Intelligent Service Robots Research Articles

Framework for Integrating Large Language Models with a Robotic Health Attendant for Adaptive Task Execution in Patient Care

The development of intelligent medical service robots for patient care presents significant challenges, particularly in integrating diverse knowledge sources and enabling robots to autonomously perform tasks in dynamic and unpredictable healthcare environments. This study introduces a novel framework that combines large language models with healthcare-specific knowledge and robotic operations to enhance autonomous task execution for a Robotic Health Attendant. Utilizing OpenAI’s ChatGPT, the system processes structured information about patient care protocols and unstructured human inputs to generate context-aware robot actions. A prototype system was tested in a simulated patient room where the robot successfully performed both simple individual actions and complex tasks involving the execution of multiple actions, based on real-time dialogues with the language model and predefined task specifications. The results demonstrate the potential of language models to reduce the reliance on hardcoded logic and provide healthcare professionals with the ability to interact with robotic systems through natural language.

Research on Visual Recognition of Service Robots Based on YOLO Algorithm

In modern society, based on the aging of population and the rapid development of intelligent technology, the demand for intelligent service robots is increasing, and visual recognition is one of the key technologies of service robots. The rapid development of deep learning technology provides new solutions for visual recognition. Existing target recognition algorithms have the problem of complex model and slow inference speed. According to the above problems, this paper proposes a visual recognition algorithm based on YOLO series, which is used for the visual recognition design scheme of service robot. It can ensure the detection accuracy, reduce the speed of the model complexity, speed up the reasoning, and determine whether to meet the actual needs. Through randomly selected pictures, the results show that the design scheme can effectively improve the detection accuracy and real-time of the service robot on targets, and enhance its autonomous service ability in various complex environments.

A Survey on the Use of Intelligent Physical Service Robots for Elderly or Disabled Bedridden People at Home

A Survey on the Use of Intelligent Physical Service Robots for Elderly or Disabled Bedridden People at Home

A research on the influence mechanism of humanization degree of service robots on user misbehavior

PurposeExplore how the degree of humanization affects user misconduct, and provide effective misconduct prevention measures for the wide application of artificial intelligence in the future.Design/methodology/approachBased on the “Uncanny Valley theory”, three experiments were conducted to explore the relationship between the degree of humanization of service machines and user misbehavior, and to analyze the mediating role of cognitive resistance and the moderating role of social class.FindingsThere is a U-shaped relationship between the degree of humanization of service machines and user misbehavior; Social class not only regulates the main effect of anthropomorphism on misbehavior, but also regulates the intermediary effect of anthropomorphism on cognitive resistance, thus affecting misbehavior.Research limitations/implicationsThe design of the service robot can be from the user’s point of view, combined with the user’s social class, match different user types, and provide the same preferences as the user’s humanoid service robot.Practical implicationsThis study is an important reference value for enterprises and governments to provide intelligent services in public places. It can prevent the robot from being vandalized and also provide users with a comfortable human-computer interaction experience, expanding the positive effects of providing smart services by government and enterprises.Social implicationsThis study avoids and reduces users' misbehavior towards intelligent service robots, improves users' satisfaction in using service robots, and avoids service robots being damaged, resulting in waste of government, enterprise and social resources.Originality/valueFrom the perspective of product factors to identify the inducing factors of improper behavior, from the perspective of social class of users to analyze the moderating effect of humanization degree and user improper behavior.

The applications and prospects of intelligent services robots in medicine

With the rapid growth of the ageing population and the increasing demand for medical services, the medical market is facing great challenges and opportunities. Intelligent service robots can provide various medical services, such as patient monitoring, drug distribution, medical record management, etc., which can improve medical efficiency, reduce the burden of medical staff, and have the advantage of 24-hour uninterrupted service. However, the application of intelligent service robots in the medical field still faces some challenges, such as technical limitations, legal and ethical issues. In addition, there are also differences in patient acceptance and demand for intelligent service robots. Based on the summary of intelligent medical treatment, this paper will introduce the application of intelligent service robots, study the application and prospect of intelligent service robots in medical treatment and the innovation of intelligent service robots in business models. Through literature review, SWOT analysis and data, the paper explains the wide application of intelligent service robots and their future opportunities and challenges.

Retracted: Construction of Facial Expression Recognition Technology of Intelligent Service Robot Based on Nanofiber Flexible Wearable Electronic Skins

Retracted: Construction of Facial Expression Recognition Technology of Intelligent Service Robot Based on Nanofiber Flexible Wearable Electronic Skins

Beyond Words: An Intelligent Human-Machine Dialogue System with Multimodal Generation and Emotional Comprehension

Intelligent service robots have become an indispensable aspect of modern-day society, playing a crucial role in various domains ranging from healthcare to hospitality. Among these robotic systems, human-machine dialogue systems are particularly noteworthy as they deliver both auditory and visual services to users, effectively bridging the communication gap between humans and machines. Despite their utility, the majority of existing approaches to these systems primarily concentrate on augmenting the logical coherence of the system’s responses, inadvertently neglecting the significance of user emotions in shaping a comprehensive communication experience. To tackle this shortcoming, we propose the development of an innovative human-machine dialogue system that is both intelligent and emotionally sensitive, employing multimodal generation techniques. This system is architecturally comprised of three components: (1) data collection and processing, responsible for gathering and preparing relevant information, (2) a dialogue engine, which generates contextually appropriate responses, and (3) an interaction module, responsible for facilitating the communication interface between users and the system. To validate our proposed approach, we have constructed a prototype system and conducted an evaluation of the performance of the core dialogue engine by utilizing an open dataset. The results of our study indicate that our system demonstrates a remarkable level of multimodal generation response, ultimately offering a more human-like dialogue experience.

Relationship Quality in Customer-service Robot Interactions in Industry 5.0: An Analysis of Value Recipes

AbstractThe paper studies the interactions between customers and robots within the framework of Industry 5.0-driven services. Prior studies have explored several factors contributing to the quality of these interactions, with perceived value being a crucial aspect. This study uses value recipes, which refer to specific configurations of how different benefits and costs are weighed up/evaluated, as a theoretical framework to investigate the quality of relationships between customers and service robots. The study aims to shed light on the complex interplay between different value dimensions that shape customers' relationships with robots. To achieve this goal, the authors analyze what value configurations facilitate or impede high-quality relationships between customers and service robots. Fuzzy set qualitative comparative analysis (fsQCA) was used to analyze data from 326 consumers. The data reveal that value recipes comprising positive values (such as relational benefit, novelty, control, personalization, excellence, and convenience) and negative values (about privacy and effort) prove highly effective in augmenting relationship quality. Results also underscore those negative values either in isolation or in conjunction with positive values, do not impede relationship quality. The theoretical contribution of this study lies in presenting new insights into relationship dynamics between customers and service robots in an Industry 5.0 value-driven context. From a practical standpoint, the findings suggest guidelines for successfully infusing the retail landscape with more intelligent service robots.

Retail robots as sales assistants: how speciesism moderates the effect of robot intelligence on customer perceptions and behaviour

PurposeRetailers increasingly endeavour to implement artificial intelligence (AI) innovations, such as humanoid social robots (HSRs), to enhance customer experience. This paper investigates the interactive effect of HSR intelligence and consumers' speciesism on their perceptions of retail robots as sales assistants.Design/methodology/approachThree online experiments testing the effects of HSRs' intellectual intelligence on individuals' perceived competence and, consequently, their decision to shop at a retail store that uses HSRs as sales assistants are reported. Furthermore, the authors examine whether speciesism attenuates these effects such that a mediation effect is likely to be observed for individuals low in speciesism but not for those with high levels of speciesism. Data for all studies were collected on Prolific and analysed with SPSS to perform a logistic regression and PROCESS 4.0 (Hayes, 2022) for the mediation and moderated-mediation analysis.FindingsThe findings show that the level of speciesism moderates the relationship between HSR intellectual intelligence and perceived competence such that an effect is found for low but not for high HSR intelligence. When HSR intellectual intelligence is low, individuals with higher levels of speciesism (vs low) rate the HSR as less competent and display lower HSR acceptance (i.e. customers' decision to shop using retail robots as sales assistants).Originality/valueThis research responds to calls in research to adopt a human-like perspective to understand the compatibility between humans and robots and determine how personality traits, such as a person's level of speciesism, may affect the acceptance of AI technologies replicating human characteristics (Schmitt, 2019). To the best of the authors' knowledge, the present research is the first to examine the moderating role of speciesism on customer perceptions of non-human retail assistants (i.e. human-like and intelligent service robots). This study is the first to showcase that speciesism, normally considered a negative social behaviour, can positively influence individuals' decisions to engage with HSRs.

Role of trust in customer attitude and behaviour formation towards social service robots

This research proposes a model that identifies the antecedents of customers’ attitudes and behaviours towards the utilization of artificially intelligent (AI) social service robots in hospitality and tourism service delivery. The model highlights the importance of trust and its determinants on customers’ attitudes and behaviours towards social service robots. The proposed model and the hypotheses are tested utilizing data collected from the users of two distinctly different hospitality and tourism services. Data were analysed adopting a PLS-SEM approach. Results indicate that the level of acceptance of the use of social robots in service delivery is determined by a multistage process, in which trust perceptions play critical roles. Heuristic (i.e., hedonic motivation) and the individual (i.e., innovativeness) factors positively influence trust in social robots during service delivery. Findings, however, suggest significant differences in different service contexts: anthropomorphism has no effect on trust construct and effort expectancy does not influence positive emotion in hedonic service contexts while these relationships are significant in functional service contexts. Practical and theoretical implications of the findings are discussed.

Learning to reason over scene graphs: a case study of finetuning GPT-2 into a robot language model for grounded task planning.

Long-horizon task planning is essential for the development of intelligent assistive and service robots. In this work, we investigate the applicability of a smaller class of large language models (LLMs), specifically GPT-2, in robotic task planning by learning to decompose tasks into subgoal specifications for a planner to execute sequentially. Our method grounds the input of the LLM on the domain that is represented as a scene graph, enabling it to translate human requests into executable robot plans, thereby learning to reason over long-horizon tasks, as encountered in the ALFRED benchmark. We compare our approach with classical planning and baseline methods to examine the applicability and generalizability of LLM-based planners. Our findings suggest that the knowledge stored in an LLM can be effectively grounded to perform long-horizon task planning, demonstrating the promising potential for the future application of neuro-symbolic planning methods in robotics.

How intelligent automation, service robots, and AI will reshape service products and their delivery

Intelligent Automation in form of robots, smart self-service technologies, wearable technologies, software and systems such as machine learning, generative artificial intelligence (AI) such as ChatGPT, and the metaverse are increasingly adopted in a wide range of customer-facing service settings. The shift toward robot- and AI-powered services will lead to improved customer experiences, service quality, and productivity all at the same time. However, these also carry ethical, fairness, and privacy risks for customers and society. In this opinion piece, we discuss the implications of the service revolution for service firms, their marketing, and their customers, and provide avenues for future research opportunities.

Advancements in Robotic Systems and Human Robot Interaction for Industry 4.0

Robotic systems are software and algorithms used to mechanize iterative human processes. Robotic Process Automation (RPA) operates based on simple principles and business logic, enabling it to engage with various information systems by using pre-existing graphical user interfaces. The process is the use of non-invasive software robots, often referred to as “bots,” to automate actions that are repetitive in nature and governed by predefined rules. The integration of data analytics, artificial intelligence (AI), process mining, and cognitive computing is now being used to expand the capabilities of RPA, enabling it to do more intricate jobs. This study investigates the progress made in robotic systems and the interaction between humans and robots in Industry 4.0 context. The paper examines the use of RPA, the incorporation of AI into robotic systems, and the advancement of autonomous driving and mobile robots. The study also emphasizes the significance of efficient human-robot interaction strategies and the possible influence of artificial intelligence (AI) on the prospective progress of intelligent and independent service robots. Furthermore, this study delves into the obstacles and forecasts pertaining to the development of sophisticated machine intelligence.

A multimodal domestic service robot interaction system for people with declined abilities to express themselves

Driven by the shortage of qualified nurses and the increasing average age of the population, the ambient assisted living style using intelligent service robots and smart home systems has become an excellent choice to free up caregiver time and energy and provide users with a sense of independence. However, users’ unique environments and differences in abilities to express themselves through different interaction modalities make intention recognition and interaction between user and service system very difficult, limiting the use of these new nursing technologies. This paper presents a multimodal domestic service robot interaction system and proposes a multimodal fusion algorithm for intention recognition to deal with these problems. The impacts of short-term and long-term changes were taken into account. Implemented interaction modalities include touch, voice, myoelectricity gesture, visual gesture, and haptics. Users could freely choose one or more modalities through which to express themselves. Virtual games and virtual activities of independent living were designed for pre-training and evaluating users’ abilities to use different interaction modalities in their unique environments. A domestic service robot interaction system was built, on which a set of experiments were carried out to test the system’s stability and intention recognition ability in different scenarios. The experiment results show that the system is stable and effective and can adapt to different scenarios. In addition, the intention recognition rate in the experiments was 93.62%. Older adults could master the system quickly and use it to provide some assistance for their independent living.

Determinants of Repurchase Intentions of Hospitality Services Delivered by Artificially Intelligent (AI) Service Robots

The current study examines how subjective norms, effort expectations, and performance expectations affect perceived value and quality of hospitality service experiences provided by service robots. Later, the experience quality and perceived value on customers’ overall satisfaction determine the plans to repurchase AI (Artificial Intelligence) services in the hotels. A total of 331 valid responses were gathered from hotel customers who had experience with service robots using a purposive sampling strategy. The salient findings of PLS-SEM indicate that subjective norms, effort expectations, and performance expectations all considerably improve the perceived value and quality of experiences. Furthermore, hotel customers’ overall satisfaction with services provided by robots is significantly impacted by experience quality and perceived value. Finally, overall satisfaction considerably increases customers’ preference to repurchase those services. This present study added significance for hotels on customer AI service robots repurchase intention that may deliver a preliminary blueprint for further research.

Refined Answer Selection Method with Attentive Bidirectional Long Short-Term Memory Network and Self-Attention Mechanism for Intelligent Medical Service Robot

Answer selection, as a crucial method for intelligent medical service robots, has become more and more important in natural language processing (NLP). However, there are still some critical issues in the answer selection model. On the one hand, the model lacks semantic understanding of long questions because of noise information in a question–answer (QA) pair. On the other hand, some researchers combine two or more neural network models to improve the quality of answer selection. However, these models focus on the similarity between questions and answers without considering background information. To this end, this paper proposes a novel refined answer selection method, which uses an attentive bidirectional long short-term memory (Bi-LSTM) network and a self-attention mechanism to solve these issues. First of all, this paper constructs the required knowledge-based text as background information and converts the questions and answers from words to vectors, respectively. Furthermore, the self-attention mechanism is adopted to extract the global features from the vectors. Finally, an attentive Bi-LSTM network is designed to address long-distance dependent learning problems and calculate the similarity between the question and answer with consideration of the background knowledge information. To verify the effectiveness of the proposed method, this paper constructs a knowledge-based QA dataset including multiple medical QA pairs and conducts a series of experiments on it. The experimental results reveal that the proposed approach could achieve impressive performance on the answer selection task and reach an accuracy of 71.4%, MAP of 68.8%, and decrease the BLUE indicator to 3.10.

Customers’ acceptance of artificially intelligent service robots: The influence of trust and culture

This study addresses two critical research gaps in human-robot interaction (HRI): the limited systematic research on the role of trust in customers’ acceptance of artificially intelligent (AI) robots; and the lack of understanding of robot acceptance under different cultural backgrounds. Drawing on the AIDUA framework, this study examines the impacts of trust and moderating effects of both national (the U.S. and China) and individual culture on customers’ intentions to use AI robots in hospitality services by developing a theoretical model. The model is tested on data collected using online data collection platforms from 491 U.S. and 495 Chinese respondents. PLS-SEM and the bootstrapping method were used to test the hypothesized relationships and analyze the moderating effects of culture, respectively. The findings suggest that trust in interaction with AI robots is a significant higher-order construct that influences the intention of use. Furthermore, uncertainty avoidance, long-term orientation, and power distance have been found to exhibit significant moderation effects. The results of this study extend the theoretical frameworks in HRI and provide detailed guidance to promote AI robot applications across different cultures.

Creating the optimal design approach of facial expression for the elderly intelligent service robot

Creating the optimal design approach of facial expression for the elderly intelligent service robot

Design & development of a slave robot for performing domestic applications

In this paper, we present the development of an automated robot which could be used for a host of interactive applications. This paper provides a comprehensive account of the design and development of the "DASYAM" robot, which is primarily used for interactive applications. The demand for intelligent service robots is now being met through research and development efforts. These applications include everything from entertainment to health care. These service robots require direct connection with their human users in order to perform their service activities and are designed for non-expert people to operate. Therefore, such service robots are typically preferred to have interaction elements that are friendly to humans. New robotic application fields have been made possible by recent advancements in intelligent service robotics, including healthcare, rehabilitation, childcare, assistance, education, and entertainment. The review's findings—possible improvements—have been offered after a thorough investigation and analysis of the available literature to determine the shortcomings of the current methodologies. The work presented here is the mini-project work of the 2nd sem students of electronics & communication engineering department of dayananda sagar college of engg., bangalore.

The effect of service robot occupational gender stereotypes on customers' willingness to use them.

Customers have obvious occupational gender stereotypes for service employees. In recent years, intelligent service robots have been widely used in the hospitality industry and have also been given gender characteristics to attract customers to use them. However, whether and when the usage of gendered service robots is effective remains to be explored. This research focuses on customers' occupational gender stereotypes and the gender of service robots, examining the influences of their consistency on customers' willingness to use service robots through three scenario studies. The findings suggest that: (1) The consistency between occupational gender stereotypes and service robot gender positively affects customers' willingness to use service robots. (2) Performance expectancy and trust are two psychological mechanisms underlying the above effect. (3) In the context of service failures, the consistency backfires and brings negative effects on willingness to use. This research extends the literature on customers' acceptance of anthropomorphized robots from the perspective of gender stereotypes and identifies the mechanisms behind the stereotype consistency effect. For practical implications, hotels should design and deploy gendered robots consistent with corresponding occupational gender stereotypes.