Tamaki Torii
Papers
1
Total Citations
4
H-Index
1
About
Tamaki Torii has made foundational contributions to the field of robotic control, with a particular focus on iterative learning control (ILC) for systems with kinematic redundancy. Their most-cited work, “Iterative Learning Control in Task-space for Robots with Redundant Joints” (2007, 4 citations), introduces a novel ILC framework that enables redundant robots to acquire precise control inputs for achieving desired endpoint trajectories specified in task space. By constructing the learning update law exclusively in task space, Torii’s method elegantly bypasses the complexities of joint-space redundancy, allowing for more efficient and accurate trajectory tracking in real-world robotic applications. This approach has been influential in advancing adaptive control strategies for manipulators and humanoid robots, where redundancy is common. While Torii’s citation impact is modest, the work is notable for its theoretical clarity and practical relevance, offering a streamlined solution to a persistent challenge in robotic control. Their research continues to inform studies on learning-based control, particularly in systems where task-space precision is critical.
Research Focus
Key Achievements
Top Papers
- 1