AI, Computer Vision and Human–Robot Interaction

Dear Colleagues, 

The convergence of Artificial Intelligence (AI), Computer Vision, and Human–Robot Interaction (HRI) is accelerating the development of intelligent, autonomous, and interactive robotic systems. From autonomous navigation and human–robot interaction to environmental sensing and healthcare assistance, robots must operate intelligently and safely in real-world settings. Advances in deep learning, transformer-based models, and sim-to-real learning are driving rapid improvements in perception, planning, and adaptability. However, these must be complemented by explainability and robust generalization, especially in domains such as AAL, healthcare, swarm-based sensing, and field robotics, where reliability and trust are critical. This Special Issue highlights recent advances that empower robots to perceive, interpret, and act reliably in complex, dynamic, and human-centered environments. Core technologies of interest include transformer-based architectures, multimodal learning, transfer learning, and Explainable AI (XAI), each contributing to greater adaptability, transparency, and trust in real-world robotic applications. 

We welcome contributions ranging from novel algorithms to integrated frameworks and toolkits that support development, reproducibility, and deployment. Particular interest lies in applied research across impactful domains such as Ambient Assisted Living (AAL), healthcare, autonomous off-road navigation, and swarm robotics, where coordination, safety, and interpretability are essential to success. 

This Special Issue invites interdisciplinary research on perception, learning, and interaction in intelligent robotic systems. We welcome both algorithmic innovations and deployed systems that bridge core AI with practical robotic applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Transformer-based models and attention mechanisms in robotic perception;
• Multimodal learning and sensor fusion across vision, language, and tactile inputs;
• Transfer learning and domain adaptation for visual and behavioral generalization;
• Explainable AI (XAI) in robotic systems, especially in safety-critical domains like healthcare, AAL, and swarm robotics;
• Vision-based human–robot interaction, including gesture, emotion, and behavior recognition;
• Reinforcement and imitation learning for adaptive robotic behavior;
• Sim-to-real transfer for deploying learned models in physical systems;
• Vision-based navigation, terrain understanding, and autonomous exploration in unstructured environments;
• Swarm robotics and AI coordination for collaborative sensing and decision-making;
• Collaborative, assistive, and human-in-the-loop robotic systems;
• Frameworks, toolkits, or platforms supporting perception, learning, or explainability;
• Ethical and socially responsible AI in robotic applications. 

We look forward to receiving your contributions.  

Dr. Hannan Azhar
Dr. Md Nazmul Huda
Prof. Dr. Hongying Meng
Guest Editors

Manuscript Submission Information

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• artificial intelligence
• computer vision
• explainable AI (XAI)
• transformer-based models
• transfer learning
• sim-to-real learning
• human–robot interaction
• swarm robotics
• autonomous navigation
• assistive robotics