An Introduction to RoboCup

1
An Introduction to RoboCup

• October 28, 2004
• by Kevin Lam
• Portions by Paul Marlow

2
Agenda

• Overview of RoboCup
• Demonstration Game
• Simulator Mechanics
• Simulator Communications Protocol
• Existing Developments, Clients, Tools
• Vision Table Project

3
Overview of RoboCup

• The Robot World Cup Initiative
• By the year 2050, develop a team of fully
  autonomous humanoid robots that can win against
  the human world soccer champion team.
• A standard problem for AI research

4
Overview of RoboCup

• Started in 1992 as the Robot J-League (Japan)
• First games and conferences in 1997
• Workshops, conferences and yearly competitions

5
RoboCup Leagues
four-legged
small size
middle size
simulation
6
World Championships
RoboCup 2004 - Lisbon
RoboCup 2003 - Padua
RoboCup 2002 - Fukuoka/Busan
RoboCup 2001 - Seattle
RoboCup 2000 - Melbourne
RoboCup 1999 - Stockholm
RoboCup 1998 - Paris
RoboCup 1997 - Nagoya
RoboCup 2005 - Japan
7
RoboCup Simulator
simulation

• Server (2-D and new 3-D)
• Monitor clients
• Player clients (i.e. agents!)
• Coach clients

8
Demonstration
9
Simulator Mechanics
10
Clients and Server

• One server
• Up to 11 clients per team (plus coach)
• Clients/server communicate via UDP/IP

11

• Can be written in any language (C, Java,
  Smalltalk, ...)
• Can be run on same machine or a network
• Clients may talk only to the server... not to
  each other!

12

• Monitor(s) used to visualize the action and/or
  interrupt the game
• Coaches (optional) to give guidance to teams

13
RoboCup Clients

• Autonomous agents
• The brains of the players
• Sensory information received from server,
  decision made, action command sent back to server
• One agent represents one player
• One agent designates itself as the goalie

14
RoboCup Clients
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RoboCup Server

• Keeps time (typically 6000 simulator cycles, 10
  cycles per second)
• Receives client messages, updates world model,
  sends back new status information
• Automated Referee tracks current play mode

16
Play Modes
kick off
game on
goals
out of field
offsides
half time and game over
17
Starting a Game

• Download and install applications (running the
  configure and make scripts for the Unix / Linux
  systems)
• Run the Server (default host is localhost and
  default port is 6000)
• Run the Monitor, connecting to the host and port
  of the Server
• Connect the players to the Server host and port
• Start the kick-off!

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Communications Protocol
19
Connection Protocols

• From client to server
• (init TeamName (version VerNum) (goalie))
• (reconnect TeamName Unum)
• (bye)
• From server to client
• (init Side Unum PlayMode) as a response for both
  client init and reconnect messages
• Side l r Unum 1 11

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An Example

• send 6000 (init MyTeam)
• recv 1067 (init l 1 before_kick_off)
• recv 1067 (see 0 ((goal r) 66.7 33) ((flag r t)
  55.7 3) ((flag p r t) 42.5 23) ((flag p r c) 53.5
  43)) recv 1067 (see 0 ((goal r) 66.7 33) ((flag
  r t) 55.7 3) ((flag p r t) 42.5 23) ((flag p r c)
  53.5 43))
• send 1067 (move 0 0)

21
Client Commands
Client Command Once per Cycle
(catch Direction) Yes
(change_view Width Quality) No
(dash Power) Yes
(kick Power Direction) Yes
(move X Y) Yes
(say Message) No
(sense_body) No
(score) No
(turn Moment) Yes
(turn_neck Angle) Yes
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Client Sensor Protocol

• Three main message types
• Hear
• See
• Sense_Body
• Noise models for each

23
Hear Messages

• Can hear one message per team per cycle
• (hear Time Sender Message)
• Sender online_coach_left/right, referee, self,
  Direction
• Direction -180 180 degrees

24
Hear Example

• (hear 18 self FCPortugal ETV 18 world_status r 2
  0.98 -10.95 16.64 1 0.95
• 0.00 0.00 1 0.95 0.00 0.00 3 0 OtGL- - fGXtZ 3F.-
  /sdhAl 1p.- 0 40.0 0.0 0 2 )

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See Messages

• (see Time ObjInfo)
• ObjInfo
• (ObjName Distance Direction DistChange DirChange
  BodyFacingDir HeadFacingDir) or
• (ObjName Distance Direction DistChange DirChange)
  or
• (ObjName Distance Direction) or
• (ObjName Direction)

26
See Messages

• ObjName
• (p TeamName UniformNum goalie) or
• (b) or
• (g lr) or
• (f c) (f lcr tb) (f p lr tcb)
• (f g lr tb) (f lrtb 0)
• (f tb lr 1020304050)
• (f lr tb 102030)
• (l lrtb)
• (B) (F) (G) (P)

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Flags and Lines
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See Example

• (see 18 ((f r t) 44.7 -22) ((f g r b) 47.9 30)
  ((f g r t) 42.5 13) ((f p r c) 30.3 34 -0 0) ((f
  p r t) 25.3 -7 0 0) ((f t r 40) 36.2 -37) ((f t r
  50) 44.7 -29) ((f r 0) 49.4 20) ((f r t 10) 47 8)
  ((f r t 20) 46.5 -3) ((f r t 30) 48.4 -15) ((f r
  b 10) 53.5 30) ((f r b 20) 59.1 38) ((f r t) 44.7
  -22) ((f g r b) 47.9 30) ((g r) 44.7 22) ((f g r
  t) 42.5 13) ((f p r c) 30.3 34) ((f p r t) 25.3
  -7 0 0) ((f t r 40) 36.2 -37) ((f t r 50) 44.7
  -29) ((f r 0) 49.4 20) ((f r t 10) 47 8) ((f r t
  20) 46.5 -3) ((f r t 30) 48.4 -15) ((f r b 10)
  53.5 30) ((f r b 20) 59.1 38) ((p "FCPortugal")
  36.6 28) ((l r) 41.7 -89))

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Sense_Body Messages

• (sense_body Time
• (view_mode high low narrow normal
  wide)
• (stamina StaminaEffort)
• (speed AmountOfSpeed DirectionOfSpeed)
• (head_angle HeadAngle)
• (kick KickCount)
• (dash DashCount)
• (turn TurnCount)
• (say SayCount)
• (turn_neck TurnNeckCount)
• (catch CatchCount)
• (move MoveCount)
• (change_view ChangeViewCount))

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Sense_Body Example

• (sense_body 19 (view_mode high normal) (stamina
  4000 1) (speed 0 0) (head_angle 0) (kick 0) (dash
  0) (turn 0) (say 98) (turn_neck 0))

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Coaches

• Privileged clients used to provide assistance
• Receives noise-free view of the whole field
• Can only send occasional messages to players
  (info, advice, freeform, etc.)
• Used for opponent modelling, game analysis,
  giving strategic tips to teammates

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RoboCup Clients
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Some RoboCup Clients

• UvA Trilearn (Amsterdam) (2003 champion)
• CMUnited (Carnegie Mellon)
• Everest (China)
• FC Portugal 2003 (Portugal)
• HELIOS (Japan)
• Magma Furtwangen (Germany)

34
Typical Approaches

• Hard-coded behaviour
• Scripted behaviour (e.g. planning)
• Neural Networks
• Opponent Modelling
• Layered Learning
• Behaviour Networks

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Example UvA Trilearn

• Coordination Graphs for passing, anticipating
  passes
• Layered skills hierarchy (pass, intercept)
• Formations
• Behaviour modelling of opponents

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Example FC Portugal

• Strategic, ball possession, ball recovery
  behaviours
• Situation Based Strategic Positioning
• Given game situation, calculated best position
  and go there

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Example Krislet

• Only one strategy run to the ball and try to
  kick it!
• Surprisingly effective
• Written in Java, easy to extend

38
Example Stripslet

• STRIPS style linear planning
• Written by Aloke Wiki
• Based off of Krislet
• A Stripslet implementation is made up of four
  main concepts Actors, Sensors, Actions, and a
  GoalList

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Actors

• These are designed to implement a specific
  action, such as run-to-ball or score-goal
• Contains two main elements, an execute method
  providing the functionality, and a name which
  is used as a key into a hash table.
• To add a new Actor
• Add a new class to the Actor.java, implementing
  the Actor Interface
• Add the new Actor to the ActorTable hash in
  StripsBrain.java

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Sensors

• These consist of objects which include a sense
  method, as well as a name for a hash table key.
• Each is designed to determine the boolean outcome
  of a specific predicate, such as can-see-goal.
• To add a new Sensor
• Add a new class to Sensor.java, implementing the
  Sensor Interface
• Add the new Sensor to the SensorTable hash in
  StripsBrain.java.

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Actions

• Actions consist of four members name,
  precoditionList, addList, deleteList.
• name corresponds directly to that of the Actor
  name property (e.g. score-goal)
• precoditionList is a list of predicates which
  must be true before execution of the action (e.g.
  have-ball can-see-goal)
• addList is a list of predicates that will be true
  upon execution of the action (e.g. ball-in-net)
• deleteList is a list of predicates that will be
  false upon execution of the action (e.g.
  have-ball)

42
Actions

• To add an Action
• StripsBrain maintains a list of actions, so any
  new actions must be added to this list
• Note that multiple predicates in a list are
  separated by spaces
• actionList.add(new Action("score-goal", // name
• "have-ball can-see-goal", // pre
• "ball-in-net", // add
• "have-ball")) // delete

43
GoalList

• Contains a list of goals for which the agents try
  to achieve
• Currently the default list consists of only one
  predicate ball-in-net
• Interesting possibilities include
• Adding multiple goals
• Re-evaluating the goal list each cycle depending
  on the environment

44
Developing Clients

• www.robocup.org
• Soccer Server Manual
• Start with Krislet and Stripslet
• Soccer Server 9.4.5

45
Other Projects

• Soccer Server 3D
• 3D Soccer Monitor (Robologvis)
• Tools to convert logs into Flash animations
• Log analyzers (eg. Team Assistant, Paul Marlows
  Classifier)

46
Vision Table Project

• Scene Learning and Recognition
• Could an agent learn from observing another?
• (original goal - learn from humans)
• Could a team observe another and as a result
  start playing similar to what it saw?
• As little human intervention as possible!

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Project Goals

• Scene Generation
• Extract data from game logs
• Create scenes - snapshots
• Scene Learning (Reasoning)
• Associate key scenes, patterns of scenes, with
  action taken

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Project Goals

• Scene Recognition
• Build a RoboCup client
• Searches from a stored library of key scenes and
  actions uses most relevant to determine course
  of action
• Evaluate performance (resemblances?)

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Scene Descriptions

• A form of generalization and knowledge
  representation
• Size of slices impacts complexity, accuracy

Extreme Left Left Center Right Extreme Right
Immediate Ball
Near Team Mate
Distant Goal Opponent
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References

• Most of the information about RoboCup itself was
  taken using from the RoboCup Soccer Server
  manual.
• For the latest manuals and code, visit the
  RoboCup project website at
• http//sourceforge.net/projects/sserver

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