Yuki Amaoka
Papers
1
Total Citations
3
H-Index
1
About
Yuki Amaoka’s research lies at the intersection of robotics, human skill analysis, and human-robot interaction, with a focus on transferring complex human abilities to autonomous systems. His most-cited work, “Reconstruction of Human Skills by Using PCA and Transferring them to a Robot” (2014), introduces a framework for classifying human skills into motor and cognitive functions, then using principal component analysis to reconstruct and transfer these skills to robots. This approach enables machines to replicate human-like adaptability, such as a table tennis player adjusting to an incoming ball—a key step toward more intuitive robotic learning. While his citation count is modest, Amaoka’s contributions are notable for bridging biomechanics and machine learning, offering a systematic method for skill decomposition and transfer. His work has implications for assistive robotics, rehabilitation, and autonomous systems that require human-like dexterity. By focusing on the reconstruction of human expertise, Amaoka has laid groundwork for robots that learn not just from data, but from the nuanced, adaptive strategies that define human performance.
Research Focus
Key Achievements
Top Papers
- 1