Masatoshi Yamaguchi
Papers
3
Total Citations
15
H-Index
3
About
Masatoshi Yamaguchi is a researcher at the forefront of intelligent robotics and neuromorphic hardware, specializing in energy-efficient computing for autonomous systems. His most impactful work introduces the "Time-domain Analog Computing with Transient states (TACT)" approach, a novel VLSI architecture that performs analog weighted-sum calculations for real-time robotic intelligence. Fabricated in 250-nm CMOS technology, this chip achieves exceptional energy efficiency, enabling robots to process complex sensor data without the power overhead of digital systems—a critical advancement for autonomous and embedded robotics. His foundational contributions also include pioneering GA-based Q-learning methods for multi-legged robot control, where he proposed the "neighboring crossover" technique to accelerate reinforcement learning in gaited locomotion. This work, published in leading venues, has garnered over 15 citations and laid groundwork for adaptive control in legged robots. Yamaguchi’s research bridges the gap between low-power analog computing and intelligent behavior, offering a path toward robots that can learn and react with minimal energy. His live demonstrations and technical papers continue to inspire innovations in neuromorphic engineering and bio-inspired robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3