Shigeki Matsumoto
Papers
2
Total Citations
12
H-Index
2
About
Shigeki Matsumoto is a pioneering researcher at the intersection of industrial robotics, optical networking, and neuromorphic computing. His work centers on enabling geographically unconstrained robot control and advancing large-scale reservoir computing for autonomous systems. In a landmark 2016 study (10 citations), Matsumoto demonstrated the first geographically unconstrained control of an industrial robot for surface blending by jointly employing SDN-based optical transport networks and edge computing. This breakthrough showed that cloud/edge technology can significantly improve the controllability of industrial robots, opening new possibilities for remote manufacturing and teleoperation. More recently, Matsumoto has pushed the boundaries of neuromorphic hardware with his 2024 work on FPGA implementation of Chaotic Boltzmann Machine Reservoir Computing (CBM-RC). By designing a large-scale reservoir architecture optimized for sensor information prediction in autonomous mobile robots, he addresses critical challenges in real-time, energy-efficient computation. His contributions bridge the gap between advanced networking, edge intelligence, and hardware-accelerated AI, positioning him as a key innovator in next-generation robotic systems.
Research Focus
Key Achievements
Top Papers
- 1First demonstration of geographically unconstrained control of an industrial robot by jointly employing SDN-based optical transport networks and edge compute10 citations · 2016
- 2