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Machines
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Robotic Intelligence Development of AI in Robot Perception, Learning, and...
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Robotic Intelligence Development of AI in Robot Perception, Learning, and Decision

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1466

Special Issue Editors

Dr. Yanhong Peng

E-Mail Website1 Website2
Guest Editor
1. College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China
2. Department of Information and Communication Engineering, Graduate School of Engineering, Nagoya University, Nagoya 4648601, Japan
Interests: soft robotics; wearable robotics; bioinspired robotics; deep learning; large language model (LLM)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fernando Gomez-Bravo

E-Mail Website
Guest Editor
Department of Electronic Engineering, Computer Systems and Automation, University of Huelva, Huelva, Spain
Interests: robot control; mobile robotics; articulated robots; motion and path planning; intelligent control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) in robotics has ushered in a new era of autonomous, adaptive, and intelligent systems capable of performing complex tasks across a wide range of domains. This Special Issue, entitled ‘Robotic Intelligence Frontier Exploration of AI in Robot Perception, Learning, and Decision’, aims to explore the transformative role of AI in advancing robotic capabilities, with a particular focus on the following key areas: Brain–Computer Interfaces (BCIs), reinforcement learning in autonomous robots, robot perception through computer vision, humanoid robots with affective computing, collaborative robots (cobots), smart manufacturing in the context of Industry 4.0, intelligent medical robots, augmented reality-enhanced robot interactions, context-aware and adaptive systems, and multi-robot systems leveraging collective intelligence.

We invite contributions that delve into how AI is reshaping these domains, enabling robots to learn, perceive, collaborate, and adapt in dynamic real-world environments. Topics of interest include the development of advanced BCIs for human–robot interactions, the application of reinforcement learning for autonomous decision making, the evolution of humanoid robots equipped with affective computing for emotional intelligence, and the use of cobots in industrial and collaborative settings. Additionally, we encourage research on AI-driven advancements in smart manufacturing, medical robotics, augmented reality interfaces, and multi-robot systems. Our goal is to highlight how AI is driving new developments in robotic intelligence, improving robot autonomy, adaptability, and interactivity.

We look forward to your innovative research that explores these exciting areas and contributes to the ongoing evolution of intelligent robotics.

Dr. Yanhong Peng
Prof. Dr. Fernando Gomez-Bravo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • brain–computer interface (BCI)
  • autonomous robots and reinforcement learning
  • robot perception and computer vision
  • humanoid robots and affective computing
  • collaborative robots (cobots)
  • smart manufacturing and Industry 4.0
  • intelligent medical robots
  • augmented reality and robot interactions
  • context-aware and adaptive robots
  • multi-robot systems and collective intelligence

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Published Papers (3 papers)

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Research

26 pages, 1863 KiB  
Article
Robotic Positioning Accuracy Enhancement via Memory Red Billed Blue Magpie Optimizer and Adaptive Momentum PSO Tuned Graph Neural Network
by Jian Liu, Xiaona Huang, Yonghong Deng, Canjun Xiao and Zhibin Li
Machines 2025, 13(6), 526; https://doi.org/10.3390/machines13060526 - 16 Jun 2025
Abstract
Robotic positioning accuracy is critically affected by both geometric and non-geometric errors. To address this dual error issue comprehensively, this paper proposes a novel two-stage compensation framework. First, a Memory based red billed blue magpie optimizer (MRBMO) is employed to identify and compensate [...] Read more.
Robotic positioning accuracy is critically affected by both geometric and non-geometric errors. To address this dual error issue comprehensively, this paper proposes a novel two-stage compensation framework. First, a Memory based red billed blue magpie optimizer (MRBMO) is employed to identify and compensate for geometric errors by optimizing the geometric parameters based on end-effector observations. This memory-guided evolutionary mechanism effectively enhances the convergence accuracy and stability of the geometric calibration process. Second, a tuned graph neural network (AMPSO-GNN) is developed to model and compensate for non-geometric errors, such as cable deformation, thermal drift, and control imperfections. The GNN architecture captures the topological structure of the robotic system, while the adaptive momentum PSO dynamically optimizes the network’s hyperparameters for improved generalization. Experimental results on a six-axis industrial robot demonstrate that the proposed method significantly reduces residual positioning errors, achieving higher accuracy compared to conventional calibration and compensation strategies. This dual-compensation approach offers a scalable and robust solution for precision-critical robotic applications. Full article
(This article belongs to the Special Issue Robotic Intelligence Development of AI in Robot Perception, Learning, and Decision)
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17 pages, 1463 KiB  
Article
An Autonomous Fluoroscopic Imaging System for Catheter Insertions by Bilateral Control Scheme: A Numerical Simulation Study
by Gregory Y. Ward, Dezhi Sun and Kenan Niu
Machines 2025, 13(6), 498; https://doi.org/10.3390/machines13060498 - 6 Jun 2025
Viewed by 291
Abstract
This study presents a bilateral control architecture that links fluoroscopic image feedback directly to the kinematics of a tendon-driven, three-joint robotic catheter and a 3-DoF motorised C-arm, intending to preserve optimal imaging geometry during autonomous catheter insertion and thereby mitigating radiation exposure. Forward [...] Read more.
This study presents a bilateral control architecture that links fluoroscopic image feedback directly to the kinematics of a tendon-driven, three-joint robotic catheter and a 3-DoF motorised C-arm, intending to preserve optimal imaging geometry during autonomous catheter insertion and thereby mitigating radiation exposure. Forward and inverse kinematics for both manipulators were derived via screw theory and geometric analysis, while a calibrated projection model generated synthetic X-ray images whose catheter bending angles were extracted through intensity thresholding, segmentation, skeletonisation, and least-squares circle fitting. The estimated angle fed a one-dimensional extremum-seeking routine that rotated the C-arm about its third axis until the apparent bending angle peaked, signalling an orthogonal view of the catheter’s bending plane. Implemented in a physics-based simulator, the framework achieved inverse-kinematic errors below 0.20% for target angles between 20° and 90°, with accuracy decreasing to 3.00% at 10°. The image-based angle estimator maintained a root-mean-square error ≤3% across most of the same range, rising to 6.4% at 10°. The C-arm search consistently located the optimal perspective, and the combined controller steered the catheter tip along a predefined aortic path without collision. These results demonstrate sub-degree angular accuracy under idealised, noise-free conditions and validate real-time coupling of image guidance to dual-manipulator motion; forthcoming work will introduce realistic image noise, refined catheter mechanics, and hardware-in-the-loop testing to confirm radiation-dose and workflow benefits. Full article
(This article belongs to the Special Issue Robotic Intelligence Development of AI in Robot Perception, Learning, and Decision)
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26 pages, 7159 KiB  
Article
Methodology for Human–Robot Collaborative Assembly Based on Human Skill Imitation and Learning
by Yixuan Zhou, Naisheng Tang, Ziyi Li and Hanlei Sun
Machines 2025, 13(5), 431; https://doi.org/10.3390/machines13050431 - 19 May 2025
Viewed by 390
Abstract
With the growing demand for personalized and flexible production, human–robot collaboration technology receives increasing attention. However, enabling robots to accurately perceive and align with human motion intentions remains a significant challenge. To address this, a novel human–robot collaborative control framework is proposed, which [...] Read more.
With the growing demand for personalized and flexible production, human–robot collaboration technology receives increasing attention. However, enabling robots to accurately perceive and align with human motion intentions remains a significant challenge. To address this, a novel human–robot collaborative control framework is proposed, which utilizes electromyography (EMG) signals as an interaction interface and integrates human skill imitation with reinforcement learning. Specifically, to manage the dynamic variation in muscle coordination patterns induced by joint angle changes, a temporal graph neural network enhanced with an Angle-Guided Attention mechanism is developed. This method adaptively models the topological relationships among muscle groups, enabling high-precision three-dimensional dynamic arm force estimation. Furthermore, an expert reward function and a fuzzy experience replay mechanism are introduced in the reinforcement learning model to guide the human skill learning process, thereby enhancing collaborative comfort and smoothness. The proposed approach is validated through a collaborative assembly task. Experimental results show that the proposed arm force estimation model reduces estimation errors by 10.38%, 8.33%, and 11.20% across three spatial directions compared to a conventional Deep Long Short-Term Memory (Deep-LSTM). Moreover, it significantly outperforms state-of-the-art methods, including traditional imitation learning and adaptive admittance control, in terms of collaborative comfort, smoothness, and assembly accuracy. Full article
(This article belongs to the Special Issue Robotic Intelligence Development of AI in Robot Perception, Learning, and Decision)
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