Kemal Açıkgöz
Papers
1
Total Citations
3
H-Index
1
About
Kemal Açıkgöz is a researcher focused on the intersection of robotics, human skill modeling, and manufacturing automation. His key research areas include robotic process parametrization, motor skill learning, and human-robot skill transfer. Açıkgöz’s major contribution lies in developing methods that enable robots to learn and replicate complex human motor skills, particularly in precision tasks like deburring. His most cited work, "Parametrization of robotic deburring process with motor skills from motion primitives of human skill model" (2017, 3 citations), introduces a novel framework where a human expert’s skill is captured through a human skill model and then implemented into robot control systems. This allows robots to imitate human dexterity in industrial processes, bridging the gap between human expertise and robotic efficiency. While his citation count is modest, his work represents a foundational step in skill-based robotic learning, offering practical pathways for automating tasks that traditionally rely on human craftsmanship. Açıkgöz’s research is particularly valuable for students and researchers exploring human-robot collaboration, motor learning, and adaptive manufacturing systems.
Research Focus
Key Achievements
Top Papers
- 1