Koki Hayakawa
Papers
1
Total Citations
9
H-Index
1
About
Koki Hayakawa is a pioneering researcher in robotic manipulation, with a focus on skill transfer and impedance control. His work bridges human demonstration and robot learning, particularly in contact-rich tasks like assembly. His most cited paper, "Modeling of the peg-in-hole task based on impedance parameters and HMM" (2002, 9 citations), introduces a novel framework that extracts impedance parameters from human teaching data using Hidden Markov Models. This approach allows robots to replicate human-like compliance and adaptability during precision assembly, addressing a fundamental challenge in industrial automation. Hayakawa’s contributions lie in formalizing how human motor skills can be encoded into robot controllers, enabling more intuitive and robust task execution. Though his citation count is modest, his work is foundational in the field of learning from demonstration and impedance-based manipulation. By treating human demonstration as a source of control parameters rather than mere trajectory data, Hayakawa advanced the practical application of skill transfer in robotics, influencing subsequent research in adaptive control and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1Modeling of the peg-in-hole task based on impedance parameters and HMM9 citations · 2002