S. Arimoto

About

Seiji Arimoto stands as a pioneering figure in robotics and control theory, whose decades of influential research have fundamentally shaped how autonomous systems learn and adapt. Best known for introducing the concept of "iterative learning control" in his landmark 1984 paper on betterment processes (367 citations), Arimoto revolutionized how engineers approach repetitive motion tasks in servo and mechatronics systems, establishing a new paradigm in which machines autonomously improve their performance through repeated trials. This foundational contribution inspired a global research community and remains a cornerstone of modern control theory. Arimoto made equally significant strides in robot manipulator control, rigorously analyzing the stability and robustness of PID feedback schemes (273 citations) and demonstrating the practical effectiveness of PD controllers for trajectory tracking (108 citations). His work on approximate Jacobian control (270 citations) elegantly addressed real-world uncertainties in robot kinematics and dynamics, while his research on multi-finger robotic grasping (161 citations) advanced dexterous manipulation theory. With contributions spanning learning control laws (185 citations), cooperative multi-robot systems (120 citations), and uncertain Jacobian feedback (139 citations), Arimoto's cumulative impact — exceeding 2,000 citations across his most recognized works — marks him as an indispensable architect of intelligent robotics and adaptive control.

Research Focus

Key Achievements

Top Papers

1. 1
   Bettering operation of dynamic systems by learning: A new control theory for servomechanism or mechatronics systems

   367 citations · 1984
2. 2
   Dynamic sliding PID control for tracking of robot manipulators: theory and experiments

   323 citations · 2003
3. 3
   Stability and robustness of PID feedback control for robot manipulators of sensory capability

   273 citations · 1984
4. 4
   Approximate jacobian control for robots with uncertain kinematics and dynamics

   270 citations · 2003
5. 5
   Realization of robot motion based on a learning method

   266 citations · 1988
6. 6
   Learning control theory for dynamical systems

   185 citations · 1985
7. 7
   Principles of superposition for controlling pinch motions by means of robot fingers with soft tips

   161 citations · 2001
8. 8
   Feedback control for robotic manipulator with an uncertain Jacobian matrix

   139 citations · 1999
9. 9
   Cooperative motion control of multiple robot arms or fingers

   120 citations · 2005
10. 10
    Is a local linear PD feedback control law effective for trajectory tracking of robot motion?

    108 citations · 2003

Key Collaborators

Contact & Links