RoboCup: The Robot World Cup Initiative Based on Wikipedia and presentations by Mariya Miteva, Kevin Lam, Paul Marlow

What is RoboCup? RoboCup is an international robotics competitionrobotics competition The official goal of the project –By mid-21st century, a team of fully autonomous humanoid robot soccer players shall win the soccer game, complying with the official rule of the FIFA, against the winner of the most recent World Cup. autonomoushumanoidrobotsoccer FIFAWorld Cup Sample Game Video

The Different Competitions RoboCup Soccer –Small size –Middle size –Four-legged –Humanoid –Simulation RoboCup Rescue RoboCup Junior –Soccer Challenge –Dance Challenge –Rescue Challenge –Genereal

Examples of RoboCup Leagues four-legged small size middle size simulation

RoboCup as a Standard AI Problem Standard problems are the driving force of AI research. For example, research on chess lead to the discovery of powerful search algorithms. AI research should be focused on solving real life problems, but often face social or economic constraints. RoboCup is designed to meet the need of handling real world complexity, though in a limited environment, while maintaining an affordable problem size and research cost.

Why is soccer a good option? Soccer challenges –dynamic environment –real-time decision making and action –high level of uncertainty and incomplete information –sensor-acquired information –distributed control and cooperation Areas of research include real-time sensor fusion, reactive behavior, strategy acquisition, learning, real-time planning, multi-agent systems, context recognition, vision, strategic decision making, motor control, intelligent robot control, etc.

Rules There are real robot, special skill, and simulation competitions, each having different rules usually controlled by human referees. In real robot competitions attributes of the environment such as the size of the field and the goal, the colors of the field, balls and robots, the maximum number of robots in a team, etc. are predetermined and differ from league to league. Most physical fouls are considered unintentional and ignored.

More Rules In simulation RoboCup a Soccer Server provides the virtual environment and controls the communication between the virtual robots and their control programs. Robots do not know their exact position, but only their position relative to landmarks Simulation allows development of advanced coordination systems without the physical constraints of real robots

Research Issues: The goal of the competitions is to stimulate research and advancement in both designing and programming robots. The major areas of interest according to the article are: –Collaboration in a multi-agent environment –Design and control –Vision and sensor fusion –Learning

Collaboration Each team has – common goal (to win the game), incompatible with the goal of the opponent team, and several subgoals (scoring) – team-wide strategies to fulfill the common goal and local and global tactics to achieve subgoals Complications: –Dynamic environment –Locally limited perception –Different roles of team players –Limited communication among players Trade-off between communication cost and accuracy of the global plan Final goal - promising local plans at each agent and coordination of these local plans

RoboCup Simulator Server Monitor clients Player clients (i.e. agents!) Coach clients

Sample Team Strategy

Design and Control Existing robots have been designed to perform mostly single behavior actions A RoboCup player needs to perform multiple subtasks( shooting, passing, heading, throwing, etc.) and meanwhile avoid opponents Two approaches in building a RoboCup player: –A combination of many specialized components –One or two multitasking components The final goal of building a successful Humanoid Soccer Player currently appears to be unfeasible Humanoid Soccer Player

Vision and Sensor Fusion Computer Vision researchers have been seeking for 3D reconstruction of 2D visual information 3D reconstruction is too time-consuming for a RoboCup player to react in real time, Other sensors (sonar, touch and force) need to be incorporated to provide further information, which can not be acquired by vision A method of sensor fusion/integration is necessary Robot Vision Video Robot Vision Video 2

Learning Because of the dynamic and uncertain RoboCup environment, programming robot behaviors for all possible situations is impossible. Reinforcement learning is promising in RoboCup, since it allows acquisition of advanced behaviors with little prior knowledge. Almost all existing reinforcement learning has been used in computer simulation, but not in physical applications.

Learning Robots first learn skills in one to one competitions. To simplify the process, task decomposition is implemented –two skills are independently acquired and then coordinated through learning Later on, many-to-many competitions are considered. – It’s hard to find a simple method for learning collective behavior – Pattern finding methods or “coordination by imitation” are used The most difficult task is integration of the learning methods in a physical environment.

Current Champions (Osaka 2005) Soccer –Small Sized: Fu-Fighters (German) –Middle Sized: EIGEN Keio Univ (Japan) –4 Legged: German Team (German) –Humanoid 2-on-2: Team Osaka (Japan) –2D Simulation: Brainstormers 2D (Germany) –3D Simulation: Aria (Iran) Rescue –Simulation: Impossibles (Iran) –Robot: Toin Pelican (Japan)