In the new industrial contexts, the workers’ well-being is the central pillar. Therefore, research on methods and technics to improve the workers’ user experience in a human–robot collaborative environment is necessary. While the effects of kinematic variables, such as speed and acceleration, on human safety have been extensively studied, their impact on human perception has not been fully explored. This study investigates the effects of the robot’s speed and acceleration on humans. An experimental research approach was used, where 20 participants (10 women and 10 men) performed an assembly task collaborating with a robot. An experiment was defined with two procedures, and the participants were evenly distributed: in the first experiment, the participants started by performing the task at a slow robot speed and then performed the same task at a faster speed. In the second experiment, the other half followed the opposite procedure. Key Performance Indicators (KPIs), physiological values (via EEG and EDA), and perceptual values (using the standardised UEQ-S questionnaire) were collected. The results showed that the robot’s speed and acceleration impact the task’s completion time and participants’ emotional responses. Our results lead to a new concept, “HRI speed bell”, which indicates that it is necessary to investigate the optimal speed and acceleration to ensure good trust and perceived safety. Furthermore, the task sequence also influences participants’ expectations and performance. Finally, the results are examined according to gender perspective.

Industry 5.0, the latest evolution in industrial processes, builds upon the principles of Industry 4.0 by emphasizing human-centric approaches and the integration of virtual reality technologies. This paradigm shift underscores the importance of collaboration between humans and advanced technologies with a focus on optimizing efficiency, safety, and worker skill development. Based on the PRISMA 2020 guidelines, this study conducts a systematic literature review, identifying 328 papers from databases. After applying inclusion and exclusion criteria, 24 papers were selected for detailed analysis. The review provides valuable insights into the diverse evaluation methods employed in the literature, and a detailed classification of 29 human factors with their associated metrics. Despite the absence of a standardized method for assessing human factors in VR experiences, this comprehensive analysis of 240 different ways of measuring factors highlights the current state of evaluating human-centered VR experiences in Industry 5.0. While the review reveals some limitations such as potential bias in study selection and heterogeneity of methods, it also identifies significant research gaps and proposes future directions. This study contributes to the establishment of a coherent structure for future research and development in human-centered design within the rapidly evolving landscape of Industry 5.0, paving the way for more effective and standardized approaches in the future.

In the absence of user experience evaluation tools for industrial human–machine interfaces (HMI), a specific tool called eXperience Capturer (XC) has been created. It is a multi-method user-centred tool that evaluates the pragmatic and experiential aspects of employees’ interaction with industrial HMIs during the three phases of experience. In this article, a case study is shown where the XC tool is used in an industrial HMI design process. The results show that evaluation using the XC tool facilitates the creation of a new design that improves the experience of employees during interaction, increasing their autonomy, competence, closeness to the system, safety and stimulation.

Human-Robot Collaboration and Cognitive Robotics are two evolving fields that, while often explored separately, share common objectives in enhancing the intelligence, adaptability, and effectiveness of robotic systems within human-centric environments. Despite their shared objectives, their interrelation is not extensively explored in current literature. This systematic literature review examines the state of cognitive abilities in HRC, focusing on core functions such as perception, attention, action selection, memory, learning, and reasoning, along with the integration of social cognitive abilities. Analyzing 40 peer-reviewed studies, we found a predominant emphasis on core cognitive abilities, with social cognitive abilities significantly underrepresented, especially in industrial robotics contexts, see Figure 1. Additionally, there is a notable gap in human-centered assessment methodologies, where technical performance metrics overshadow critical human factors like user satisfaction, trust, and ergonomic considerations. Safety strategies in HRC reveal a disparity between theoretical advancements and practical implementations, highlighting the need for comprehensive safety frameworks that incorporate both physical and psychological aspects. Emerging human factors due to increased automation, such as automation bias and diminished sense of agency, further challenge effective human-robot interactions. This review emphasizes the necessity for interdisciplinary approaches and robust ethical frameworks to address these issues. Future research should evaluate the cost-benefit of developing higher-order cognitive abilities, translate insights from social robotics into industrial settings, adopt standardized human-centered evaluation methods, bridge the gap between safety theories and practice, and foster interdisciplinary collaborations to ensure the responsible integration of cognitive robotics into human-centric environments. Addressing these gaps will advance the development of intelligent, adaptive, and socially adept robotic systems that seamlessly integrate into diverse collaborative settings.

Extended reality (XR) and collaborative robots are reshaping human–robot interaction (HRI) by introducing novel control methods that enhance user experience (UX). However, human factors such as cognitive workload, usability, trust, and task performance are often underexplored. This study evaluated UX during robotic manipulation tasks under three interaction modalities: manual control, XR-based control at real-time speed (RS), and XR-based control at reduced safety speed (SS). Twenty-one participants performed a series of tasks across three scenarios, where we measured usability, workload, flow state, trust, and agency using a subjective questionnaire adapted from SUS, NASA-TLX, FSS, SoAS, and Trust in Industrial Human–Robot Collaboration Questionnaire, and objective task metrics (completion time, errors, and attempts). Our results reveal that RS-based control modes significantly reduced physical workload and improved usability compared to manual control. RS control at real-time speed enhanced task efficiency but increased error rates during complex tasks, while SS mode mitigated errors at the cost of prolonged completion times. Trust and agency remained stable across all modalities, indicating extended reality technologies do not undermine user confidence. These findings contribute to the field of human–robot collaboration by offering insights regarding efficiency, accuracy, and UX. The results are particularly relevant for industries seeking to optimize safety, productivity, and human-centric robotic systems.