Takuma Katsumata
Papers
2
Total Citations
29
H-Index
2
About
Takuma Katsumata is a robotics researcher whose work focuses on the critical intersection of robot dynamics, adaptive control, and human-robot interaction. His primary contributions lie in developing advanced model-based controllers that enable robots to operate safely and effectively in contact-rich environments, such as painting tasks and collaborative settings. Katsumata’s most cited work, "Optimal exciting motion for fast robot identification" (2019, 24 citations), introduces a method for rapidly and accurately identifying a robot’s dynamic parameters—a fundamental challenge for high-performance control. This technique is directly applied to contact tasks where external forces must be estimated in real time. His related research on adaptive generalized predictive control and Cartesian force control (2018) demonstrates how accurate dynamic and geometric identification can be leveraged to create robust controllers that adapt to changing environments, a key requirement for seamless human-robot collaboration. By addressing the practical hurdles of model-based control, Katsumata’s work provides a foundation for more responsive, safer, and more capable robotic systems in industrial and service applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2