RoboCupSimData: A RoboCup soccer research dataset
This dataset addresses the need for standardized robotics data in machine learning research, though it is incremental as it builds on existing RoboCup resources.
The authors compiled a large dataset from RoboCup Soccer Simulation League matches, containing 1125 games with over 180 hours of gameplay and 17GB of data, which includes both ground truth and noisy local percepts for each robot.
RoboCup is an international scientific robot competition in which teams of multiple robots compete against each other. Its different leagues provide many sources of robotics data, that can be used for further analysis and application of machine learning. This paper describes a large dataset from games of some of the top teams (from 2016 and 2017) in RoboCup Soccer Simulation League (2D), where teams of 11 robots (agents) compete against each other. Overall, we used 10 different teams to play each other, resulting in 45 unique pairings. For each pairing, we ran 25 matches (of 10mins), leading to 1125 matches or more than 180 hours of game play. The generated CSV files are 17GB of data (zipped), or 229GB (unzipped). The dataset is unique in the sense that it contains both the ground truth data (global, complete, noise-free information of all objects on the field), as well as the noisy, local and incomplete percepts of each robot. These data are made available as CSV files, as well as in the original soccer simulator formats.