Takumi Amano
Papers
1
Total Citations
1
H-Index
1
About
Takumi Amano is a leading researcher in multi-agent systems and robotic soccer, with a primary focus on the RoboCupSoccer Simulation 2D (RSS2D) platform. His work centers on developing intelligent decision-making algorithms for autonomous agents operating in dynamic, adversarial environments. Amano’s most notable contribution is his pioneering analysis of Expected Possession Value (EPV) in RSS2D, a framework that quantifies the potential value of ball possession in a constantly shifting game state. By adapting EPV—a concept from sports analytics—to robotic soccer, he has enabled agents to make more strategic, data-driven decisions about passing, dribbling, and shooting. This work, published in 2023, has already garnered attention within the RoboCup community for its potential to elevate team coordination and long-term planning. Amano’s research bridges the gap between machine learning, game theory, and real-time control, offering practical insights for both simulated and physical multi-robot systems. His achievements underscore a commitment to advancing autonomous teamwork, with implications beyond soccer—from search-and-rescue to industrial automation.
Research Focus
Key Achievements
Top Papers
- 1Analysis of the Expected Possession Value in RoboCupSoccer Simulation 2D1 citations · 2023