RoboCup 2019 AdultSize Winner NimbRo: Deep Learning Perception, In-Walk Kick, Push Recovery, and Team Play Capabilities

TL;DR

This paper details the NimbRo team's advancements in deep learning perception, in-walk kicking, push recovery, and team strategies that led to winning RoboCup 2019 in the AdultSize humanoid soccer competition.

Contribution

Introduction of integrated deep learning vision, in-walk kicking, and push recovery methods, enhancing team coordination and robustness in humanoid soccer robots.

Findings

Won RoboCup 2019 AdultSize tournament

Developed a deep learning vision system

Implemented in-walk kick and push recovery

Abstract

Individual and team capabilities are challenged every year by rule changes and the increasing performance of the soccer teams at RoboCup Humanoid League. For RoboCup 2019 in the AdultSize class, the number of players (2 vs. 2 games) and the field dimensions were increased, which demanded for team coordination and robust visual perception and localization modules. In this paper, we present the latest developments that lead team NimbRo to win the soccer tournament, drop-in games, technical challenges and the Best Humanoid Award of the RoboCup Humanoid League 2019 in Sydney. These developments include a deep learning vision system, in-walk kicks, step-based push-recovery, and team play strategies.