Special Issues
Table of Content

Robotics Vision and Thinking

Submission Deadline: 30 September 2026 View: 988 Submit to Special Issue

Guest Editor(s)

Assist. Prof. Zhou(Joe) Zhang

Email: zhangz@farmingdale.edu

Affiliation: Mechanical Engineering Technology, SUNY Farmingdale State College, Farmingdale, United States

Homepage:

Research Interests: artificial intelligence, robotics, virtual reality, computer vision

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Summary

Robotics is rapidly evolving as advances in Artificial Intelligence, vision systems, and cognitive modeling enable robots to perceive, interpret, and act in complex environments. Modern applications require robots not only to gather visual data but also to derive meaningful understanding and make context-aware decisions. This emerging integration of perception and cognition—"robotic thinking"—is essential for deploying intelligent systems in dynamic settings such as manufacturing, healthcare, logistics, and service environments.

This Special Issue invites contributions that push the boundaries of robotic vision and cognitive intelligence. We seek studies that introduce innovative computational approaches, robust system architectures, or impactful applications that connect visual perception with reasoning, adaptability, and autonomous behavior. Works aligned with the journal's focus on modeling, simulation, and intelligent computation are especially welcome.

Topics of Interest include, but are not limited to:
· Vision-guided robotic perception, object understanding, and scene interpretation
· Cognitive architectures, reasoning models, and decision-making for robotics
· 3D reconstruction, SLAM, and environmental mapping
· Human–robot interaction, gesture recognition, and behavioral prediction
· Digital twins, VR/AR, and immersive simulation environments for robotics
· Multimodal sensing, sensor fusion, and perceptual intelligence
· Autonomous navigation, manipulation, and planning in dynamic environments


Keywords

artificial intelligence, robotics, computer vision, cognitive robot, virtual reality

Published Papers


  • Open Access

    ARTICLE

    Deep Hand Segmentation and Multi-Modal Gesture Recognition for Human-Robot Interaction via 3D Volumetric Encoding

    Zarnab Kausar, Shaheryar Najam, Hadeel Alsolai, Bayan Alabdullah, Fatimah Alhayan, Ahmad Jalal, Hui Liu
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.084514
    (This article belongs to the Special Issue: Robotics Vision and Thinking)
    Abstract Hand gesture recognition (HGR) is essential for Human–Robot Interaction (HRI) but remains challenging due to variations in hand shape, motion, viewpoint, illumination, and background, while vision-based methods often suffer from sensitivity to skin tone, occlusions, deformations, and limited interpretability. To address these issues, we propose a unified framework integrating deep learning, geometry-driven analysis, and temporal motion modeling. We introduce Z-HandSegNet framework, involving a U-Net with a ResNet-34 encoder for robust hand segmentation, and the Ellipse-Guided Geometric Finger Segmentation and Keypoint Extraction (EG-FSKE) method, which decomposes hand silhouettes into palm and finger regions using distance transforms,… More >

  • Open Access

    REVIEW

    Training Methods and Generation Technologies for Embodied Intelligent Robot Manipulation Skill Models: A Systematic Review

    Lianpeng Li, Zhoujun Ruan, Zhichuang Wang, Haibo Zhang, Hang Zhong, Mingyang Li, Chunpeng Kang
    CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084092
    (This article belongs to the Special Issue: Robotics Vision and Thinking)
    Abstract Endowing embodied intelligent robots with dexterous manipulation capabilities is paramount for executing complex, open-ended tasks. These capabilities are foundational to advancing true robotic autonomy, thereby facilitating precision assembly, seamless collaborative operations, and highly specialized maneuvers across diverse industrial and service sectors. Focusing on dynamic, unstructured environments where conventional programmed behaviors prove inadequate, this paper presents a systematic, quantitatively driven review of training methodologies and generation techniques for manipulation skill models within the domain of embodied artificial intelligence (AI). To provide rigorous trend validation, this study conducts a comprehensive bibliometric analysis and quantitative literature evaluation. By… More >

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