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

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 ment...

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Bibliographic Details
Published inAI EDAM Vol. 39
Main Authors Kirgil-Budakli, Rukiye, Zeng, Yong, Akgunduz, Ali
Format Journal Article
LanguageEnglish
Published New York, USA Cambridge University Press 22.07.2025
Subjects
Artificial intelligence
Case studies
Collaboration
Communication channels
Effectiveness
Emotions
Human performance
Knowledge management
Learning
Literature reviews
Optimization
Productivity
Real time
Research Article
Robots
Stress analysis
Teams
Workload
Workloads
stress and work performance
task reallocation
controlling stress level
human–robot collaboration
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Summary: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.
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ISSN:0890-0604
1469-1760
DOI:10.1017/S0890060425100073