拡張現実環境におけるロボット学習

拡張現実環境におけるゴールキーパー戦略の自律学習

• Demonstration of our system (internal state)
• Goalie learning in our system (internal state)
• Portable debugging system without ceiling cameras

発表論文

Hayato Kobayashi, Tsugutoyo Osaki, Tetsuro Okuyama, Akira Ishino, and Ayumi Shinohara. "Development of an Augmented Environment and Autonomous Learning for Quadruped Robots", In RoboCup 2008: Robot Soccer World Cup XII. Springer-Verlag, to appear.

Abstract. This paper describes an interactive experimental environment for autonomous soccer robots, which is a soccer field augmented by utilizing camera input and projector output. This environment, in a sense, plays an intermediate role between simulated environments and real environments. We can simulate some parts of real environments, e.g., real objects such as robots or a ball, and reflect simulated data into the real environments, e.g., to visualize the positions on the field, so as to create a situation that allows easy debugging of robot programs. As an application in the augmented environment, we address the learning of goalie strategies on real quadruped robots in penalty kicks. Our robots learn and acquire sophisticated strategies in a fully simulated environment, and then they autonomously adapt to real environments in the augmented environment.