Quantum Machine Learning for Next-Generation Robot Control
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".
Deadline for manuscript submissions: 15 July 2026 | Viewed by 11
Special Issue Editors
Interests: industrial robots; system optimisation; robot industrial engineering
Special Issues, Collections and Topics in MDPI journals
Interests: CAD/CAE/CAM; industrial automation; PLCs; industrial robots; CNC systems
Special Issues, Collections and Topics in MDPI journals
Interests: robotic design; system optimisation; autonomous robots; machine learning; adaptive robotics; multi-robot systems; human–robot interaction; energy efficiency; real-time control; AI integration in robotics
Interests: robotic system design; system optimization; autonomous mobile robots; sensor fusion; adaptive robotics; machine learning for robotics; reinforcement learning; multi-robot systems; human–robot interaction; real-time motion control; energy-efficient robotics; AI integration in robotics; embedded robotic systems; robotics simulation and digital twins; fault detection and reliability in robotics
Special Issue Information
Dear Colleagues,
The use of machine learning for autonomous robot control is becoming increasingly essential across multiple industrial applications, including smart factories, automated logistics, and healthcare. Machine learning enables autonomous robots to operate efficiently and adapt to the complex and dynamic environments in which they function. However, its limitations—particularly in safety, real-time performance, interpretability, and data requirements—often necessitate the integration of classical control strategies or hybrid approaches. In this context, quantum machine learning (QML) offers additional advantages such as computational acceleration, scalability, and enhanced flexibility, making autonomous robot control more robust and adaptable in demanding industrial scenarios.
This Special Issue aims to highlight the latest advances and research in machine learning and quantum machine learning for next-generation robot control. We invite contributions that advance current knowledge and explore, among others, the following topics:
- Optimization algorithms for improving robotic control performance;
- Methods for reducing robot training time;
- Approaches for managing dynamic and unpredictable environments;
- Techniques for minimizing computational resource consumption in robot control;
- Solutions for handling failures or extreme situations that pose safety risks;
- Strategies enabling fast adaptation to new environmental conditions;
- Simulation and modeling tools that support the development of advanced control systems;
- Methodologies for achieving real-time performance with low-latency decision-making.
We welcome original research presenting new findings and practical solutions, as well as review articles summarizing recent advances and emerging trends in these domains. Through this collection, we aim to foster a deeper understanding and stimulate innovative ideas that will drive the next generation of autonomous robot control technologies. We hope to support the development of more advanced, efficient, and adaptable robotic control systems for the future and look forward to hearing from you.
Dr. Melania Tera
Prof. Dr. Radu-Eugen Breaz
Dr. Adrian I. Marosan
Dr. Mihai Octavian Popp
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- quantum machine learning
- robot control
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