A Scalable Data-Driven Methodology for Human Intention Prediction in Diverse Collaborative Scenarios

Collaborative robots were designed to work alongside humans and enable Human-Robot Collaboration in industry, but without intelligence, these robots cannot adapt, make decisions, or respond dynamically to human actions. They function more as programmable tools rather than true teammates. This study proposes a methodology to predict operators’ short-term intentions by identifying execution patterns and contextual features. Its key strength lies in its task-agnostic nature, enabling adaptation across different scenarios through a structured formalization of use-case characteristics. This flexibility allows model reconfiguration to optimize performance in various industrial applications. The predictive capability is integrated into an orchestration system, allowing the cobot to complement human actions and optimize task execution. The Intention Prediction Model was first validated in individual scenarios, demonstrating its ability to interpret human intentions in different manufacturing contexts. It was then tested with 12 participants in three collaborative scenarios, showing effective task adaptation, improved role synchronization, and task variability management, despite lower intention prediction accuracy compared to individual setups. While no collisions occurred, collaboration smoothness varied, indicating the need for advanced coordination techniques and task assignment logics based on human intention interpretation.