RoboCup 2019 AdultSize Winner NimbRo: Deep Learning Perception, In-Walk Kick, Push Recovery, and Team Play Capabilities
This work addresses the problem of improving robot soccer performance for the RoboCup Humanoid League, though it is incremental as it builds on existing capabilities.
The paper tackled the challenges of the RoboCup 2019 AdultSize class, including increased players and field dimensions, by developing deep learning vision, in-walk kicks, push recovery, and team strategies, leading to winning the tournament and Best Humanoid Award.
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.