Thiago Filipe de Medeiros
Papers
1
Total Citations
10
H-Index
1
About
Thiago Filipe de Medeiros is a researcher at the forefront of applying artificial intelligence to competitive robotics, with a primary focus on deep reinforcement learning for multi-agent systems. His most notable contribution is the development of a virtual learning framework that trains robots to execute complex, strategic behaviors in the high-speed environment of the IEEE Very Small Size Soccer competition. In his highly cited 2020 work, de Medeiros demonstrated how agents can autonomously learn to intercept the ball on their own side of the field, moving beyond hand-coded rules to enable adaptive, real-time decision-making. This research bridges the gap between simulated training and physical robot performance, offering a scalable approach to mastering dynamic, adversarial tasks. By integrating deep reinforcement learning into a well-known robotic benchmark, his work has not only advanced the state of the art in robot soccer strategy but also provided a valuable template for applying AI to other domains requiring rapid, coordinated responses. With 10 citations, this paper stands as a key reference for researchers exploring the intersection of reinforcement learning and competitive robotics.
Research Focus
Key Achievements
Top Papers
- 1Deep Reinforcement Learning Applied to IEEE Very Small Size Soccer Strategy10 citations · 2020