RoboCupSimData: Software and Data for Machine Learning from RoboCup Simulation League

Michael, Olivia; Obst, Oliver; Schmidsberger, Falk; Stolzenburg, Frieder

doi:10.1007/978-3-030-27544-0_19

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Because game evaluation can be enhanced by using richer data with less noise, data generation is a powerful tool for that purpose. Michael et al (143) generated game data with incomplete and noisy percepts (as sent to each player) in addition to a ground-truth log file created by the simulator (global, complete, noise-free information on all objects on the field). These data were made available as comma-separated value (CSV) files as well as in the original soccer simulator formats.…”

Section: Performance Checkmentioning

confidence: 99%

Scientific and Technological Challenges in RoboCup

Asada

Stryk

2020

Annu. Rev. Control Robot. Auton. Syst.

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Since its inception in 1997, RoboCup has developed into a truly unique and long-standing research community advancing robotics and artificial intelligence through various challenges, benchmarks, and test fields. The main purposes of this article are to evaluate the research and development achievements so far and to identify new challenges and related new research issues. Unlike other robot competitions and research conferences, RoboCup eliminates the boundaries between pure research activities and the development of full system designs with hardware and software implementations at a site open to the public. It also creates specific scientific and technological research and development challenges to be addressed. In this article, we provide an overview of RoboCup, including its league structure and related research issues. We also review recent studies across several research categories to show how participants (called RoboCuppers) address the research and development challenges before, during, and after the annual competitions. Among the diversity of research issues, we highlight two unique aspects of the challenges: the platform design of the robots and the game evaluations. Both of these aspects contribute to solving the research and development challenges of RoboCup and verifying the results from a common perspective (i.e., a more objective view). Finally, we provide concluding remarks and discuss future research directions.

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Section: Performance Checkmentioning

confidence: 99%

Scientific and Technological Challenges in RoboCup

Asada

Stryk

2020

Annu. Rev. Control Robot. Auton. Syst.

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“…A soccer simulation game lasts 10 mins and is divided into 6000 time steps where the length of each cycle is 100 ms. Logfiles contain information about the game, in particular about the current positions of all players and the ball including velocity and orientation for each cycle. Michael et al (2019) describe a research dataset using some of the released binaries of the RoboCup 2D soccer simulation league (Chen et al, 2003) from 2016 and 2017 (see also . In our experiments we evaluated ten games of the top-five teams (available from https://bitbucket.org/oliverobst/robocupsimdata), considering only the (x, y)-coordinates of the ball and the altogether 22 players for all time points during the so-called "play-on" mode (see also Steckhan, 2018).…”

Section: Replaying Soccer Gamesmentioning

confidence: 99%

The Power of Linear Recurrent Neural Networks

Stolzenburg¹,

Litz²,

Michael³

et al. 2018

Preprint

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Recurrent neural networks are a powerful means to cope with time series. We show how a type of linearly activated recurrent neural networks can approximate any timedependent function f (t) given by a number of function values. The approximation can effectively be learned by simply solving a linear equation system; no backpropagation or similar methods are needed. Furthermore, the network size can be reduced by taking only the most relevant components of the network. Thus, in contrast to others, our approach not only learns network weights but also the network architecture. The networks have interesting properties: They end up in ellipse trajectories in the long run and allow the prediction of further values and compact representations of functions. We demonstrate this by several experiments, among them multiple superimposed oscillators (MSO) and robotic soccer. Predictive neural networks outperform the previous state-of-the-art for the MSO task with a minimal number of units.

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Mathematical Model of the Player Control in Soccer Simulation

Belyaev

2021

2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

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RoboCupSimData: Software and Data for Machine Learning from RoboCup Simulation League

Cited by 3 publications

References 9 publications

Scientific and Technological Challenges in RoboCup

Scientific and Technological Challenges in RoboCup

The Power of Linear Recurrent Neural Networks

Mathematical Model of the Player Control in Soccer Simulation

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