Towards a Distributed-Based Learning Robot from Scratch via Neuro-Evolutionary Computation
Journal
Advances in Soft Computing : 24th Mexican International Conference on Artificial Intelligence, MICAI 2025, Guanajuato, Mexico, November 3, 2025, Proceedings, Part II
ISSN
0302-9743
1611-3349
Publisher
Springer Nature Switzerland
Date Issued
2025
Author(s)
Type
text::book::book part
Abstract
In the context of Industry 4.0, multi-robot systems (MRS) have become essential for enhancing the adaptability and efficiency of flexible manufacturing systems, enabling rapid responses to market demands through personalized customization. Effective collaboration among multiple robots requires advanced communication, shared goal alignment, and the ability to gather and process environmental data to execute coordinated actions with precision. Despite their advantages, improving efficiency of robot learning still remains a crucial challenge, particularly in flexible manufacturing multi-robot systems, where generalization across diverse scenarios is essential for effective deployment. In this work, we propose the use of neuro-evolutionary computation to solve the particular learning-from-scratch problem in a multi-robot system. This approach consists of an architecture of a decentralized MRS in which a local controller per robot is based on Artificial Hydrocarbon Networks machine learning model. Also, it includes a learning strategy via Wound Treatment Optimization algorithm. We implement the architecture in a simulated environment to solve the mountain car domain. Experimental results and a comparison with reinforcement learning validate the ability of this approach to learn a task from scratch without prior explicit data. We anticipate the applicability of this approach in smart factories or autonomous vehicles. ©The authors ©Springer.
License
Acceso Restringido
How to cite
Ponce, H., Martínez-Villaseñor, L. (2026). Towards a Distributed-Based Learning Robot from Scratch via Neuro-Evolutionary Computation. In: Martínez-Villaseñor, L., Vázquez, R.A., Ochoa-Ruiz, G. (eds) Advances in Soft Computing. MICAI 2025. Lecture Notes in Computer Science(), vol 16222. Springer, Cham. https://doi.org/10.1007/978-3-032-09044-7_8