Dynamic workload reallocation for human–robot teams based on real-time stress analysis

Abstract As artificial intelligence grows, human–robot collaboration becomes more common for efficient task completion. Effective communication between humans and AI-assisted robots is crucial for maximizing collaboration potential. This study explores human–robot interactions, focusing on the differing mental models used by humans and collaborative robots. Humans communicate using knowledge, skills, and emotions, while robotic systems rely on algorithms and technology. This communication disparity can hinder productivity. Integrating emotional intelligence with cognitive intelligence is key for successful collaboration. To address this, a communication model tailored for human–robot teams is proposed, incorporating robots’ observation of human emotions to optimize workload allocation. The model’s efficacy is demonstrated through a case study in an SAP system. By enhancing understanding and proposing practical solutions, this study contributes to optimizing teamwork between humans and AI-assisted robots.