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The Use of Artificial Intelligence & Synthetic Actors for Personalized Learning in Manufacturing Collaborative Robotics
This research focuses on advancing personalized training strategies for complex collaborative robotic manufacturing assembly tasks. An interdisciplinary framework is employed, integrating cognitive science, manufacturing, spatial computing, human factors, artificial intelligence, and advanced robotics to create an innovative, personalized learning paradigm. This paradigm is customized to each trainee’s cognitive and sensorimotor capabilities, aiming to maximize the effectiveness of training transfer. The core challenge addressed is the accurate interpretation of physiological data and its translation into real-time training modifications with the help of machine learning algorithms. This effort emphasizes the importance of understanding the complex interplay between physiological data, sensorimotor interactions, and cognitive processes. The main research questions are: How can personalized training frameworks incorporating cognitive function, sensorimotor interaction, and machine learning improve the efficiency and effectiveness of workers in collaborative robotic manufacturing environments? What are the impacts of integrating synthetic actors and wearable physiological monitoring on cognitive workload and task performance in learning transfer for complex manufacturing tasks? How does real-time adaptive instruction driven by AI and biometric feedback influence human workers’ safety, efficiency, and satisfaction in collaborative robotic training settings? The insights gained are expected to enhance training methodologies, ultimately fostering a more capable and adaptable workforce equipped to navigate the future of complex manufacturing environments.