Enhancing Human-Robot Collaboration in Industry 4.0 with AI-driven HRI
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Abstract
Human-robot interaction (HRI) is an important consideration in mechatronic design to ensure safe and intuitive operation of robotic systems. With advancements in artificial intelligence (AI), new opportunities have emerged to enhance HRI through learned models that can adapt to human behavior and preferences. This paper provides a comprehensive review of techniques to integrate AI into HRI for mechatronic systems. An overview is first provided of challenges and objectives in integrating intelligence into robotics for effective HRI. Modern approaches utilizing neural networks, reinforcement learning, and graph neural networks are then discussed for robotic perception, decision-making, motion control, and interaction adaptation. Additionally, hybrid approaches combining rule-based methods with learned models are highlighted. Guidelines are provided for collecting human interaction data, evaluating integrated system performance, and considering adjustability, explainability, and safety. Multiple tables summarize key studies on AI-enhanced user interfaces, interactive task learning, socially aware navigation, bio-inspired sensorimotor control, and personalized robots. Finally, open issues and future outlook are discussed. This paper aims to support mechatronic designers through an structured analysis of the emerging field of intelligent HRI with insights into current best practices for integration.