Kohei Miki
Papers
4
Total Citations
23
H-Index
3
About
Kohei Miki is a robotics researcher whose work centers on the integration of computer vision, deep learning, and automation for industrial applications. His primary research areas include visual feedback control, convolutional neural networks (CNNs), and transfer learning for robotic manipulation. Miki’s major contributions lie in developing pick-and-place robots that leverage CNN-based image recognition and pixel-level visual feedback to improve precision and adaptability in automated production lines. His most cited work, "Pick and Place Robot Using Visual Feedback Control and Transfer Learning-Based CNN" (2020, 13 citations), demonstrates how deep neural networks can enhance robotic grasping tasks. He has extended this approach to sliding rail systems and articulated robot arms, as seen in his 2021 and 2022 papers. Miki also contributed to the development of a hyper CLS data-based robotic interface for streamlining production-line automation. While his citation counts are modest, his research provides practical frameworks for combining transfer learning with real-time visual control, offering valuable insights for students and engineers working on intelligent manufacturing and robotic vision systems.
Research Focus
Key Achievements
Top Papers
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