The RoboCup - NAIST

Through robotic soccer issue, we focus on “perception” and “situation and behavior” problem among RoboCup physical agent challenges [1]. So far, we have implemented some behaviors for playing soccer by combining four primitve processes (motor control, camera control, vision, and behavior generation processes)[2]. Such behaviors were not sophisticated very much because they were fully implemented by the human programmer. In order to improve the performance of such behaviors, a kind of learning algorithm would be useful during off/on-line skill development phase. To acquire purposive behavior for a goalie, we have developed a robot learning method based on system identification approach. We also have developed the vision system with on-line visual learning function [3]. This vision system can adapt to the change of lighting condition in realtime. This year, we refined some behaviors using such learning algorithms. Furthermore, we constructed a new robot equipped with an omnidirectional camera in addition to an active vision system so as to enlarge view of our soccer robot. Using this omnidirectional camera, such robot can recognize its location in the soccer field. In the RoboCup99 competition, our team had 9 games including roundrobin, wild card match, and finals. We won 5 games and lost 4 games. Finally, our team reached the quaterfinals. But, our team lost the game against the CS Freiburg.