Advances in Human–Robot Interactions and Assistive Applications

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 1012

Special Issue Editor


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Guest Editor
School of Engineering, Colorado State University Pueblo, Pueblo, CO 81001, USA
Interests: intelligent systems; companion robots; wearable devices; human–robot interaction; conversational AI; reinforcement learning; meta learning and large language models

Special Issue Information

Dear Colleagues,

Human–Robot Interaction (HRI) is central to enabling robots to work effectively, safely, and intuitively with humans across healthcare, education, industry, and domestic environments. The field is rapidly advancing due to significant progress in robotics, deep learning, reinforcement learning, imitation learning, large language models, and human-centered design. Despite these advances, several challenges remain before HRI can achieve widespread adoption and impact. Key issues include interpreting complex environments and human behaviors, adapting to diverse user needs in real time, ensuring safety and reliability, and building trust and long-term acceptance.

This Special Issue aims to highlight the latest advances, research findings, and real-world applications that enhance the interaction between humans and robots. We hereby extend an invitation to contributions that explore the frontiers of HRI through innovative methodologies, system architectures, and deployment strategies, with a particular emphasis on assistive applications that improve quality of life, support healthcare, and promote human well-being.

Dr. Zhidong Su
Guest Editor

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Keywords

  • human–robot interaction
  • assistive robotics
  • large language models
  • artificial intelligence agent
  • context-aware systems
  • multi-modal interaction
  • safety and reliability

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Published Papers (1 paper)

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Research

24 pages, 8934 KB  
Article
Vision Transformer-Based Identification for Early Alzheimer’s Disease and Mild Cognitive Impairment
by Yang Li, Biao Xu, Qiang Bai, Zhenghong Liu, Junfeng Zhu and Qipeng Chen
Information 2026, 17(2), 129; https://doi.org/10.3390/info17020129 - 30 Jan 2026
Cited by 1 | Viewed by 621
Abstract
Distinguishing Alzheimer’s Disease (AD) from Mild Cognitive Impairment (MCI) is challenging due to their subtle morphological similarities in MRI, yet distinct therapeutic strategies are required. To assist junior clinicians with limited diagnostic experience, this paper proposes Vi-ADiM, a Vision Transformer framework designed for [...] Read more.
Distinguishing Alzheimer’s Disease (AD) from Mild Cognitive Impairment (MCI) is challenging due to their subtle morphological similarities in MRI, yet distinct therapeutic strategies are required. To assist junior clinicians with limited diagnostic experience, this paper proposes Vi-ADiM, a Vision Transformer framework designed for the early differentiation of AD and MCI. Leveraging cross-domain feature adaptation and task-specific data augmentation, the model ensures rapid convergence and robust generalization even in data-limited regimes. By optimizing a two-stage encoding module, Vi-ADiM efficiently extracts both global and local MRI features. Furthermore, by integrating SHAP and Grad-CAM++, the framework offers multi-granular interpretability of pathological regions, providing intuitive visual evidence for clinical decision-making. Experimental results demonstrate that Vi-ADiM outperforms the standard ViT-Base/16, improving accuracy, precision, recall, and F1 score by 0.444%, 0.486%, 0.476%, and 0.482%, respectively, while reducing standard deviations by approximately 0.06–0.29%. Notably, the model achieves these gains with a 48.96% reduction in parameters and a 49.65% decrease in computational cost (FLOPs), offering a reliable, efficient, and interpretable solution for computer-aided diagnosis. Full article
(This article belongs to the Special Issue Advances in Human–Robot Interactions and Assistive Applications)
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