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Learning to Improve Capture Steps for Disturbance Rejection in Humanoid Soccer

  • Marcell Missura
  • Cedrick Münstermann
  • Philipp Allgeuer
  • Max Schwarz
  • Julio Pastrana
  • Sebastian Schueller
  • Michael Schreiber
  • Sven Behnke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8371)

Abstract

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.

Keywords

Humanoid Robot Orbital Energy Iterative Learning Control Bipedal Walking Soccer Game 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Marcell Missura
    • 1
  • Cedrick Münstermann
    • 1
  • Philipp Allgeuer
    • 1
  • Max Schwarz
    • 1
  • Julio Pastrana
    • 1
  • Sebastian Schueller
    • 1
  • Michael Schreiber
    • 1
  • Sven Behnke
    • 1
  1. 1.Autonomous Intelligent Systems, Computer ScienceUniv. of BonnGermany

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