Pupil Responses as Indicators of Learning and Adaptation in Human-Robot Collaboration Scenarios

Abstract

The shift towards a more human-centric manufacturing approach in Industry 5.0 emphasizes the integration of technologies that augment rather than replace human capabilities, highlighting the role of collaborative robots (cobots). These cobots, designed to work closely with human operators, bring adaptability and efficiency to the manufacturing floor, adjusting to various tasks and production needs. This integration, while promising, introduces challenges, especially in terms of human adaptation and learning in dynamic work settings. To date, research has primarily focused on the technological advancement of cobots, often overlooking the human component in this collaborative equation. Our study seeks to bridge this gap by employing pupillometry to explore learning effects within human-robot collaboration (HRC), specifically examining human adaptation to complex and extended tasks reflective of industrial environments. Through a multifactorial design involving 19 participants engaged in three trials repeated for two task difficulty levels, the research analyzes performance metrics along with changes in pupil diameter. The results discovered that repetitive task execution is related to decreased operation time and pupil diameter, suggesting reduced cognitive load levels. These findings imply the potential utility of pupillometry as an indicator of human adaptation to complex task execution, promoting further investigation into physiological measures to optimize cobot integration into the workplace.