Machine Learning for Actuation and Control in Robotic Joint Systems

Dear Colleagues,

Robotic joint systems are fundamental to modern robotics, enabling precise motion, adaptability, and autonomy in applications ranging from industrial automation to biomedical devices. Recent advances in machine learning (ML) have opened new possibilities for enhancing the actuation and control of these systems, improving efficiency, robustness, and adaptability in dynamic environments. This Special Issue seeks to explore cutting-edge research at the intersection of ML, actuation technologies, and control strategies for robotic joints.

We aim to showcase innovative methodologies, experimental validations, and reviews that address challenges and opportunities in ML-driven robotic joint systems. Researchers are encouraged to submit original work that bridges the gap between theoretical ML advancements and practical robotic applications.

Dr. Gao Huang
Dr. Pan Yu
Guest Editors

Manuscript Submission Information

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