A Review of Human Intention Recognition Frameworks in Industrial Collaborative Robotics

The integration of intention recognition systems in industrial collaborative robotics is crucial for improving safety and efficiency in modern manufacturing environments. This review paper looks at frameworks that enable collaborative robots to understand human intentions. This ability is essential for providing effective robotic assistance and promoting seamless human–robot collaboration, particularly in enhancing safety, improving operational efficiency, and enabling natural interactions. The paper discusses learning techniques such as rule-based, probabilistic, machine learning, and deep learning models. These technologies empower robots with human-like adaptability and decision-making skills. It also explores cues for intention recognition, categorising them into physical, physiological, and contextual cues. It highlights how implementing these various sensory inputs sharpen the interpretation of human intentions. Additionally, the discussion assesses the limitations of current research, including the need for usability, robustness, industrial readiness, real-time processing, and generalisability across various industrial applications. This evaluation identifies future research gaps that could improve the effectiveness of these systems in industrial settings. This work contributes to the ongoing conversation about the future of collaborative robotics, laying the foundation for advancements that can bridge the gap between human and robotic interactions. The key findings point out the significance of predictive understanding in promoting safer and more efficient human–robot interactions in industrial environments and provide recommendations for its use.