Applications of Neural Networks in Robot Control

Dear Colleagues,

The field of robotics has made significant strides in recent years. Robots are increasingly becoming an integral part of various industries and daily life. This progress has largely been driven by advances in artificial intelligence, including neural-network-based applications. Neural networks have enhanced the capabilities of robots, enabling them to perform complex tasks, adapt to dynamic environments, and more effectively interact with humans. As a result, the integration between neural networks and robot control has become a key research area with great potential for innovation and impact. However, many aspects still need to be further explored: the computational effort needed for real-time vision-based control applications, the need for custom hardware implementations for specific tasks, the use of continuous learning for efficient adaptation to dynamically changing environments, the problem of dynamic stability when dealing with multi-link robots, and many others.

Last but not least, in addition to AI-based neural structures, neuromorphic spiking neural networks have recently been attracting attention because of their similarity to living neural tissue, but also because of their impressive dynamic behaviour, which would represent a potential alternative approach for robot control.

The proposed Special Issue aims to address these and other relevant research topics, as well as to consolidate and present the latest developments in the application of neural networks to robot control in different domains: from traditional wheeled robots to legged robots and from high-level motion planning tasks to single limb movements. Relevant potential applications range from the exploration of unstructured terrain to industrial automation and healthcare.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Deep learning for robot perception;
• Reinforcement learning for robot control in dynamic environments;
• Neural-network-based motion planning and control;
• Human–robot interaction with deep learning techniques;
• Autonomous systems and navigation;
• Spiking neural networks for robot control;
• Bio-inspired brain models;
• Neuromorphic systems;
• Neural networks and soft robotics.

We look forward to receiving your contributions.

Prof. Dr. Luca Patanè
Prof. Dr. Paolo Arena
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. Robotics 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 1800 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.

• neural control
• spiking neural networks
• bio-inspired brain models
• neuromorphic systems
• learning in bio-inspired robots
• sensory–motor coordination
• soft robotics
• legged robotics