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The purpose of this study is to analyze the robot trends of the next generation.

This paper is divided into two sections: the key modern technology on which Europe's robotics industry has built its foundation is described. Then, the next key megatrends were analyzed.

Artificial intelligence (AI) and robotics are technologies of major importance for the development of humanity. This time is mature for the evolution of industrial and service robots. The perception of robot use has changed from threading to aiding. The cost of mass production of technological devices is decreasing, while a rich set of enabling technologies is under development. Soft mechanisms, 5G and AI have enabled us to address a wide range of new problems. Ethics should guide human behavior in addressing this newly available powerful technology in the right direction.

The paper describes the impact of new technology, such as AI and soft robotics. The world of work must react quickly to these epochal changes to enjoy their full benefits.

The aim of the study is to conduct a thematic review of assessment tools and types of activities in the application of robotics to autistic students during the period 1996–2021 using Web of Science and Scopus. The sample consisted of 119 documents.

Nowadays, emerging technologies have become increasingly prevalent across various fields of knowledge. In this regard, robotics is being increasingly applied in the educational environment. The characteristics of this tool are the ones that best suit the needs of autistic students.

Results reveal that 16.80% of the studies used automatic measurement systems, whilst 15.96% of the studies used user observation and recording techniques. As for the tasks, 37.80% were focussed on imitation tasks. Amongst the practical implications is the need to include tasks that could be developed collaboratively in the regular classroom.

With this research, it is intended to disseminate in the scientific community what are the characteristics that should have the interaction activities between a robot and autistic students. In addition, the type of tool needed to evaluate the improvements in the interaction is proposed.

Drawing on the cognitive–affective–conative framework, this study aims to develop a model of service robot acceptance in the hospitality sector by incorporating both cognitive evaluations and affective responses.

A mixed-method approach combining qualitative and quantitative methods was used to develop measurement and test research hypotheses.

The results show that five cognitive evaluations (i.e. cuteness, coolness, courtesy, utility and autonomy) significantly influence consumers’ positive affect, leading to customer acceptance intention. Four cognitive evaluations (cuteness, interactivity, courtesy and utility) significantly influence consumers’ negative affect, which in turn positively affects consumer acceptance intention.

This study provides significant implications for the design and implementation of service robots in the hospitality and tourism sector.

Different from traditional technology acceptance models, this study proposed a model based on the hierarchical relationships of cognition, affect and conation to enhance knowledge about human–robot interactions.

Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model.

This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found.

Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy.

An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.

This study investigates human behavior, specifically attitude and anxiety, toward humanoid service robots in a hotel business environment.

The researcher adopted direct observations and interviews to complete the study. Visitors of Henn-na Hotel were observed and their spatial distance from the robots, along with verbal and non-verbal behavior, was recorded. The researcher then invited the observed hotel guests to participate in a short interview.

Most visitors showed a positive attitude towards the robot. More than half of the visitors offered compliments when they first saw the robot receptionists although they hesitated and maintained a distance from them. Hotel guests were also disappointed with the low human–robot interaction (HRI). As the role of robots in hotels currently remains at the presentation level, a comprehensive assessment of their interactive ability is lacking.

This study contributes to the HRI theory by confirming that people may treat robots as human strangers when they first see them. When a robot's face is more realistic, people expect it to behave like an actual human being. However, as the sample size of this study was small and all visitors were Asian, the researcher cannot generalize the results to the wider population.

Current robot receptionist has limited interaction ability. Hotel practitioners could learn about hotel guests' behavior and expectation towards android robots to enhance satisfaction and reduce disappointment.

Prior robot research has used questionnaires to investigate perceptions and usage intention, but this study collected on-site data and directly observed people's attitude toward robot staff in an actual business environment.

Drawing on anthropomorphism and the stereotype content model, this study aims to explore the impacts of robot anthropomorphism on hotel guests’ word of mouth and continuous usage intention through perceived warmth and competence. This study also investigates the moderating effects of guests’ expertise.

A total of 524 valid data was collected with Chinese hotel guest through an online survey. Data were analyzed by using structural equation modeling.

The results indicate that robot anthropomorphism positively affects perceived warmth and competence. Perceived warmth positively affects guests’ word of mouth and continuous usage intention, while perceived competence only positively influences guests’ continuous usage intention. The findings further show that guest expertise weakens the positive relationship between robot anthropomorphism and perceived warmth and competence.

This study advances robot service literature by integrating the stereotype content model with robot anthropomorphism in exploring hotel guests’ perceptions on service robots. This study also discovers the vital boundary role of guest characteristics in human–robot interaction. This study provides valuable information for hoteliers to design and adopt better robots for optimal guest experiences.

基于拟人化和刻板印象内容模型, 本研究探讨了机器人拟人化通过感知温暖和感知能力对酒店客人的口碑和持续使用意向的影响。此外, 本研究也探讨了客人专业知识的调节作用。

通过在线调查问卷, 我们共收集了来自中国酒店客人的524份有效数据, 并采用结构方程模型对这些数据进行了分析。

研究结果表明, 机器人拟人化积极影响感知温暖和感知能力。感知温暖会对客人的口碑和持续使用意向产生积极影响, 而感知能力只对客人的持续使用意向产生积极影响。研究结果进一步表明, 客人的专业知识会削弱机器人拟人化与感知温暖和能力之间的积极关系。

为了探索酒店客人对服务机器人的感知（状况/情况）, 本研究将机器人拟人化因素整合到刻板印象内容模型中, 其结果推动了机器人服务研究的发展。本研究还发现了客人特征在人机互动中的重要边界作用。本研究为酒店经营者设计和采用更好的机器人以获得最佳的客人体验提供了有价值的参考。

Se recopiló datos válidos de un total de 524 clientes de hoteles chinos mediante una encuesta en línea. Los datos fueron analizados mediante un modelo de ecuaciones estructurales.

Basándose en el antropomorfismo y en el modelo de contenido estereotipado, este estudio explora el impacto del antropomorfismo de los robots en el boca a boca de los clientes del hotel y en la intención de uso continuado a través de la calidez y la competencia percibidas. El estudio investiga también los efectos moderadores de la experiencia de los huéspedes.

Los resultados indican que el antropomorfismo del robot afecta positivamente a la calidez y a la competencia percibidas. La calidez percibida influye positivamente en el boca a boca y en la intención de uso continuado de los huéspedes, mientras que la competencia percibida sólo influye positivamente en la intención de uso continuado de los huéspedes. Los resultados muestran, además, que la experiencia del cliente debilita la relación positiva entre el antropomorfismo del robot y la percepción de calidez y de competencia.

Este estudio supone un avance en la literatura sobre robots de servicio al integrar el modelo de contenido estereotipado con el antropomorfismo de los robots, en la exploración de las percepciones de los huéspedes de hoteles sobre los robots de servicio. Este estudio también muestra el papel vital que desempeñan las características de los huéspedes en la interacción humano-robot. El estudio proporciona información valiosa para que los hoteleros diseñen y adopten mejores robots para ofrecer experiencias óptimas a los huéspedes.

The current difficulties of distribution network working robots are mainly in the performance and operation mode. On the one hand, high-altitude power operation tasks require high load-carrying capacity and dexterity of the robot; on the other hand, the fully autonomous mode is uncontrollable and the teleoperation mode has a high failure rate. Therefore, this study aims to design a distribution network operation robot named Sky-Worker to solve the above two problems.

The heterogeneous arms of Sky-Worker are driven by hydraulics and electric motors to solve the contradiction between high load-carrying capacity and high flexibility. A human–robot collaborative shared control architecture is built to realize real-time human intervention during autonomous operation, and control weights are dynamically assigned based on energy optimization.

Simulations and tests show that Sky-Worker has good dexterity while having a high load capacity. Based on Sky-Worker, multiuser tests and practical application experiments show that the designed shared-control mode effectively improves the success rate and efficiency of operations compared with other current operation modes.

The designed heterogeneous dual-arm distribution robot aims to better serve distribution line operation tasks.

For the first time, the integration of hydraulic and motor drives into a distribution network operation robot has achieved better overall performance. A human–robot cooperative shared control framework is proposed for remote live-line working robots, which provides better operation results than other current operation modes.

The purpose of this paper is to design a new trajectory error compensation method to improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction motion.

A trajectory error compensation method based on admittance-extended Kalman filter (AEKF) error fusion for human–exoskeleton interaction control. The admittance controller is used to calculate the trajectory error adjustment through the feedback human–exoskeleton interaction force, and the actual trajectory error is obtained through the encoder feedback of exoskeleton and the designed trajectory. By using the fusion and prediction characteristics of EKF, the calculated trajectory error adjustment and the actual error are fused to obtain a new trajectory error compensation, which is feedback to the knee exoskeleton controller. This method is designed to be capable of improving the trajectory tracking performance of the knee exoskeleton and enhancing the compliance of knee exoskeleton interaction.

Six volunteers conducted comparative experiments on four different motion frequencies. The experimental results show that this method can effectively improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction.

The AEKF method first uses the data fusion idea to fuse the estimated error with measurement errors, obtaining more accurate trajectory error compensation for the knee exoskeleton motion control. This work provides great benefits for the trajectory tracking performance and compliance of lower limb exoskeletons in human–exoskeleton interaction movements.

Robotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.

This study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.

Experiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.

A new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

The concept of ubiquitous assimilation in e-commerce, denoting the seamless integration of technologies into customer shopping experiences, has played a pivotal role in aiding e-satisfaction and, consequently, fostering patronage intention. Among these, text-based chatbots are significant innovations. In light of this, the paper aims to develop a conceptual framework and comprehend the patronage behaviour of artificial intelligence-enabled chatbot users by using chatbot usability cues and to determine whether the social presence and flow theories impact e-satisfaction, which leads to users’ patronage intention. The current research provides insights into online travel agencies (OTAs), a crucial segment within the travel and tourism sector. Given the significance of building a loyal clientele and cultivating patronage in this industry, these insights are of paramount importance for achieving sustained profitability and growth.

The research framework primarily focused on the factors that precede e-satisfaction and patronage intention among chatbot users, which include social presence, flow, perceived anthropomorphism and need for human interaction. The researchers collected the data by surveying 397 OTA chatbot users by using an online questionnaire. The data of this cross-sectional study were analysed using covariance-based structural equation modelling.

Findings reveal that e-satisfaction is positively linked with patronage intention and the variables of social presence and flow impact e-satisfaction along with chatbot usability cues. There were direct and indirect relations between chatbot usability and e-satisfaction. Moreover, the personal attributes, “need for human interaction” and, “perceived anthropomorphism” were found to moderate relations between chatbot usability cues, social presence and flow.

The impact of chatbot’s usability cues/attributes on e-satisfaction, along with perceived attributes – social presence and flow in the realm of OTAs contributes to the human–chatbot interaction literature. Moreover, the interacting effects of perceived anthropomorphism and the need for human interaction are unique in the current contextual relations.