This paper presents an in-depth exploration of Human-Robot Collaborative Systems (HRCSs) within industrial environments, a dynamic field that has witnessed significant advancements due to technological innovation and the increasing integration of Artificial Intelligence (AI). As industries evolve towards more collaborative and adaptive manufacturing systems, the dynamic interaction between humans and robots becomes pivotal. This study reviews the current state of HRCSs, focusing on the challenges of task allocation and skill alignment, safety, trust, and the psychological wellbeing of human workers. We review control strategies and architectural frameworks that underpin effective human-robot interactions (HRI), emphasising the critical role of AI in enhancing decision-making processes and the adaptability of collaborative efforts. Our review sheds light on the complexities involved in designing HRCSs that are not only efficient but also cognisant of the human experience, advocating for a balanced approach that leverages the strengths of both human and robotic counterparts. We argue that research in and implications of HRCSs should extend beyond technical considerations, touching on ethical, social, and organisational dimensions, thereby contributing to the broader discourse on the future of work in the era of Industry 4.0 and future Industry 5.0.

As artificial intelligence and robotics increasingly shape societies, ensuring that these technologies align with ethical and societal values is a pressing challenge. This paper presents survey findings from 98 MSc Robotics and Applied AI students at Cranfield University, offering rare empirical evidence of how future AI and robotics professionals perceive their ethical responsibilities. While students demonstrate strong awareness of key risks such as autonomous decision-making in warfare, surveillance, labour displacement, and emotional manipulation, they show limited engagement with professional codes of ethics or structured training. Instead, ethical reflection often occurs informally, through peer discussions or media exposure. These findings highlight a consistent gap between ethical awareness and institutionalised engagement, raising questions about how future engineers will navigate the ethical challenges of AI. To address this, the paper proposes an “ethics up front” model for ethics integration that embeds reflection early in the development lifecycle, supported by participatory design, professional education, and regulatory alignment. This paper provides empirical evidence on future AI engineers’ ethical orientations and proposes a practical model for early-stage ethics integration into the practice of AI and robotics engineering.

The presentation aims to show an example of the use of AI in the positioning of semiconductor elements with accuracy at the micrometer level. The stand will be used in the production process of infrared detectors. It was shown how AI will support humans in decision-making and improve the production process, which was previously fully manual. The impact on the individuals in terms of physical and psychological wellbeing are also described.

In recent years, AI-enabled technologies have become an integral part of our daily lives. While industries such as finance, healthcare, and logistics have rapidly adopted AI-driven solutions, the manufacturing sector has approached this transition more cautiously. The integration of AI-enabled human-robot interaction (HRI) in manufacturing presents opportunities and challenges impacting workforce sustainability, ergonomics, user acceptance, and ethical deployment. This qualitative study employed operator engagement workshops and semi-structured interviews to identify critical operational and safety concerns in powder handling for beverage production. Key findings revealed significant ergonomic issues, notably physical strain and airborne dust exposure, prompting recommendations for adaptive robotic systems and real-time monitoring sensors to enhance operator comfort and safety. User acceptance emerged as essential but context-specific, driven by mandated interactions and reliant on trust built through transparent communication and standardized training. Ethical concerns focused on transparency, fairness, and privacy, particularly the balance between effective surveillance and respecting worker privacy. Additionally, workforce skill sustainability requires comprehensive training to address emerging roles. The study concludes that a multidisciplinary, human-centered approach is vital for successful, ethical, and sustainable AI integration into manufacturing environments

The UK is the twelfth-largest manufacturing nation globally, yet its adoption of digital manufacturing technologies (DMTs) lags behind other European countries. In an era where industrial automation and digital transformation are essential for maintaining competitiveness, understanding the human factors influencing the acceptance and implementation of these technologies is critical. This study examines the perceptions of 313 UK manufacturing employees regarding the usefulness, ease of use, and workplace impact of DMTs. Findings indicate that while employees recognise the potential benefits of DMTs such as increased productivity, improved product quality, and enhanced competitiveness, concerns remain regarding ease of use, workforce upskilling, and physical interaction with new technologies. Notably, employees with lower educational qualifications expressed greater scepticism about the applicability of DMTs. Furthermore, those working in companies that had already implemented digital technologies reported more positive perceptions compared to non-users, emphasising the role of experience in shaping attitudes. The study highlights the need for targeted training and change management strategies to facilitate smoother workforce adaptation to digital advancements. These findings provide insights for policymakers, industry leaders, and system designers aiming to integrate human-centric approaches in the transition to Industry 4.0 and beyond.

Understanding human needs and the ways that workplace design can effectively address these needs is fundamental for improving the productivity and sustainability of a workforce. As automation increasingly replaces manual tasks in advanced manufacturing, it is crucial that human factors are considered throughout the stages of design and implementation. Initiatives like the EU AI-PRISM project aim to integrate human-centred design into new AI-based automation solutions to foster effective collaboration between humans and robots in challenging automated environments. AI-PRISM seeks to enhance productivity by combining advanced technology with human skills. To ensure that these automated solutions also meet the needs of their human operators and adhere to ethical standards, it is essential to incorporate human-centred design throughout both the design and implementation phases. The study described in this paper aims to identify the task-related challenges faced by current operators-from novices to experts-in four of the project's use cases in manufacturing companies. To achieve this, four participatory design workshops were conducted to gather valuable feedback on the planned AI-PRISM automation solutions and capture operator requirements across different manufacturing sectors: wooden furniture, photonics and microelectronics, white goods and brewery. The workshops incorporated group interviews, lasting about one hour each, that provided a platform for operators to share their experiences, concerns, and suggestions regarding the integration of AI and robotics into their daily tasks. The results revealed that operators across all groups faced challenges with fault detection, precision, and repetitive tasks. Responses also included a number of recommendations for the design of AI-based automation to address these challenges including enhanced interaction interfaces, human oversight for precision tasks and robotic assistance. In conclusion, the findings from this research highlight the importance of aligning human skills with automated technologies, which can lead to a more efficient and effective future for manufacturing.

The following paper presents a study that investigates the existence of a skill gap for operators of Industry 5.0. This study is part of the CONVERGING project, a project that aims to develop and deploy smart-production systems for different industrial use cases. In each use case, a manually performed task is being redesigned as a smart-production system that leverages collaboration between human operators and smart machines such as collaborative robots. The current study’s aim is twofold – firstly, to identify the current skill set of the operators and the skills expected from them to perform the future roles in the smart-production systems. This was achieved through a literature review, hierarchical task analysis and task decomposition. The second aim is to assess whether there is a gap between operators’ current skills and the skills expected from them for performing roles in the future systems. To meet the second aim, an online survey was developed using the list of skills already identified for the first aim, and the survey was administered to use case supervisors and managers. The following paper details the methodology for skill identification and survey development and the results from the survey are discussed.

Distress and agitation are predictors of entry into long-term care and health inequalities (Schulz et al., 2004, Weir et al., 2022). Physiological data has been shown to reliably predict distress (Goodwin et al., 2019), yet wearable devices have low acceptance rates (Koumpouros & Kafazis, 2019). The current study discusses findings from a multifaceted approach investigating the detection of early signs of distress via physiological sensors in a foot-worn device. Firstly, the acceptance and concern ratings for a foot-worn device, SmartSocks, wrist-worn devices, Empatica E4 and Shimmer GSR+, and chest-worn device, Equivital within a healthy population (N = 10) were assessed with a self-report questionnaire. Secondly, data accuracy between Shimmer ECG and Polar OH1+ was compared within a healthy population (N = 12) in a standing, sitting and supine position. Finally, an ongoing ecologically valid feasibility trial (N = 2) involving participants with dementia or a learning disability is assessing the reliability of physiological data and AI-detected stress from SmartSocks relative to subjective ratings of distress, the Abbey Pain Scale (APS), and the Neuropsychiatric Inventory (NPI). Firstly, the SmartSocks received lowest concern ratings compared to wrist- and chest-worn devices (1.64 vs <1.71). Secondly, the accuracy of SmartSocks pulse rate (PR) estimates obtained using photoplethysmography (PPG) in combination with the delineator algorithm was determined by comparing estimates to a Shimmer 1-lead ECG, recording Mean Absolute Error (MAE)<5bpm at 64HZ for participants in a supine position (Fig. 1). This led to the development of new features for classifying PPG signal quality using neural networks, achieving approximately 95% accuracy. Finally, the initial stage of the feasibility trial indicated APS and NPI scores were lower after the participant with dementia wore SmartSocks for two weeks. Physiological data collected from the participant with a learning disability using SmartSocks showed moderate correlation ( χ 2 = 0.45) between the reported and AI-detected stress over the day (Fig. 2 & 3). Early findings suggest SmartSocks are more comfortable than comparable wrist- and chest-worn devices, and validity of the data is comparable to other devices. Preliminary data obtained from people with dementia and learning disabilities suggest SmartSocks are capable of detecting distress to alleviate user discomfort.

This paper aims to show the efforts of the AI-PRISM Horizon Project for Semiconductors Pilot. Pilot is an example of the microscale positioning of semiconductor chips supported by AI. Usage of the stand is verified in the production environment. The second iteration of the assembly station is an effect of social aspect analysis. The collaboration and human acceptance of AI decision-making is validated in the infrared detectors production facility.

Industry 5.0 is characterized by human-centric designs and solutions. This implies that the creation and implementation of new technologies in industrial tasks should involve and engage the workforce at every stage of development. An effective method of workforce engagement are co-creation workshops - collaborative sessions designed to understand the needs and preferences of the end-users of new technology. Operator engagement through such workshops can provide valuable feedback for the design of new technology as well as boost operators’ acceptance of the new smart production systems. The following paper presents the methodology and findings from co-creation workshops conducted by the EU-funded project “CONVERGING”, in four use cases with factory operators. The workshops enforced a three-step protocol combining discussions and hands-on design tasks with the industrial operators. The workshops aimed to capture the operators’ experiences with the current task, expectations from smart assistive technologies, and their opinions on the CONVERGING solutions. The findings that emerged from the workshops are discussed for each use-case.

Skill acquisition in the manufacturing industry is a crucial aspect of optimising performance, efficiency, and safety in complex work environments. Human factors play a significant role in skill acquisition, encompassing factors such as cognitive processes, perception, decision-making, and physical interactions within the work environment. Eye tracking data has emerged as a valuable tool for studying skill acquisition (Toker et al., 2014) in the manufacturing industry, offering insights into workers' visual attention, cognitive strategies, and task performance. Currently, in manufacturing settings, skill acquisition involves the mastery of various tasks, from operating machinery and assembly line processes to quality control and troubleshooting. Effective skill acquisition is essential for ensuring consistent product quality, minimising errors, and maximizing productivity. Human factors, including attentional processes, perception, and decision-making, influence how workers acquire and apply new skills in these dynamic and often high-pressure environments (Mark et al., 2020).The aim of the current research is to understand the duration required to acquire skills through procedural learning and the transition to routine development occurs when leaned behaviour becomes habitual and routine. Furthermore, the research also aims to understand how mental and physical fatigue impact their performance with manual quality control tasks. The study aims to showcase preliminary results regarding human factors and performance variations. Participants completed an inspection task that involved an industrial component (monitor) for their serial number, visual and tactile quality under a control condition: control (no manipulation) measuring physical demand and stress levels during each monitor inspection. Physiological measures were captured using a Empatica E4 wristband (capturing electrodermal activity (EDA), heart rate, skin temperature) and eye tracking was performed with Tobii Glasses 3, as well as subjective measures of performance via NASA TLX. The results from the physiological data show that the initial 10 minutes of the task showed a positive significant correlation between EDA and NASA Performance score (Spearman rho = .675, p =.016), the second set of 10 minutes positively correlated EDA and NASA TLX temporal demands (Spearman rho = .757, p =.004), while final 10 minutes showed a positive correlation between EDA and NASA TLX physical demand (Spearman rho = .639, p =.025). Such results indicate that skill acquisition over time goes through several stages – individual's anxiety of their performance, then concerns for timely performance, and finally experiencing physical impact – as well as that EDA is good indicator of changing workload demands.The current study will present the findings of the stress and physical discomfort levels, physiological data and preliminary eye tracking data whereby results found that for the initial inspection of the first two components participants move between the instructions for the inspection and the actual inspection, however, analysis of the third component and further components revealed that attention is mainly focused on the component inspection with little fixations towards the instructions. There was also an increase in the development of skill acquisition whereby by participants displayed a decrease in error count (from 1 to 0 over the inspection of 10 monitors), instruction analysis and increase in time of task completions as the average time reduced from 6 minutes 07 seconds for the first component to 1 minute 51 seconds for the last component. (The results from the eye tracking data reveal participants’ gaze patterns during the experiment highlighting the time spent on task comprehension and completion suggesting the skill was acquired during the experimental task. In summary, the current study demonstrates the development of skills through a variety of measures showing temporal progression, mental demand and the physicality of manual handling tasks is a complex process influenced by various human factors, including attention, fatigue, and tactile knowledge. By leveraging physiological data, eye tracking and stress levels the research provides scope into how human skill acquisition is progressive through various factors that can reduce overtime to upkeep and maintain the skills of workers perform in the manufacturing industry.

Over the last years both Research and Industry have allocated significant effort to address the requirement for flexible production by introducing technologies that allow humans and robots to coexist and share production tasks safely. The human-robot coexistence and collaboration in cageless environments introduces challenges that are both of technical and social nature. A human robot collaborative system can be viewed as a sociotechnical work system that can both increase production KPIs and improve the quality of life for people working in HRC environment. In addition, effective HRC requires acceptance and trust from the side of the operators and clear communication between operators and robots. This work presents a software architecture approach that enables personalized and human-centric HRC and aims to improve ergonomics, cognitive factors and acceptance. In this context, key technology solutions for different HRC aspects such as training, design and production are also presented.

Human labour has always been essential in manufacturing and, still, no machine or robot can replace innate human complex physical (dexterity) and cognitive (reasoning) skills. Understandably, industry has constantly sought new automation technologies and largely only concerned itself with physical health and safety issues to improve / maintain production processes, but these industrial engineering approaches have largely overshadowed our understanding of wider social and emotional issues that can also significantly impact on human-system performance and wellbeing. In the current climate, industrial automation is rapidly increasing and crucial to manufacturing competitiveness, and requires greater, closer human interaction. Consequently, people’s cognitive-affective abilities have never been more critical and there has never been a more important time to thoroughly understand them. Moreover, industrial engineers are themselves now more aware and interested in understanding how people can better perform tasks in collaboration with intelligent automation and robotics. This paper describes why industry is only now realising the need for psychology, how far research has advanced our knowledge, and how a major UK project is working to develop new human behaviour models to improve effectiveness in the design of human-robot interactions in modern production processes. As one recent anecdotal comment from a UK industrialist set out: “we don’t need ergonomics anymore – our industrial engineers can do that, we need psychology”!

With the start of Industry 5.0, there is greater emphasis on increased workforce sustainability. Manufacturing among other industries realised the economic importance not only of increased production efficiency, but the positive impact physical and psychological workforce wellbeing has on the company. The current paper presents a three-step approach of engaging multicultural end users for robotic technology introduction in the manufacturing where language dependent knowledge capture is challenging. The first step is video analysis of the process to determine which human factors might be key contributors to the existing processes. The second proposed step is process observation while the operators wear eye tracking glasses combined with several questions for the process clarification. This step allows to determine decision making points and visual attention sequence. Finally, a focus group conducted with small group of representative operators. The paper will introduce the use cases and protocol to achieve a two-fold aim: (i) feedback to the technology developers and engineers, the user critical aspects of the existing aspects, and (ii) to increase user acceptance and engagement with the developing technology/processes. The user acceptance and engagement with the final solution is expected to be improved due to the proposed three step engagement program delivered at the start of the project.

There is an increasing interest in considering, measuring, and implementing trust in human-robot interaction (HRI). New avenues in this field include identifying social means for robots to influence trust, and identifying social aspects of trust such as a perceptions of robots’ integrity, sincerity or even benevolence. However, questions remain regarding robots’ authenticity in obtaining trust through social means and their capacity to increase such experiences through social interaction with users. We propose that the dyadic model of HRI misses a key complexity: a robot’s trustworthiness may be contingent on the user’s relationship with, and opinion of, the individual or organisation deploying the robot (termed here, Deployer). We present a case study in three parts on researching HRI and a LEGO ® Serious ® Play focus group on care robotics to indicate how Users’ trust towards the Deployer can affect trust towards robots and robotic research. Our Social Triad model (User, Robot, Deployer) offers novel avenues for exploring trust in a social context.

As part of the Industry 4.0 movement, the introduction of digital manufacturing technologies (DMTs) poses various concerns, particularly the impact of technology adoption on the workforce. In consideration of adoption challenges and implications, various studies explore the topic from the perspective of safety, socio-economic impact, technical readiness, and risk assessment. This paper presents mixed methods research to explore the challenges and acceptance factors of the adoption of human-robot collaboration (HRC) applications and other digital manufacturing technologies from the perspective of different stakeholders: from manufacturing employees at all levels to legal experts to consultants to ethicists. We found that some of the prominent challenges and tensions inherent in technology adoption are job displacement, employee’s acceptance, trust, and privacy. This paper argues that it is crucial to understand the wider human factors implications to better strategize technology adoption; therefore, it recommends interventions targeted at individual employees and at the organisational level. This paper contributes to the roadmap of responsible DMT and HRC implementation to encourage a sustainable workforce in digital manufacturing.

Robots and automated technology are still being perceived as threatening by operators in manufacturing.The current study investigates how participants' feedback and evaluation of the user interfaces guide the final design.The initial development of the Augmented Reality (AR) guided assembly was completed over several stages; however, the current study focuses on testing the product after the initial comments on the AR demonstrator from the users were integrated.The work discussed here was completed over three steps: (i) An online survey where participants (N = 21) viewed pre-recorded videos of an experimenter completing the task and answered questions related to clarity and usability of the AR glasses; (ii) A trial experience where participants (N = 5) were invited to the lab to complete the task using the proposed solution, and (iii) experimental study where participants (N = 10) interacted with a robot via the developed AR guided instructions in two conditions where robot communicated its next steps (the transparency condition) and where it did not communicate its next steps (the neutral condition).The comparison between step (i) and (ii) provides information not only between two development points of the AR glasses guided instructions, but also estimates how at hand experience (trial experience and interview) influences the perception of the technology compared to distance experience (online survey).Finally, the experimental study indicates that robot behaviour transparency can affect experienced lower physical demand when the user knows the robot's next steps.The current paper will address issues of the development and introduction of technology while incorporating the users.

Collaborative robots offer opportunities to increase the sustainability of work and workforces by increasing productivity, quality, and efficiency, whilst removing workers from hazardous, repetitive, and strenuous tasks. They also offer opportunities for increasing accessibility to work, supporting those who may otherwise be disadvantaged through age, ability, gender, or other characteristics. However, to maximise the benefits, employers must overcome negative attitudes toward, and a lack of confidence in, the technology, and must take steps to reduce errors arising from misuse. This study explores how dynamic graphical signage could be employed to address these issues in a manufacturing task. Forty employees from one UK manufacturing company participated in a field experiment to complete a precision pick-and-place task working in conjunction with a collaborative robotic arm. Twenty-one participants completed the task with the support of dynamic graphical signage that provided information about the robot and the activity, while the rest completed the same task with no signage. The presence of the signage improved the completion time of the task as well as reducing negative attitudes towards the robots. Furthermore, participants provided with no signage had worse outcome expectancies as a function of their response time. Our results indicate that the provision of instructional information conveyed through appropriate graphical signage can improve task efficiency and user wellbeing, contributing to greater workforce sustainability. The findings will be of interest for companies introducing collaborative robots as well as those wanting to improve their workforce wellbeing and technology acceptance.