RoboCup 2D Soccer Simulation League: Evaluation Challenges

We summarise the results of RoboCup 2D Soccer Simulation League in 2016 (Leipzig), including the main competition and the evaluation round. The evaluation round held in Leipzig confirmed the strength of RoboCup-2015 champion (WrightEagle, i.e. WE2015) in the League, with only eventual finalists of 2016 competition capable of defeating WE2015. An extended, post-Leipzig, round-robin tournament which included the top 8 teams of 2016, as well as WE2015, with over 1000 games played for each pair, placed WE2015 third behind the champion team (Gliders2016) and the runner-up (HELIOS2016). This establishes WE2015 as a stable benchmark for the 2D Simulation League. We then contrast two ranking methods and suggest two options for future evaluation challenges. The first one, "The Champions Simulation League", is proposed to include 6 previous champions, directly competing against each other in a round-robin tournament, with the view to systematically trace the advancements in the League. The second proposal, "The Global Challenge", is aimed to increase the realism of the environmental conditions during the simulated games, by simulating specific features of different participating countries.Comment: 12 pages, RoboCup-2017, Nagoya, Japan, July 201

Meeskonna rUNSWift s ¨usteemi p˜ohjal k¨aitumisloogika arendamine 2015 RoboCup v˜oistluse jaoks

The RoboCup Standard Platform League has two teams, each consisting of five robots play football against each other in a semi-controlled setting. The robots used have the same hardware and modifications are not allowed. The purpose of this thesis was to find a method to improve the overall performance displayed during 2014 RoboCup and implement the method(s). During the course of the project, a new codebase, developed by team rUNSWift, was evaluated, tested and then adopted as it offered improvements compared to the Austin Villa codebase used in 2014. As the codebase offered only basic core functionality, a behaviour module needed to be implemented to offer both low- and high-level behaviours. The behaviours developed provide low-level functionality for movement, ball alignment and targeting and high-level functionality for basic soccer gameplay according to RoboCup 2015 rules. The individual strategy mimics the system used in 2014 with the main difference being the ability to recognize our teammates and then use that information to avoid collisions while trying to hit a ball that is in the common playing area of the two robots. The kick and walk performance appear more stable, as they are both dynamically generated using rUNSWift’s motion system. The walk is also offers greater configurability and needs careful calibration for tuning the input parameters

FC Portugal 3D Simulation Team: Team Description Paper 2020

The FC Portugal 3D team is developed upon the structure of our previous Simulation league 2D/3D teams and our standard platform league team. Our research concerning the robot low-level skills is focused on developing behaviors that may be applied on real robots with minimal adaptation using model-based approaches. Our research on high-level soccer coordination methodologies and team playing is mainly focused on the adaptation of previously developed methodologies from our 2D soccer teams to the 3D humanoid environment and on creating new coordination methodologies based on the previously developed ones. The research-oriented development of our team has been pushing it to be one of the most competitive over the years (World champion in 2000 and Coach Champion in 2002, European champion in 2000 and 2001, Coach 2nd place in 2003 and 2004, European champion in Rescue Simulation and Simulation 3D in 2006, World Champion in Simulation 3D in Bremen 2006 and European champion in 2007, 2012, 2013, 2014 and 2015). This paper describes some of the main innovations of our 3D simulation league team during the last years. A new generic framework for reinforcement learning tasks has also been developed. The current research is focused on improving the above-mentioned framework by developing new learning algorithms to optimize low-level skills, such as running and sprinting. We are also trying to increase student contact by providing reinforcement learning assignments to be completed using our new framework, which exposes a simple interface without sharing low-level implementation details

Multi-Agent Coordination for a Partially Observable and Dynamic Robot Soccer Environment with Limited Communication

RoboCup represents an International testbed for advancing research in AI and robotics, focusing on a definite goal: developing a robot team that can win against the human world soccer champion team by the year 2050. To achieve this goal, autonomous humanoid robots' coordination is crucial. This paper explores novel solutions within the RoboCup Standard Platform League (SPL), where a reduction in WiFi communication is imperative, leading to the development of new coordination paradigms. The SPL has experienced a substantial decrease in network packet rate, compelling the need for advanced coordination architectures to maintain optimal team functionality in dynamic environments. Inspired by market-based task assignment, we introduce a novel distributed coordination system to orchestrate autonomous robots' actions efficiently in low communication scenarios. This approach has been tested with NAO robots during official RoboCup competitions and in the SimRobot simulator, demonstrating a notable reduction in task overlaps in limited communication settings.Comment: International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023) - Italian Workshop on Artificial Intelligence and Robotics (AIRO) Rome, 6 - 9 November, 202

Robot Detection Using Gradient and Color Signatures

Tasks which are simple for a human can be some of the most challenging for a robot. Finding and classifying objects in an image is a complex computer vision problem that computer scientists are constantly working to solve. In the context of the RoboCup Standard Platform League (SPL) Competition, in which humanoid robots are programmed to autonomously play soccer, identifying other robots on the field is an example of this difficult computer vision problem. Without obstacle detection in RoboCup, the robotic soccer players are unable to smoothly move around the field and can be penalized for walking into another robot. This project aims to use gradient and color signatures to identify robots in an image as a novel approach to visual robot detection. The method, Fastgrad , is presented and analyzed in the context of the Bowdoin College Northern Bites codebase and then compared to other common methods of robot detection in RoboCup SPL

Benchmarking robot cooperation without pre-coordination in the RoboCup Standard Platform League drop-in player competition

Abstract — The Standard Platform League is one of the main competitions of the annual RoboCup world championships. In this competition, teams of five humanoid robots play soccer against each other. In 2014, the league added a new sub-competition which serves as a testbed for cooperation without pre-coordination: the Drop-in Player Competition. Instead of homogeneous robot teams that are each programmed by the same people and hence implicitly pre-coordinated, this competition features ad hoc teams, i. e. teams that consist of robots originating from different RoboCup teams and that are each running different software. In this paper, we provide an overview of this competition, including its motivation and rules. We then present and analyze the results of the 2014 competition, which gathered robots from 23 teams, involved at least 50 human participants, and consisted of fifteen 20-minute games for a total playing time of 300 minutes. We also suggest improvements for future iterations, many of which will be evaluated at RoboCup 2015. I