Machine Learning Application in Robots
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 9
Special Issue Editor
Special Issue Information
Dear Colleagues,
Machine learning is rapidly transforming the field of robotics, enabling systems to achieve higher levels of autonomy, adaptability, and precision across diverse sectors such as manufacturing, healthcare, and smart mobility. Traditional robotic and automation systems—including servo systems and actuators—were largely designed without the integration of artificial intelligence due to technological limitations. However, recent advances in machine learning, computer vision, and intelligent control are now revolutionising how these systems operate within, perceive, and interact with their environments.
This Special Issue invites high-quality research papers and review articles that explore innovative applications of machine learning in robotics and intelligent machines. Contributions demonstrating novel approaches in control, perception, and decision-making, particularly those that enhance the performance, reliability, and fault tolerance of robotic and actuator-based systems, are especially welcome.
Dr. Adeel Mehmood
Guest Editor
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.
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Keywords
- machine learning for adaptive and fault-tolerant control of robotic and servo systems
- intelligent actuation and sensor-based learning for robotic applications
- computer vision, sensor fusion, and perception-driven autonomy
- reinforcement and deep learning for robotic motion and trajectory planning
- learning-based optimisation for precision manufacturing and service robotics
- human–robot interaction and collaborative intelligence
- predictive maintenance and diagnostics of actuators and robotic subsystems
- AI-driven control strategies for industrial automation and mechatronic systems
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