Guest Editors
Prof. Jiahui Yu
Email: jiahui.yu@zju.edu.cn
Affiliation: Binjiang Institute, Zhejiang University, Hangzhou, China
Homepage:
Research Interests: human sensing, hman-robot interaction, embodied AI
Prof. Zhenzhong Wang
Email: zhenzhongwang@xmu.edu.cn
Affiliation: School of Informatics, Xiamen University, Xiamen, China
Homepage:
Research Interests: evolutionary algorithm, multi object optimization, AI for Science
Dr. Jinchao Ge
Email: jge@uow.edu.au
Affiliation: School of Computing and Information Technology, University of Wollongong, Wollongong, Australia
Homepage:
Research Interests: medical image processing, segmentation, unsupervised learning
Dr. Shuwen Zhao
Email: donutzsw@gmail.com
Affiliation: School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China
Homepage:
Research Interests: Industrial vision, embodied intelligence, target detection
Prof. Haibin Cai
Email: h.cai@lboro.ac.uk
Affiliation: Department of Computer Science, Loughborough University, Loughborough, LE11 3TU, United Kingdom
Homepage:
Research Interests: computer vision, robotics and the application of convolution neural networks
Summary
Human-centered embodied intelligence is rapidly reshaping how machines perceive, interact with, and assist people in complex real-world environments. Recent advances in multimodal sensing, learning-based perception, and trustworthy control are enabling robots and intelligent systems to move from controlled laboratories to high-stakes deployment in industry and healthcare, where safety, reliability, and human factors are critical.
This Special Issue aims to present cutting-edge research on Human-Centered Embodied Intelligence, with an emphasis on end-to-end pipelines that integrate sensing, interaction, and control. We welcome methodological innovations (e.g., multimodal fusion, representation learning, foundation/agentic models for embodied tasks, sim-to-real transfer, uncertainty-aware learning) as well as system-level studies that demonstrate robust performance in realistic settings (e.g., factories, hospitals, rehabilitation, assistive care). A key goal is to bridge algorithmic advances with practical constraints such as safety certification, interpretability, data efficiency, and human-in-the-loop decision making.
Suggested themes include:
· Multimodal human/environment sensing and perception for embodied systems
· Human–robot interaction, intent inference, and shared autonomy
· Trustworthy control (safety, robustness, uncertainty, verification)
· Foundation models and agents for embodied perception–action
· Sim-to-real transfer, domain adaptation, and continual learning
· Industrial robotics applications (inspection, manipulation, logistics)
· Healthcare applications (assistive robotics, rehabilitation, surgical/clinical support)
Keywords
embodied intelligence; human action understanding; human–robot interaction; multimodal sensing; trustworthy control; safety and robustness; sim-to-real transfer; industrial imaging; medical imaging; foundation models
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