environment matches the real one by employing 3D technical artistry skills, which demands a professional knowledgeable in this domain. This paper presents a sandbox prototype for scene customization that allows importing, positioning, scaling, and saving changes for mobile robot simulation. Our preliminary testing of the sandbox has focused on usability to understand how the setting up of the environment is perceived. Preliminary results indicate that the sandbox is usable with improvements pertaining to improving the manipulation of the objects. © 2022 IEEE.

Deep Learning-Based Multi-modal COVID-19 Screening by Socially Assistive Robots Using Cough and Breathing Symptoms

In this paper, we present the development of a novel autonomous social robot deep learning architecture capable of real-time COVID-19 screening during human-robot interactions. The architecture allows for autonomous preliminary multi-modal COVID-19 detection of cough and breathing symptoms using a VGG16 deep learning framework. We train and validate our VGG16 network using existing COVID datasets. We then perform real-time non-contact preliminary COVID-19 screening experiments with the Pepper robot. The results for our deep learning architecture demonstrate: 1) an average computation time of 4.57 s for detection, and 2) an accuracy of 84.4% with respect to self-reported COVID symptoms. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Addressing loneliness in the workplace through human-robot interaction: Development and evaluation of a social office robot concept

New work has been a topic for a few years now and the COVID-19 pandemic has brought this trend more into focus, i.e., working remotely became more popular. However, besides various advantages, there is the risk of loneliness in employees, which can negatively affect their work performance and mental health. Research in different domains suggests that social robots could reduce loneliness. Since we were interested in whether and how such findings are transferable to the office context, we developed and tested a concept for a social office robot. More specifically, we first conducted a cultural probes study with white-collar workers to gain information about workplace loneliness and its drivers. Second, we explored design possibilities for a social office robot in a focus group. Based on the results, we created a concrete concept, Luca, which we finally evaluated and optimized with the help of interviews with participants from various industries. The present work contributes to HRI research and practice, e.g., by providing design recommendations for the implementation of a social office robot. Future research could investigate the effectiveness of a social office robot intervention in field studies. Next to implications for research and practice, potential limitations are discussed. © 2023 the author(s), published by De Gruyter, Berlin/Boston 2023.

Buzzo or Eureka - Robot that Makes Remote Participants Feel More Presence in Hybrid Discussions

Teleconferencing technology has been widely used in the context of the covid-19 pandemic. However, local and remote participants always have a poorer experience of hybrid discussion for various reasons in the leaderless group discussions with mixed online and offline members. In this paper, this phenomenon is explored through an early pilot study. We found problems with the lack of presence of remote participants in hybrid discussion sessions, as well as unclear information about the status of members. To solve such problems, we've designed a social robot called SNOTBOX. The bot indicates the participation status (marginalized or not) of the remote participant using "Buzzo" and the remote participant's desire to be heard through a "Eureka". We used both representations to attract the attention of local participants as a way to enhance the presence of remote participants in the conference. SNOTBOX is easy to produce and allows for DIY customization, and also supports multi-participant online discussions. © 2023 IEEE Computer Society. All rights reserved.

How to increase consumer intention to use Chatbots? An empirical analysis of hedonic and utilitarian motivations on social presence and the moderating effects of fear across generations

As chatbots become more advanced and popular, marketing research has paid enormous attention to the antecedents of consumer adoption of chatbots. This has become increasingly relevant because chatbots can help mitigate the fear and loneliness caused by the global pandemic. Therefore, unlike previous work that focused on design factors, we theorize that social presence serves a mediating role between consumer motivations (i.e., hedonic and utilitarian) and intention to use a chatbot service based on self-determination theory. Our results from a structural equation model (n = 377) indicate that hedonic (but not utilitarian) motivation significantly affects chatbots' social presence, ultimately influencing intention to use the chatbot service. We also found that fear of COVID-19 amplifies the effect of social presence on intention to use the chatbot service. In this dynamic, we found an additional moderated moderation effect of generational cohorts (i.e., baby boomers and Generations X, Y, and Z) in experiencing different levels of fear of COVID-19. Overall, our findings emphasize the importance of motivation-matching features for consumer adoption of chatbot services. Our findings also indicate that marketers may utilize the fear element to increase adoption of chatbot services, especially when targeting the young generations (e.g., Generation Z). © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Human vs robot baristas during the COVID-19 pandemic: effects of masks and vaccines on perceived safety and visit intention

Purpose>This study aims to compare the effect of barista type (human vs robot) on perceived safety and examine the role of two moderators (mask-wearing and coronavirus vaccination) on the effects of barista type on perceived safety and visit intention.Design/methodology/approach>The research design consists of three studies. Three experiments were sequentially designed and conducted to address research questions.Findings>Study 1 found that perceived safety mediates the effect of barista type on customers' visit intention. Study 2 revealed that the mask-wearing of human and robot baristas differently influences perceived safety. Study 3 showed that customers, especially where robot baristas are used, perceive the effect of mask-wearing differently depending on their coronavirus vaccination status.Research limitations/implications>Given that the levels of restrictions vary worldwide, together with the extent of countries' vaccination rollouts, caution is required when generalising the research findings.Practical implications>The findings have practical implications for the hospitality industry, where the roles of face masks and coronavirus vaccines in shaping consumer psychology and behaviour have been underexplored.Originality/value>Coronavirus vaccination is considered one of the most important driving forces for the recovery of hospitality businesses. As a heuristic-systematic model postulated, this study identified that vaccination status (fully vaccinated vs not vaccinated) changes the level of involvement when customers assess the level of risk in service environments. By pinpointing the function of service robots in safeguarding customers from the potential spread of the disease, this study broadens the scope of human–robot interaction research in hospitality.

Human-robot interaction environment to enhance the sense of presence in remote sports watching

The recent coronavirus disease 2019 (COVID-19) outbreak has helped increase the popularity of online video communication and meeting platforms as alternatives to face-to-face interactions. Such a trend has also triggered the emergence of remote cheering systems, hinting at the possibility that people could enjoy watching sports games in virtual environments even from their homes in the near future. However, reproducing a sense of presence similar to the atmosphere felt by fans in stadiums or event venues is a major challenge within virtual environments. Thus, our idea is to embed groups of cheerful robots in virtual environments, thereby creating a sense of unity and mimicking emotion spread among fans. In this study, we built a virtual cheering environment and embedded a game-event driven behavior model that enables a group of robots to display various emotions through nonverbal reactions according to the game flow. Then, we conducted a preliminary evaluation of the proposed system, where the participants and a group of robots were placed in a virtual cheering environment to watch a baseball game. The obtained results hinted at the meaningfulness of the proposed approach. Nevertheless, further work is necessary to achieve a sufficient sense of presence and validate the effectiveness of our proposed environment. © 2022 IEEE.

SanitizerBot: How Human-in-the-Loop Social Robots Can Playfully Support Humans

This paper evaluates a robot that distributed hand-sanitizer over an eight month period (October 2020-June 2021) in public places on the Oregon State University campus. During COVID times, many robots have been deployed in public places as social distancing enforcers, food delivery robots, UV-sanitation robots and more, but few studies have assessed the social situations of these robots. Using the context of robot distributing hand sanitizer, this work explores the benefits that social robots may provide to encouraging healthy human activities, as well as ways in which street-performance inspired approaches and a bit of humor might improve the quality and experience of functional human-robot interactions. After gaining human-in-the-loop deployment experience with a customized interface to enable both planned and improvized responses to human bystanders, we run two sub-studies. In the first, we compare the performance of the robot (moving or still) relative to a traditional hand sanitizer dispenser stick (N=2048, 3 week data collection period). In the second, we evaluate how varied utterance strategies further impact the interaction results (N=185, 2 week data collection period). The robot dramatically outperforms the stick dispenser across all tracked behavioral variables, cuing high levels of positive social engagement. This work finds the utterance design is more complex socially, and offer insights to future robot designers about how to integrate helpful and playful speech into service robot interactions. Finally, across both sub-studies, the work shows that people in groups are more likely to engage with the robot and each other, as well as sanitize their hands. © 2022 IEEE.

User experience of human-robot long-term interactions

Since interactions with social robots are novel and exciting for many people, one particular concern in this specific area of human-robot interaction (HRI) is the extent to which human users will experience the interactions positively over time, when the robot's novelty is particularly salient. In the current paper, we investigated users' experience in long-term HRIs;how users perceive the ongoing interactions and the robot's ability to sustain it over time. Therefore, here we examine the effect of the repeated measures (10 testing sessions) and the discussion theme (Covid-19 related vs general) on the way participants experienced the interaction quality with a social robot and perceived the robot's communication competency over time. We found that despite individual differences between the participants, over time participants found the interactions with Pepper to be of higher quality and that Pepper's communication skills got better. Nevertheless, our results also stressed that the discussion theme has no meaningful nor significant effect on the way people perceive Pepper and the interaction. © 2022 ACM.

Complementary role of conversational agents in e-health services

In recent years, business environments are undergoing disruptive changes across sectors [1]. Globalization and technological advances, such as artificial intelligence and the internet of things, have completely redesigned business activities, bringing to light an ever-increasing interest and attention towards the customer [2], especially in healthcare sector. In this context, researchers is paying more and more attention to the introduction of new technologies capable of meeting the patients' needs [3, 4] and the Covid-19 pandemic has contributed and still contributes to accelerate this phenomenon [5]. Therefore, emerging technologies (i.e., AI-enabled solutions, service robots, conversational agents) are proving to be effective partners in improving medical care and quality of life [6]. Conversational agents, often identified in other ways as 'chatbots', are AI-enabled service robots based on the use of text [7] and capable of interpreting natural language and ensuring automation of responses by emulating human behavior [8, 9, 10]. Their introduction is linked to help institutions and doctors in the management of their patients [11, 12], at the same time maintaining the negligible incremental costs thanks to their virtual aspect [13-14]. However, while the utilization of these tools has significantly increased during the pandemic [15, 16, 17], it is unclear what benefits they bring to service delivery. In order to identify their contributions, there is a need to find out which activities can be supported by conversational agents.This paper takes a grounded approach [18] to achieve contextual understanding design and to effectively interpret the context and meanings related to conversational agents in healthcare interactions. The study context concerns six chatbots adopted in the healthcare sector through semi-structured interviews conducted in the health ecosystem. Secondary data relating to these tools under consideration are also used to complete the picture on them. Observation, interviewing and archival documents [19] could be used in qualitative research to make comparisons and obtain enriched results due to the opportunity to bridge the weaknesses of one source by compensating it with the strengths of others. Conversational agents automate customer interactions with smart meaningful interactions powered by Artificial Intelligence, making support, information provision and contextual understanding scalable. They help doctors to conduct the conversations that matter with their patients. In this context, conversational agents play a critical role in making relevant healthcare information accessible to the right stakeholders at the right time, defining an ever-present accessible solution for patients' needs. In summary, conversational agents cannot replace the role of doctors but help them to manage patients. By conveying constant presence and fast information, they help doctors to build close relationships and trust with patients. © 2022 IEEE.

The Evaluation of Interviewer's Presentation Styles for Interview Practice with a Communicative Robot

The impact of COVID-19 has led to the shift of job interviews online. There is now a return to face-to-face interviews in important situations, such as the final interview. However, it is still difficult to practice face-to-face interviews, and there is a growing need to practice face-to-face interviews alone or remotely. The problems with practicing interviews alone are that there is no listener in front of the practitioner, so the practitioner does not feel the nervousness about being watched and evaluated. In this paper, we aim to support these issues by using a small communication robot. We conduct experiments under six conditions: practicing alone, with a person face-to-face, with an autonomous robot, with a teleoperated robot, with an avatar remotely, and with a person remotely. Then we examine the influence of the practice style, such as the practitioner's nervousness. The results suggest that the most effective practice is possible when practicing with a person, regardless of whether it is face-to-face or remotely, but that the interview practice support with a small communicative robot is useful in the current social situation. © 2022 IEEE.

An affordance-based framework for the design and analysis of learning activities in playful educational robotics contexts

Taking into account the profound impact of technology on modern education, especially during the covid19 pandemic, increasing academic interest has focused towards the design and application of such tools on different learning contexts. A specific area of Human-Computer Interaction, called affordance theory, focuses on the perception, design and use of different technologies by educators and learners in learning contexts. This paper explores the impact of affordances in the process of creative problem in the context of playful educational robotics, with an intension of informing the design of future educational experiences around the field. The study capitalizes upon previous affordance propositions and frameworks in order to establish an affordance-based framework in the scope of playful educational robotics contexts, through the adoption of a qualitative research methodology, which was considered more appropriate as an exploratory tool. As part of the qualitative analysis, the study is mapping different types of affordances, related to such technologies, as well as an iterative creative problem-solving process that stems from learners' interactions with robotic artifacts, like the CreaCube playful robotics activity, which is presented in this study.