In the context of Human-Robot Interaction (HRI) within manufacturing environments, Human Error (HE) remains a critical factor affecting performance, safety, and efficiency. Understanding and categorizing these errors, essential for the design of safe and efficient collaborative work cells, still remains a research topic uncovered by the scientific literature. This study aims to fill this gap by systematically investigating human errors in HRI, offering a comprehensive review of existing classifications, their underlying causes, and the research gaps in current literature. The primary objective is to develop a preliminary taxonomy of human errors specific to HRI, which will serve as a foundation for improving the design of collaborative cells. By providing actionable insights, this taxonomy supports the optimization of both performance and safety in industrial operations. Despite these contributions, the research highlights ongoing challenges in fully grasping the complex interactions and feedback mechanisms that drive human errors. Therefore, the study calls for future research on adaptive systems, zero-shot reasoning, and enhanced feedback loops to further minimize human error in HRI settings.
The role of human error in human robot interaction
Esposito, Carmen;De Simone, Valentina;Di Pasquale, Valentina;Rinaldi, Marta;Miranda, Salvatore
2025
Abstract
In the context of Human-Robot Interaction (HRI) within manufacturing environments, Human Error (HE) remains a critical factor affecting performance, safety, and efficiency. Understanding and categorizing these errors, essential for the design of safe and efficient collaborative work cells, still remains a research topic uncovered by the scientific literature. This study aims to fill this gap by systematically investigating human errors in HRI, offering a comprehensive review of existing classifications, their underlying causes, and the research gaps in current literature. The primary objective is to develop a preliminary taxonomy of human errors specific to HRI, which will serve as a foundation for improving the design of collaborative cells. By providing actionable insights, this taxonomy supports the optimization of both performance and safety in industrial operations. Despite these contributions, the research highlights ongoing challenges in fully grasping the complex interactions and feedback mechanisms that drive human errors. Therefore, the study calls for future research on adaptive systems, zero-shot reasoning, and enhanced feedback loops to further minimize human error in HRI settings.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
