Robots with Intelligence: Developments and Applications

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 905

Special Issue Editor


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Guest Editor
Department of Artificial Intelligence and Robotics, Sejong University, Seoul, Republic of Korea
Interests: robots; soft robots; wearable robots; soft actuators; tendon-driven; actuators; mechatronics; soft sensors; mechanisms; control; input-shaping control; artificial intelligence
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Special Issue Information

Dear Colleagues,

Robotic systems are undergoing a paradigm shift as intelligence increasingly becomes a core component of their design and application. By incorporating advanced sensing, control, and decision-making mechanisms, robots are evolving from conventional automation tools into autonomous systems capable of adapting to complex, dynamic, and human-centered environments. The integration of artificial intelligence, machine learning, and cognitive computing enables robots to perceive, learn, and collaborate in ways that extend their functionality far beyond traditional domains.

Recent developments have demonstrated the potential of intelligent robots in a wide range of applications, including manufacturing, healthcare, rehabilitation, service, agriculture and autonomous systems. Nonetheless, the field continues to face fundamental challenges, such as robust perception under uncertainty, safe and effective human–robot collaboration, scalable deployment across heterogeneous settings, and the assurance of transparency and trustworthiness in AI-driven decisions.

This Special Issue aims to present the latest advances and emerging directions in intelligent robotics. We invite high-quality contributions that address novel theories, methods, technologies, and applications in this field, including but not limited to the following:

  • Intelligent sensing, perception, and multimodal data fusion;
  • Learning-based and adaptive control for robots;
  • Human–robot interaction and collaboration;
  • Cognitive architectures and decision-making in robotics;
  • AI-driven planning, navigation, and task execution;
  • Intelligent service, industrial, agricultural, and medical robots;
  • Robotics for healthcare, rehabilitation, and assistive technologies;
  • Autonomous systems in real-world and unstructured environments;
  • Explainable and trustworthy AI for robotics;
  • Emerging applications of intelligent robots in society.

Through this collection, we seek to provide a platform for disseminating cutting-edge research that will contribute to advancing the development and deployment of robots with intelligence across diverse domains.

Prof. Dr. Brian Byunghyun Kang
Guest Editor

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Keywords

  • intelligent robotics
  • artificial intelligence in robots
  • human–robot interaction
  • adaptive control and learning
  • real-world applications

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Published Papers (2 papers)

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Research

30 pages, 4814 KB  
Article
Cross-Embodiment Kinematic Behavioral Cloning (X-EKBC): An Energy-Based Framework for Human–Robot Imitation Learning with the Embodiment Gap
by Yoshiki Tsunekawa, Masaki Tanaka and Kosuke Sekiyama
Machines 2025, 13(12), 1134; https://doi.org/10.3390/machines13121134 - 10 Dec 2025
Viewed by 388
Abstract
In imitation learning with the embodiment gap, directly transferring human motions to robots is challenging due to differences in body structures. Therefore, it is necessary to reconstruct human motions in accordance with each robot’s embodiment. Our previous work focused on the right arm [...] Read more.
In imitation learning with the embodiment gap, directly transferring human motions to robots is challenging due to differences in body structures. Therefore, it is necessary to reconstruct human motions in accordance with each robot’s embodiment. Our previous work focused on the right arm of a humanoid robot, which limited the generality of the approach. To address this, we propose Cross-Embodiment Kinematic Behavioral Cloning (X-EKBC), an imitation learning framework that enables movement-level imitation on a one-to-one basis between humans and multiple robots with embodiment gaps. We introduce a joint matrix that represents the structural correspondence between the human and robot bodies, and by solving kinematics based on this matrix, the system can efficiently reconstruct motions adapted to each robot’s embodiment. Furthermore, by employing Implicit Behavioral Cloning (IBC), the proposed method achieves both imitation learning of the reconstructed motions and quantitative evaluation of embodiment gaps using energy-based modeling. As a result, motion reconstruction through the joint matrix became feasible, enabling both imitation learning and quantitative embodiment evaluation based on reconstructed behaviors. Future work will aim to extend this framework toward motion-level imitation that captures higher-level behavioral outcomes. Full article
(This article belongs to the Special Issue Robots with Intelligence: Developments and Applications)
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25 pages, 2873 KB  
Article
Dynamic Attention Analysis of Body Parts in Transformer-Based Human–Robot Imitation Learning with the Embodiment Gap
by Yoshiki Tsunekawa and Kosuke Sekiyama
Machines 2025, 13(12), 1133; https://doi.org/10.3390/machines13121133 - 10 Dec 2025
Viewed by 298
Abstract
In imitation learning between humans and robots, the embodiment gap is a key challenge. By focusing on a specific body part and compensating for the rest according to the robot’s size, the embodiment gap can be overcome. In this paper, we analyze dynamic [...] Read more.
In imitation learning between humans and robots, the embodiment gap is a key challenge. By focusing on a specific body part and compensating for the rest according to the robot’s size, the embodiment gap can be overcome. In this paper, we analyze dynamic attention to body parts in imitation learning between humans and robots based on a Transformer model. To adapt human imitation movements to a robot, we solved forward and inverse kinematics using the Levenberg–Marquardt method and performed feature extraction using the k-means method to make the data suitable for Transformer input. The imitation learning process is carried out using the Transformer. UMAP is employed to visualize the attention layer within the Transformer. As a result, this system enabled imitation of movements while focusing on multiple body parts between humans and robots with an embodiment gap, revealing the transitions of body parts receiving attention and their relationships in the robot’s acquired imitation movements. Full article
(This article belongs to the Special Issue Robots with Intelligence: Developments and Applications)
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