EDITORIAL article
Front. Robot. AI
Sec. Robot Learning and Evolution
This article is part of the Research TopicAdvancements in Neural Learning Control for Enhanced Multi-Robot CoordinationView all 5 articles
Editorial: Advancements in Neural Learning Control for Enhanced Multi-Robot Coordination
Provisionally accepted
- 1Guangzhou University, Guangzhou, China
- 2South China University of Technology, Guangzhou, China
- 3University of Rhode Island, Kingston, United States
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1 Sample et al. Organized under the section "Robot Learning and Evolution", this Research Topic has published four 17 articles. All the accepted papers are summarized as follows. for distributed state estimation with a decentralized deterministic learning controller using radial basis 37 function neural networks, the method achieves precise formation tracking without prior knowledge of robot 38 parameters or environmental conditions. A key innovation is the system's ability to learn and store dynamic 39 knowledge as constant neural weights, enabling efficient reuse after system restarts without retraining. Comprehensive simulations demonstrate the framework's stability, resilience, and superior adaptability in
Keywords: neural network control, Learning control, dynamic learning, reinforcement learning, Adaptive control, Robotics, Autonomous vehicles, Multi robotics
Received: 24 Oct 2025; Accepted: 13 Nov 2025.
Copyright: © 2025 He, Dai, Yuan and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Shude He, shude_he@163.com
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