摘要
With the implementation of the Industry 4.0 concept, technologies such as artificial intelligence (AI), robotics, the industrial inter-net of things (IIoT), and cloud computing are gradually being introduced into production environments to manage the growing complexity of manufacturing. However, due to constraints such as limited engineering capabilities, high variability in operating environments, significant costs, and maintenance challenges, many manufacturing tasks remain manual or are extremely difficult to automate [1]. To address these challenges, the academic community has proposed the concept of human–robot collaboration (HRC) [2], reexamining the interactions and working relationships between humans and machines in manufacturing. This perspective shifts away from the traditional notion of replacement and elimination , emphasizing the importance of cooperation and complemen-tarity. It also highlights the importance of human-centered system design, which has become a core value of the emerging Industry 5.0 [3]. Indeed, humans and robots complement each other by leveraging their respective strengths. Humans can adapt to new situations, make judgments based on incomplete data, and apply experience-based reasoning. They also excel in abstract thinking, troubleshoot-ing unexpected issues, and interpreting ambiguous information. Physically, with dexterity and fine motor skills, humans can handle complex manual tasks that require precision, such as the assembly of delicate components, as well as tasks that demand extensive experience and craftsmanship, such as scraping. On the other hand, robots and machines perform repetitive tasks with high accuracy, minimizing errors and ensuring consistent quality. They can operate continuously without fatigue and handle physically demanding tasks beyond human capabilities. Their ability to operate in hazardous environments, such as extreme temperatures, toxic atmospheres, or high-radiation areas, reduces human exposure to risks. Together, humans and robots enhance productivity, safety, and efficiency in manufacturing. For this purpose, collaborative robots (or cobots) have emerged as an effective solution for HRC in manufacturing environments, offering advantages over traditional industrial robots. In fact, human–robot collaboration has evolved beyond its literal meaning to include the concept of collaboration between human intelligence and AI [4], also referred to as human-AI synergy [5]. This is typically realized by combining human cog-nitive abilities with AI's computational power to improve decision-making quality, efficiency, and innovation. AI processes vast amounts of data and generates insights, while humans apply reasoning, intuition, and ethical considerations to make final decisions in areas such as healthcare and finance. In design fields, AI-generated artwork, concepts, or drafts serve as a starting point or stimulus for designers to refine or modify them based on human creativity and preferences [6]. In manufacturing industries , AI enhances human intelligence by providing real-time insights and automating repetitive tasks for quality control and inspection. AI-powered vision systems inspect wafers in semiconductor manufacturing for microscopic defects and identify surface defects in automobile manufacturing, while human engineers verify critical anomalies, assess ambiguous cases, and determine rework or process adjustments [7]. Another emerging application is AI-enabled augmented reality (AR) for assisting workers in the field. An AR headset highlights faulty components or modules that may require maintenance, while human technicians leverage their expertise to troubleshoot and repair the machinery [8]. A Broader Perspective Therefore, HRC can be expanded into human–machine collaboration to acknowledge that humans and machines, including AI systems and robotics, complement each other in cognitive and physical capabilities. The cognitive aspect of human–machine collaboration focuses on how humans and AI/machines work together to process information, make decisions, and solve problems. Research issues in cognitive collaboration include explain-ability, trust, and shared situational awareness. AI's black-box nature makes interpretation challenging, requiring transparency to build human trust and enable effective collaboration [9]. Optimizing human-AI complementarity is crucial to balance AI's data-driven reasoning with human intuition and adaptability. In addition, the physical aspect of human–machine collaboration focuses on how humans and robots interact in shared work-spaces, combining physical strength, precision, and dexterity. Challenges in physical collaboration between humans and machines include safety risks, ergonomics, and task allocation. Ensuring safe interaction in shared spaces requires effective collision avoidance, emergency stops, and protective measures [10]. Designing intuitive and ergonomic interfaces is essential to minimize fatigue, reduce the risk of occupational injuries, and enhance user comfort [11]. Another key challenge is optimizing task allocation, and balancing automation and human intervention to improve efficiency while maintaining flexibility in dynamic environments [12]. The proper design of individual