Mikhail Fadeev
Papers
2
Total Citations
16
H-Index
2
About
Mikhail Fadeev is a researcher specializing in robotics, with a particular focus on cable-driven robotic systems and the application of deep learning for precision control. His work addresses a critical challenge in this domain: the inherent inaccuracies caused by cable elasticity, friction, and dynamic loads. Fadeev’s major contribution lies in pioneering model-free error compensation techniques that leverage transfer learning to bridge the gap between simulation and real-world deployment. His most-cited paper, "Deep Learning with Transfer Learning Method for Error Compensation of Cable-driven Robot" (2020, 12 citations), introduced a novel framework that significantly improves positional accuracy without requiring complex physical models. He further advanced this approach in his 2022 work, "Model Free Error Compensation for Cable-Driven Robot Based on Deep Learning with Sim2real Transfer Learning" (4 citations), demonstrating how neural networks trained in simulation can be effectively adapted to physical robots. Fadeev’s research is notable for its practical impact, offering scalable solutions that reduce the need for expensive real-world data collection. His work is highly relevant for applications in industrial automation, medical robotics, and soft robotics, where cable-driven systems offer flexibility and safety.
Research Focus
Key Achievements
Top Papers
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