  Over the past few years, soccer-playing humanoid robots have advanced
significantly. Elementary skills, such as bipedal walking, visual perception,
and collision avoidance have matured enough to allow for dynamic and exciting
games. When two robots are fighting for the ball, they frequently push each
other and balance recovery becomes crucial. In this paper, we report on
insights we gained from systematic push experiments performed on a bipedal
model and outline an online learning method we used to improve its
push-recovery capabilities. In addition, we describe how the localization
ambiguity introduced by the uniform goal color was resolved and report on the
results of the RoboCup 2013 competition.
