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: 31 July 2025 | Viewed by 695

Special Issue Editors

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)
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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

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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 (1 paper)

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Research

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 140
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
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