From Neurocognitive Functions and Individual Differences to AI-Driven Human-Robot Collaboration

This study explores the role of individual cognitive and emotional differences in typical industrial tasks, such as manual assembly, providing insights for AI-driven human-robot collaboration (HRC). Results indicate that cognitive functions such as planning, decision-making, and selective attention significantly impact task performance, while stress, anxiety, and negative mood states heighten perceived workload. Key workload dimensions, such as performance and frustration, were particularly affected. In addition, higher cognitive flexibility and assembly familiarity reduced workload perception. These findings suggest that AI-enhanced cobots should integrate real-time cognitive and emotional assessments to dynamically adapt support strategies based on operator needs. Future research should incorporate neurophysiological measures to refine AI models, enhancing both efficiency and user experience in industrial HRC, by fostering a more intuitive and personalized HRC.