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Based on cognitive-affective system theory and using tour guides, hotel and attraction staff as subjects, three experimental studies investigated the effect of using service robots on career identity. The results were that: (1) the use of service robots significantly affected career identity; (2) emotional exhaustion mediated the relationship between service robot use and career identity; and (3) years of work experience and staff age positively moderated the main effect. The findings provide meaningful implications for managers to more effectively deploy service robots and enhance staff career identity. [ABSTRACT FROM AUTHOR]

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

AbstractThe employment of service robots in restaurants has become increasingly common. Previous studies have explored the factors related to the impact of service robots on consumers from multiple perspectives, but this topic still lacks an integrated empirical study to organize and analyze the conclusions of previous studies. This study obtained 46 empirical studies through the WoS and Scopus databases. Based on the conclusions and data of these studies and guided by the SOR theory, a holistic conceptual framework was constructed to describe how service robots affect restaurant consumers. This study tested the conceptual framework of the construct through meta-analysis and verified the moderating role of macro variables such as time and culture. This study not only has theoretical significance for future research but can also provide practical guidance for restaurant managers in the application and deployment of service robots. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of human–robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot's intention to serve based on the human's location at static and adaptive positions. This paper presents three methodologies to address these challenges: binary classification to analyze static and adaptive postures for human localization in indoor environments using the sensor fusion method, adaptive Simultaneous Localization and Mapping (SLAM) for the robot to deliver the task, and human–robot implicit communication. VLSI hardware schemes are developed for the proposed method. Initially, the control unit processes real-time sensor data through PIR sensors and multiple ultrasonic sensors to analyze the human posture. Subsequently, static and adaptive human posture data are communicated to the robot via Wi-Fi. Finally, the robot performs services for humans using an adaptive SLAM-based triangulation navigation method. The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed-board-based FPGA Xilinx board was used for experimental validation. [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]

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With the advancement of service robots, discovering the emotional needs of users is becoming increasingly important. Unlike research focusing solely on human facial expression recognition or image sentiment recognition, our work proposes that the objects in the environment also impact human emotions, such as candy, which can make people happy. Therefore, studying the impact of objects on human emotions is crucial for service robots to regulate human emotions and provide more satisfactory services. In this work, we first propose the emotional attribute of objects: the ability to improve people's moods. And we propose a strategy for recognizing this attribute. To achieve this, we first construct the H–S object emotional attribute image dataset, which contains different objects with pleasant or unpleasant emotion labels for people. We then propose the YOLOv3-SESA object detection model. By incorporating YOLOv3 with the SESA attention module, the model focuses more on the target objects, achieving higher recognition accuracy for small objects in the environment. We gain the correlation frequency between objects and emotion labels and convert it into emotional attribute probability values. Objects with the value exceeding a predefined threshold are defined as having an emotional attribute. Our experiments validate the effectiveness of our approach, yielding a list of common objects that can please users. By leveraging the knowledge of object emotional attributes, service robots can proactively provide emotionally appealing objects to humans, offering psychological comfort when they are depressed. [ABSTRACT FROM AUTHOR]

Object handover is a fundamental task for collaborative robots, particularly service robots. In in-home assistance scenarios, individuals often face constraints due to their posture and declining physical functions, necessitating high demands on robots for flexible real-time control and intuitive interactions. During robot-to-human handovers, individuals are limited to making perceptual judgements based on the appearance of the object and the consistent behaviour of the robot. This hinders their comprehensive perception and may lead to unexpected dangerous behaviour. Various handover trajectories pose challenges to predictive robot control and motion coordination. Many studies have shown that force guidance can provide adequate information to the receivers. However, force modulation with intention judgements based on velocity, acceleration, or jerk may impede the intended motion and require additional effort. In this paper, starting from a human-to-human handover study, an anisotropic variable force-guided robot-to-human handover control method is proposed to overcome the cognition-reality gap. The retraction motion was decoupled based on a fitted motion plane and a task-related linear trajectory, which served as a reference for overshoot suppression and impedance force modulation. The experimental results and user surveys show that the anisotropic variable impedance force suppresses overshooting without impeding the intended motions, giving the receiver sufficient time for behavioural adjustments and assisting them in completing a safe and efficient handover in a preferred manner.

Humanoid robots are increasingly being utilized in various activities involving humans, as they can facilitate certain tasks and provide benefits to users. Humanoid service robots possess capabilities akin to human performance, often proving advantageous due to their operational speed and immunity to fatigue. Within the scope of this study, Pepper, a humanoid robot renowned for its fidelity in mimicking human gestures and behavior, serves as the focal point. Tasked with aiding office occupants in object manipulation and relocation, Pepper underwent a targeted reliability assessment. This assessment encompassed the development of a reliability block diagram (RBD), alongside meticulous analyses of individual components and system functionality across diverse operational scenarios. [ABSTRACT FROM AUTHOR]

This study investigated the effect of attribution and approach movement of the social robot when the user wrongly perceives an error as the robot's responsibility. The robot's responsibility attribution and approach movement strategies for the error recovery were examined in a situation where the robot was functioning normally, but the user misunderstood it as robot's fault. In the experiment participants were exposed to four different verbal and movement interaction scenarios with a social robot and then responded to a survey concerning emotions and trust. Results showed that people no longer trusted the robot that approached while attributing the responsibility to the user. The implication of this study is that a powerful self-serving bias is aroused when a robot attributes the responsibility to the user, and thus, it negatively impacts user experiences even if the event took place due to the user's misunderstanding. This study suggests empirical guidance for designing a social robot's attribution strategies and movement interactions. [ABSTRACT FROM AUTHOR]

Service robots are increasingly enhancing the quality of life across multiple sectors and personal domains. Recently, their use has broadened to include fence-free environments in areas such as restaurants, factories, delivery hubs, and hospitals. Evaluating the performance of these robots within each domain is crucial. It is vital to assess and determine the most suitable driving platform and application for each work environment. This study proposes a classification scheme for service robots in both industrial and personal settings released by 2023, focusing on their driving mechanisms. Initially, we categorize driving mechanisms into holonomic and non-holonomic methods. Subsequently, we classify service robots based on the driving platforms adopted and delve into their applications, strengths, and limitations. We then highlight the functionalities and performance metrics of a recently researched service robot. Through this study, we aim to present a clear classification and evaluation of service robots' driving mechanisms. Our objective is to facilitate the development of more dependable robotic solutions. [ABSTRACT FROM AUTHOR]

Service robots that coexist with humans in everyday life have become more common, and they have provided customer service in physical shops around the world in recent years. However, their potential in effective sales strategies has not been fully realized due to their low social presence. This study aims to clarify what kind of robot behavior enhances the social presence of service robots and how it affects human–robot interaction and purchasing behavior. We conducted two experiments with a sales robot, Pepper, at a retail shop in Kyoto. In Experiment 1, we showed that the robot's social presence increased and that customers looked at the robot longer when the robot understood human gaze information and was capable of shared attention. In Experiment 2, we showed that the probability of customers picking up products increased when the robot suggested products based on the humans' degree of attention from gaze and posture information. These results indicate that the robot's ability to understand and make utterances about a customer's orientation and attention effectively enhances human–robot communication and purchasing motivation. [ABSTRACT FROM AUTHOR]

Telepresence robots (TRs) are increasingly important for remote communication and collaboration, particularly in situations where physical presence is not possible. One key feature of TRs is person-following, which relies on the detection and distance estimation of individuals. This study proposes an autonomous person-following TR using a monocular camera and deep-learning YOLO for person detection and distance estimation. To compensate for the monocular camera's inability to provide depth information, a novel distance estimation algorithm based on focal length and person width is introduced. The estimated width information of the detected person is extracted from the bounding box generated by YOLO. A pre-trained model using the MS COCO dataset is employed with YOLO for the person detection task. For robot movement control, a region-based controller is proposed to enable the robot to move based on the detected person's location in the image captured by the camera. Finally, integration and deployment of the proposed method in the TR is carried out using the Robot Operating System (ROS). Experimental results demonstrate that the TR can successfully follow a person using the proposed algorithm, thus highlighting its effectiveness for person-following tasks.

Introduction: In recent years, the perceptual capabilities of robots have been significantly enhanced. However, the task execution of the robots still lacks adaptive capabilities in unstructured and dynamic environments. Methods: In this paper, we propose an ontology based autonomous robot task processing framework (ARTProF), to improve the robot's adaptability within unstructured and dynamic environments. ARTProF unifies ontological knowledge representation, reasoning, and autonomous task planning and execution into a single framework. The interface between the knowledge base and neural network-based object detection is first introduced in ARTProF to improve the robot's perception capabilities. A knowledge-driven manipulation operator based on Robot Operating System (ROS) is then designed to facilitate the interaction between the knowledge base and the robot's primitive actions. Additionally, an operation similarity model is proposed to endow the robot with the ability to generalize to novel objects. Finally, a dynamic task planning algorithm, leveraging ontological knowledge, equips the robot with adaptability to execute tasks in unstructured and dynamic environments. Results: Experimental results on real-world scenarios and simulations demonstrate the effectiveness and efficiency of the proposed ARTProF framework. Discussion: In future work, we will focus on refining the ARTProF framework by integrating neurosymbolic inference. [ABSTRACT FROM AUTHOR]

Copyright of Tourism & Management Studies is the property of Escola Superior de Gestao, Hotelaria e Turismo, Universidade do Algarve 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.)

When faced with challenges such as adapting to dynamic environments and handling ambiguous identification, indoor service robots encounter manifold difficulties. This paper aims to address this issue by proposing the design of a service robot equipped with precise small-object recognition, autonomous path planning, and obstacle-avoidance capabilities. We conducted in-depth research on the suitability of three SLAM algorithms (GMapping, Hector-SLAM, and Cartographer) in indoor environments and explored their performance disparities. Upon this foundation, we have elected to utilize the STM32F407VET6 and Nvidia Jetson Nano B01 as our processing controllers. For the program design on the STM32 side, we are employing the FreeRTOS operating system, while for the Jetson Nano side, we are employing ROS (Robot Operating System) for program design. The robot employs a differential drive chassis, enabling successful autonomous path planning and obstacle-avoidance maneuvers. Within indoor environments, we utilized the YOLOv3 algorithm for target detection, achieving precise target identification. Through a series of simulations and real-world experiments, we validated the performance and feasibility of the robot, including mapping, navigation, and target detection functionalities. Experimental results demonstrate the robot's outstanding performance and accuracy in indoor environments, offering users efficient service and presenting new avenues and methodologies for the development of indoor service robots. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to test the effect that anthropomorphic framing (i.e. robot vs automatic machine) has on consumers' responses in case of service failure. Specifically, the authors hypothesize that consumers hold an unconscious association between the word "robot" and agency and that the higher agency attributed to self-service machines framed as robots (vs automatic machines) leads, in turn, to a more positive service evaluation in case of service failure. Design/methodology/approach: The authors have conducted four experimental studies to test the framework presented in this paper. In Studies 1a and 1b, the authors used an Implicit Association Test to test for the unconscious association held by consumers about robots as being intelligent machines (i.e. agency). In Studies 2 and 3, the authors tested the effect that framing technology as robots (vs automatic machines) has on consumers' responses to service failure using two online experiments across different consumption contexts (hotel, restaurant) and using different dependent variables (service evaluation, satisfaction and word-of-mouth). Findings: The authors show that consumers evaluate more positively a service failure involving a self-service technology framed as a robot rather than one framed as an automatic machine. They provide evidence that this effect is driven by higher perceptions of agency and that the association between technology and agency held by consumers is an unconscious one. Originality/value: This paper investigates a novel driver of consumers' perception of agency of technology, namely, how the technology is framed. Moreover, this study sheds light on consumers' responses to technology's service failure. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hospitality & Tourism Technology is the property of Emerald Publishing Limited 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.)

Rapid advancement in robotics technology has paved the way for developing mobile service robots capable of human interaction and assistance. In this paper, we propose a comprehensive approach to design, fabricate, and optimize the overall structure of a dual-arm service robot. The conceptual design phase focuses on both critical components, the mobile platform and the manipulation system, essential for seamless navigation and effective task execution. In the proposed system, the distribution of the robot payload in terms of region, maximum stress, and displacement is examined, comprehensively analyzed, and compared with the relevant works. In addition, to enhance the system’s efficiency while minimizing its weight, we introduce a lightweight design approach in which Finite Element Analysis is utilized to optimize the frame structure. Subsequently, we fabricate a physical prototype based on the derived model. Finally, we provide a kinematic model for our dual-arm service robot and demonstrate its efficacy in both control and human–robot interaction (HRI) tasks. Experimental results indicate that the proposed dual arm design can achieve a significant weight reduction of 25% from the original design while still performing actions smoothly for HRI tasks.

Recent years have seen a surge in service robots built on the ROS framework, where effective path planning algorithms are crucial for their successful operation. However, real-world operation presents challenges due to changes in environmental features and unexpected dynamic obstacles. If the path planning algorithms fail to update the state of the map layers in a timely manner, it can result in mission failures for the robots. Therefore, this study proposes a novel Motion Trajectory Analysis Regulator Navigation Framework within the ROS system. This framework incorporates path planning regulators for both global and local path planning algorithms, effectively mitigating path planning failures caused by dynamic environmental changes. For enhanced flexibility in path planning and dynamic obstacle avoidance, this framework proposes the ADRRT* algorithm for global path planning and the FGM-DWA algorithm for local path planning, replacing the traditional A* and DWA algorithms, respectively. Real-field experiments with a service robot demonstrate that the proposed methods not only ensure successful navigation with 100% success rate and a 23% improvement in time efficiency but also effectively handle dynamic and U-shaped obstacles, overcoming the challenge of path planning failures.

A critical part of achieving robust and safe navigation for mobile robots is selecting the right navigation policies trained through simulation to operate effectively in real-world situations. Simulation-trained policies often struggle for mobile robot settings deployed in real-world navigation tasks, leading to policy degradation and increased risk manners. To address these challenges, a cooperative deep reinforcement learning policies (CDRL) framework is proposed, ensuring safe exploration and deployment in unknown complex environments. The CDRL framework cooperates with exploration and exploitation policies based on a policy-switching mechanism, which efficiently helps the robot escape the local optima. Instead of transferring a single navigation policy, CDRL leverages cooperative navigation policies with diverse reward functions, enabling them to adapt to unknown complex environments. The proposed technique is based on an exploration distributional soft actor critic (E-DSAC) and soft actor critic (SAC) algorithms, which enhances training efficiency. The deep reinforcement learning (deep RL) models in this framework are represented by a mobile service robot that reaches target positions without requiring a map presentation. Experimental results show that the proposed framework is proven to have safe and fast motions in terms of navigation time and success rates. The sim-to-real transfer process of mobile service robots can be found (https://youtu.be/vIxRqXidKIM).

This paper presents a novel algorithm that enhances the efficiency of robotic manipulation by enabling the robot to reach its target with fewer obstacles being removed, thus leading to faster performance times. The challenges faced by robotic manipulation in complex and cluttered environments (e.g., supermarkets, factories, etc.), where changes are constantly occurring, are well-known. To tackle these challenges, the robot should generate a collision-free path to reach the target while requiring minimal obstacle relocation to improve task efficiency. To achieve this, the proposed task planning algorithm incorporates the Rapidly-exploring Random Tree (RRT) algorithm and a modified Vector Field Histogram Plus (VFH+). The algorithm was tested in simulated service domain environments with objects placed on shelves followed by real-robot experiments. In both experiments, the results of the proposed algorithm demonstrated a lower number of obstacle removals (up to 37%) and consequently faster performance times compared to other existing methods (improved by up to 36%). Thus, the results showed that the proposed algorithm reduces the number of obstacle relocations and improves performance time compared to existing methods. As a result, the proposed method was evaluated as an improvement in task efficiency over previous approaches and has significant implications for the use of robotic manipulation in service domains with cluttered environments.

This investigation is centrally focused on the comprehensive evolution and enhancement of FOODIEBOT(shortened name for food delivery robot, an adaptive service automaton with a wide range of functionalities. Its capabilities encompass sophisticated image processing methods, seamlessly integrated via mobile applications (APP) and web interfaces, tailored specifically for intricate object manipulation in dining hall settings. During its developmental phase, the precise calibration of PID controller coefficients emerged as an essential requirement. The model underwent meticulous scrutiny through detailed simulations using MATLAB software. Following this phase, its operational efficiency navigating through circular, elliptical, spiral, and octagonal trajectories underwent rigorous examination, utilizing optimization methodologies like Beetle Antennae Search (BAS) Algorithm, Particle Swarm Optimization (PSO), Pelican Optimization Algorithm (POA), and Equilibrium Optimizer (EO). The exposition emphasizes the diverse dispersion of optimized coefficients within each algorithmic framework. The pinnacle of this effort involved a comprehensive evaluation of pathway performance, amalgamating insights from each optimization paradigm. The discussion extensively delineates both simulated and real-time performance metrics of the robot, validating the accuracy and reliability of simulation in deriving PID controller values. In the comprehensive evaluation of methodologies and the robotic system’s effectiveness, the BAS technique excels in operational efficiency. This method consistently outperforms its counterparts in execution time, primarily due to its meticulous optimization of particle count. The comparative analysis across various trajectories reveals intriguing insights. The EO approach showcases outstanding accuracy in Path 1, while the POA method achieves optimal precision in Path 3. Impressively, the BAS technique demonstrates unparalleled accuracy in Path 4. Furthermore, in terms of solution optimization, the BAS method consistently displays the shortest execution times across all traversed pathways. When examining maximum velocity along these routes, the PSO method excels in Paths 1, 3, and 4, consistently achieving the highest speeds. Notably, Path 2 uniquely displays the peak velocity attained by the POA method. This article presents comprehensive insights into the constituent elements of the robotic system’s design. The inquiry delves into the intricate nuances of optimization methodologies, elucidating their profound impact on the service automaton’s performance across diverse orientations. The pragmatic implications underscore the critical role of temporal considerations in the judicious selection of these methodologies. The observed congruence between simulated and practical performance serves as a definitive validation, affirming the precision of simulation computations and the subsequent derivation of PID controller values.

This study addresses the challenges of human–robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot’s intention to serve based on the human’s location at static and adaptive positions. This paper presents three methodologies to address these challenges: binary classification to analyze static and adaptive postures for human localization in indoor environments using the sensor fusion method, adaptive Simultaneous Localization and Mapping (SLAM) for the robot to deliver the task, and human–robot implicit communication. VLSI hardware schemes are developed for the proposed method. Initially, the control unit processes real-time sensor data through PIR sensors and multiple ultrasonic sensors to analyze the human posture. Subsequently, static and adaptive human posture data are communicated to the robot via Wi-Fi. Finally, the robot performs services for humans using an adaptive SLAM-based triangulation navigation method. The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed-board-based FPGA Xilinx board was used for experimental validation.

The purpose of this study was to investigate decision-makers' views on changes that robotics will create in welfare services. The purpose was also to discover what the opportunities and challenges are in human–robot interaction during these changes and how to manage these changes. As a research method, an online survey was used. The survey was sent to Finnish decision-makers (N = 184). They were divided into three groups: Techno-positive (n = 66), Techno-neutral (n = 47), and Techno-critical (n = 71). According to the results, more than 80% of the respondents saw that robots can offer support in existing work tasks, and more than 70% saw that the robots can do existing tasks. The most often mentioned challenges were the reduction of interaction and the reduction of human touch. Further, there are various knowledge needs among the respondents. Most of the knowledge needs were not based on the technical use of the robots; rather, they were quite scattered. The results suggest that successful use and implementation of robots in welfare services require a comprehensive plan and change agents. This study suggests that techno-positive people could act as change agents, assisting in implementing the changes. In addition, to manage change in the welfare services it is essential to improve the quality of the information, solve the resistance to change, create organizational awareness, and understanding, and establish a psychological commitment to change the processes. [ABSTRACT FROM AUTHOR]

AbstractThis study investigates whether an extroverted personality of service robots can facilitate user self-disclosure and enhance human–robot intimacy. An experiment of 2 2 between-subjects factorial design was conducted to examine the effects of robot personality (extroverted and introverted) and human personality (extroverted and introverted) on user self-disclosure and intimacy. Sixty-four participants from a university campus were included in this study. We simulated a financial investment scenario in which the participants were required to disclose personal information to a robot acting as an investment advisor. Functional near-infrared spectroscopy (fNIRS) was used to capture participants’ prefrontal cortex (PFC) activity. The results revealed a positive effect of robot extroversion on user self-disclosure and intimacy, and this impact was stronger among extroverted participants than among introverted ones. Additionally, perceived robot extroversion enabled users to regard the robot as more animate, promoting automated social presence and ultimately enhancing self-disclosure and intimacy. According to the fNIRS results, the participants who interacted with an extroverted robot had stronger activation in the medial PFC than those who interacted with an introverted robot. The self-reported and fNIRS results were consistently aligned, demonstrating the positive role of extroversion in service robots. These findings provide theoretical and practical insights into the design of service robots in contexts requiring user disclosure. [ABSTRACT FROM AUTHOR]

Telepresence technology creates the opportunity for people that were traditionally left out of the workforce to work remotely. In the service industry, a pool of novice remote workers could teleoperate robots to perform short work stints to fill in the gaps left by the dwindling workforce. A hurdle is that consistently talking appropriately and politely imposes a severe mental burden on such novice operators and the quality of the service may suffer. In this study, we propose a teleoperation support system that lets novice remote workers talk freely without considering appropriateness and politeness while maintaining the quality of the service. The proposed system exploits intent recognition to transform casual utterances into predefined appropriate and polite utterances. We conducted a within-subject user study where 23 participants played the role of novice remote operators controlling a guardsman robot in charge of monitoring customers' behaviors. We measured the workload with and without using the proposed support system using NASA task load index questionnaires. The workload was significantly lower (p<.001) when using the proposed support system (M = 46.07, SD = 14.36) than when not using it (M = 62.74, SD = 12.70). The effect size was large (Cohen's d = 1.23). [ABSTRACT FROM AUTHOR]

The willingness to use service robots plays a pivotal role in human–robot interaction. To establish a valid measure in the Chinese context, this study aimed to revisit the validity and reliability of the Service Robot Integration Willingness (SRIW) Scale among Chinese adults. A total of 955 participants were recruited to complete the Chinese version of the SRIW. Our findings revealed a four-factor model comprising 31 items, indicating a strong model fit. Furthermore, trust in automation correlated positively with the Chinese SRIW, while negative attitudes toward robots exhibited a significant inverse correlation, supportting the Chinese SRIW's substantial criterion-related validity. In conclusion, this article introduces an updated Chinese SRIW, underscoring its efficacy in measuring the readiness to adopt service robots in China. [ABSTRACT FROM AUTHOR]