RoboCup is an international robotics competition The official goal
of the projectBy 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. Sample Game Video
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 challengesdynamic environmentreal-time decision making and
actionhigh level of uncertainty and incomplete
informationsensor-acquired informationdistributed control and
cooperationAreas 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 landmarksSimulation
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 environmentDesign and controlVision and sensor
fusionLearning
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 subgoalsComplications:Dynamic environmentLocally limited
perceptionDifferent roles of team playersLimited communication
among playersTrade-off between communication cost and accuracy of
the global planFinal 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 actionsA RoboCup player needs to perform multiple
subtasks( shooting, passing, heading, throwing, etc.) and meanwhile
avoid opponentsTwo approaches in building a RoboCup player:A
combination of many specialized componentsOne or two multitasking
componentsThe final goal of building a successful Humanoid Soccer
Player currently appears to be unfeasible
Vision and Sensor Fusion
Computer Vision researchers have been seeking for 3D reconstruction
of 2D visual information3D 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 visionA method of sensor
fusion/integration is necessaryRobot Vision VideoRobot 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. Its 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 2008 (Germany)
SoccerSmall 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) RescueSimulation: Impossibles (Iran) Robot:
Toin Pelican (Japan)
