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Explainable Machine Learning and Human-Centered Robotic Systems for Collaborative Manufacturing, Rehabilitation, and Advanced Material Design: An Integrated Research Framework
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Keywords
collaborative robotics
rehabilitation robotics
explainable machine learning
high-entropy alloys
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Abstract
Background: Contemporary intelligent systems research is developing along several strongly connected but too often separately studied trajectories. One trajectory concerns collaborative robotics, including human–robot safety, hand–eye calibration, actuator testing, task allocation, and precision compensation in industrial settings (Xiahou et al., 2023; Mao et al., 2024; Shi & Cheng, 2023; Faccio et al., 2024; Liu et al., 2024). A second concerns rehabilitation and assistive robotics, where rigid–flexible interaction control and gait planning are central to embodied human support (Teng, 2021; Xiang, 2020). A third concerns machine learning–aided materials design, especially high-entropy alloys, where phase prediction, property optimization, explainability, and descriptor-driven modeling increasingly shape advanced engineering materials research (Yeh et al., 2004; Wen et al., 2019; Zeng et al., 2021; Sun & Ni, 2024; Yan et al., 2025). Additional human-centered concerns arise from digital healthcare inequalities, social-bot disorder, emotional modeling, and emotionally constrained conversational systems, all of which affect how intelligent systems are adopted, trusted, and socially integrated (Liu et al., 2024; Zhao, 2023; Yang & Zhang, 2012; Sun et al., 2025).
Objective: This article develops a unified conceptual framework linking explainable machine learning, human-centered robotics, and advanced material design into one research architecture for future intelligent industrial and healthcare systems.
Methods: The study uses a conceptual and integrative methodology based strictly on the supplied references. It synthesizes work on collaborative robotics, rehabilitation robotics, machine learning algorithms, explainable artificial intelligence, safety standards, sensor fusion, and high-entropy alloy design into a multi-layer framework spanning materials, components, control, interaction, and social deployment.
Results: The analysis indicates that the next generation of robotic systems will depend on a simultaneous integration of material intelligence, component reliability, embodied human interaction, interpretable learning, and socially responsible deployment. High-performance materials and core parts improve robotic capability, but these gains remain incomplete without explainable models, multimodal sensing, safety standards, emotional adaptation, and digital inclusion. Machine learning emerges as a cross-domain bridge connecting phase prediction in materials, fault-sensitive performance interpretation in structures, perception fusion, and adaptive human-robot interaction.
Conclusion: Intelligent robotics should no longer be studied as an isolated control problem or as a purely mechanical system. It should be understood as a layered socio-technical architecture in which materials, machine learning, safety engineering, rehabilitation logic, and human communication co-evolve. This integrated perspective offers a stronger foundation for trustworthy, high-performance, and inclusive robotic futures.
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References
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