SoccerKicks: A Dataset of 3D dead ball kicks reference movements for humanoid robots

Abstract

The possibility of robots imitating reference movements performed by experts recently emerged in the Machine Learning context. Based on Deep Reinforcement Learning (DRL), this process focuses on observing a reference movement policy and its adaptation to a robot with a similar body scheme. In the humanoid robots domain, the massive availability of videos on the internet holds the potential to provide reference movements for virtually any task performed by humans. However, 3D pose estimation algorithms based on videos are currently subject to failure due to several practical situations (poor image framing, low video quality, joints occlusions and mismatch, and so on) and typically require applying a complex methodology. This paper presents SoccerKicks, a new dataset that provides 3D reference movements of humans performing dead ball kicks (penalty and foul) obtained from reference videos suitable for use in the robotics soccer domain. In this work we describe: i) the methodology adopted for the videos selection; ii) the algorithms chosen to perform the 2D and 3D pose estimation based on the videos; iii) the evaluation of the algorithms performance; iv) the annotation on these videos and the reference movements provided. Our dataset is publicly available at https://github.com/larocs/SoccerKicks.