Learning to Improve Capture Steps for Disturbance Rejection in Humanoid Soccer
This work addresses a specific challenge in humanoid robotics for soccer applications, representing an incremental improvement in disturbance rejection.
The paper tackled the problem of improving push-recovery capabilities in soccer-playing humanoid robots during physical interactions, resulting in enhanced balance recovery as demonstrated in systematic experiments and the RoboCup 2013 competition.
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.