cognitive roboticscognitive roboticsnaoth/robo... · f h ibl t id l ti lfor each possible percept...

Cognitive RoboticsCognitive Robotics

Behavior ControlBehavior Control

Hans-Dieter Burkhard/Monika DomanskaMarch 2013

OverviewIntroduction

C f CExample: Behavior Control for RoboCupControl ArchitecturesBehavior Based Robotics

Burkhard/Domanska Cognitive Robotics Behavior Control 2

Reflexes (Stimulus-Response, reactive) ( p , )


Goal directed behaviorActing according to a predefined goal


CooperationpCooperation

Joint intention (Double pass)


Behavior ControlThe robots should act appropriately to reach its tasks/plans• It can use its action skills• It can use its action skills

e.g. by motion planning/controlIt can use its sensors to adapt to the situation• It can use its sensors to adapt to the situatione.g. by sensor-actor-coupling,

b tiby perception, by world model

It d i i d t fi d b t ti• It can use decision procedures to find best options

Behavior control of robots is similar to behavior control of agents in Artificial Intelligence.

E l B li f D i I t ti A hit t (BDI)


Example: Belief-Desire-Intention Architecture (BDI)

Decision ProblemsWhich behavior is useful? (needs criteria for usefulness)

Which goal/situation is desirable? (goal selection)How can in be reached? (planning)How can in be reached? (planning)

N d l k h dNeeds look ahead:Short term: next action(s)L t fi l lLong term: final goals

Least commitment:Postpone decisions as long as possible.


p g p

Ideal Rational AgentsgDefinition by Russell/Norvig: Artificial Intelligence – A Modern Approach.

F h ibl t id l ti lFor each possible percept sequence, an ideal rational agent should do whatever action is expected to maximize its performance measure on the basis ofmaximize its performance measure, on the basis of the evidence provided by the percept sequence and whatever built-in knowledge the agent has.

Aspects of the definition:• Performance measure: determines the purposes of agent/robot• Design problem: designer has to built the necessary means

“Bounded Rationality”: Efficiency w.r.t. limited resources


DesigngThere are different principles of design:• “Classical AI”• Classical AI

prefers symbolic descriptions and inference methods“Physical symbols systems”“Physical symbols systems”

B h i l R b ti /Bi l i ll I i d R b ti• Behavioral Robotics/Biologically Inspired Robotics prefer principles from (primitive?) natural systems“Ph i l d d t ”“Physical grounded systems”


„Conscious“ Behavior„I see the light left in front.

I lik th li htI like the light.I should go to the left.I have to turn an walkI have to turn an walk.

My right wheel should move faster than the left one.etc.

Alternatively: Stimulus ResponseStimulus Response


Stimulus Response p

Likes Light

Afraid from lightBraitenberg VehicleBraitenberg Vehicle

Alternatively: Conscious“ ActingAlternatively: „Conscious Acting


Efficient behaviordepends on

Situation in the environment- Situation in the environmentaccessible by sensors, perception, world model(integration of past situations)(integration of past situations)

- Expectations about results of possible actionsl ti f h bl it ti l ievaluation of reachable situations, planning,

(integration of possible future situations)

Different control architectures depending on the time horizonsR di f i iRegarding past resp. future situations.


Classical Types of BehaviorypReactive Behavior:

like Stimulus-Response: short termlike Stimulus Response: short term„simple“ behavior patterns, simple skills

Deliberative BehaviorGoal directed, plan based behavior: long termp g„complex“ behavior

Hybrid:Combination of reactive and deliberative behaviore.g. goal driven usage of reactive skills


Behavior Types: Data StructuresypData Structures Past Situations

Data Structures Future Situations

ComplexityPast Situations(World Model)

Future Situations(Goals, Plans)

Reactive Stimulus Response lowReactive, Stimulus Response - - low

(example: Chess) - - high

Reactive with world model + - low

Reactive with world model + - high

- + low

- + high

+ + low

Deliberative + + high+ + g


How to program a double pass?p g p

1. Trial („Chess-like“):(„ )

• Foresight simulation

Choice of best alternative• Choice of best alternative


Where to intercept the ball?p

B l l tiBy calculation

By simulation

By learned behavior


Which player can intercept first?p y pBased oncalculationcalculationof intercept point


Pass to which team mate?Based on

l l ticalculationsof intercepting players


How to program a double pass?p g p1. Trial („Chess-like“):

R l• Foresight simulation

Result:Useful only for

• Choice of best alternative short term decisions2 Trial ( Emergence“)2. Trial („Emergence )

If every player behaves in an optimal way,

then a double pass emerges without planning.Result:Result:Double pass emerges


from time to time

How to program a double pass?p g p

3. Trial:3. Trial:

• Use Bratman´s concept of „bounded rationality“

Belief-Desire-Intention-Architecture (BDI)

U C B d R i• Use Case-Based Reasoning

Still under work …


Only partial plan in the beginning y p p g gAnalysis of situation <situation description>

Dribbling on path <path parameters>5´10´´

5´12´´ Kick with parameters <ball speed vector>

R th < th t >

5´12´´5´13´´

Run on path <path parameters>

5´17´´

Intercept ball at point <position>

Run to ball on path <path parameters>5 17

5´19´´5´20´´ p p p5 20


Hierarchy of Optionsy pPlaySoccer

Offensive Defensive . . .

Score OffsideTraAttackChangeWings/1DoublePass/2DoublePass/1 ...

Dribble

...

Kick

Pass... ... ... ......

Kick. . .

Run... ...

... ...

. . . . . .. . .Reposition

Burkhard/Domanska Cognitive Robotics Behavior Control 23Intercept

... ...

Result of “Deliberator“: Intention SubtreePlaySoccer


S Off id TAtt kCh Wi /1D bl P /2D bl P /1Score OffsideTrapAttackChangeWings/1DoublePass/2DoublePass/1 ...

Dribble

...

Kick . . .

Pass

... ... ... ... ......

Run

... ...... ...

. . . . . .. . . . . . . . .. . .Reposition

... ...


Intercept

... ...

Activity Path: Present state of an Intentionc y a ese s a e o a e oPlaySoccer


S r Off id TrAtt kCh Wi /1D bl P /2D bl P /1Score OffsideTrapAttackChangeWings/1DoublePass/2DoublePass/1 ...

Dribble

...

Kick. . .

Pass

... ... ... ... ......

Run

... ...... ...

. . . . . .. . . . . . . . .. . .Reposition

... ...


Intercept

... ...




Dribble

...

Kick. . .

Pass

... ... ... ... ......

Run

... ...... ...

. . . . . .. . . „Executor“ checks conditions on all. . . . . .. . .Reposition

... ...

„levels of the hierarchy and decides by least commitment principle


Intercept

... ...




Dribble

...

Kick. . .

Pass

... ... ... ... ......

Pass ready next: RunRun

... ...... ...

. . . . . .. . .

Pass ready, next: Run

. . . . . .. . .Reposition

... ...


Intercept

... ...

Double-Pass ArchitecturePredefined Option HierarchyDeliberatorExecutor

„Doubled“ 1-Pass-Architecture:1. Pass: Deliberator (goal-oriented: Intention Subtree)2 Pass: Executor (stimulus response: activity path)2. Pass: Executor (stimulus-response: activity path)

- on all levels -

Differences to “classical” ProgrammingControl flow by Deliberation (“Agent- oriented”)R i i i b 2 P h h ll l lRuntime organisation by 2 Passes through all levels


Control ArchitecturesDistribution

fInterfacesInformation flow

Sensors Actuators


Sense-Think-Act-CycleyDependency concerning time and contents

think

sense actsense

Sensors Actuators


„Horizontal“ Structure„

Sensors

Sense Think Act

A t tActuators


Synchronisationy

sensethinkact

sensethinkact

sensesensethinkact conflict


Different Complexitiesp• Sensor Signals

Si l P t• Simple Percepts

•... •Reaction (Stimulus Response)• World Model

( p )

• Choice of Skill

•....

• Planning • Simple Action

• Cooperative Planning •....

• Operation by PlanOperation by Plan

• Cooperative Plan


Example: 3-Layer-Architecturesp yDeliberative Layer:

Planning: Longterm analysis goal selectionPlanning: Longterm analysis, goal selectionTime consuming processes

Working Layer:Working Layer:Choice and scheduling of appropriate „skills“ to reach goalsN d d t tiNeeds moderate time

Reactive Layer:L l l b h i kill “Low-level-behaviors: „skills“Immediate reactions

Example: 3-Tiered (3T) Architecture (NASA)


Vertical LayersySense Think Act

WorldModel Planning PlanWorldModel:::::

Planning:::::

Plan:::::

::::: ::::: :::::

Signals Reflex Action

Sensors Actuators


How to Organize Data Flow?gSense Think ActWorldModel Planning PlanWorldModel

:::::Planning

:::::Plan

:::::::::: :::::

::::: ::::: :::::::::: :::::

Signals Response Action

Sensors Actuators


Classical One-Pass-ArchitectureSense Think Act

Worldmodel

BeliefsBeliefs

Low levelLow-levelController

Sensors Actuators


Classical Two-Pass-ArchitectureSense Think Act

W ld d lWorldmodel

KnowledgeBase

Low levelLow-levelController

Sensors Actuators


Classical Two-Pass-ArchitectureSense Think Act

Mission

W ld d l

MissionPlanner

Worldmodel

KnowledgeNavigator

Base

Low levelPilot Low-levelContoller

Sensors Actuators


Behavior Based RoboticsHypothesis:C l b h i b bi ti f i l b h i

Simple behavior by e g

Complex behavior emerges by combination of simple behaviors

Simple behavior by e.g.- Immediate reaction to sensor data (sensor-actor-coupling)

Si l h i l t f ti “ ( l d i )- Simple physical „transformation“ (clever design)

I t lli t ti ith t i t lli t thi kiIntelligent action without intelligent thinking:• No worldmodel

N b lEmergent behavior:

• No symbols • No deliberation

Complex behavior emerges by interaction of situated robots with the


environment.

„New AI“ „Since middle of 1980s

P b R d B kPapers by Rodney Brooks:„Elefants don´t play chess“„Intelligence without reason“„Intelligence without representation“ Patty Maes

Orientation on natural principles:- Emergent behavior- Situated agents/robots- No internal representation

Kismet


CoqRoomba

Critics on Classical AIGOFAI = “Good Old Fashioned AI”

Problems with Closed world assumption: everything is known“• Closed world assumption: „everything is known“

• Frame problem:„all assumptions/effects are modelled Ph i l t h th i l t b li• Physical systems hypothesis: complete symbolic representation

1966-72: Robot Shakey (Stanford)with hierarchical planner STRIPS(„Stanford Research Institute Problem Solver“)


Physical Symbol System Hypothesisy y y yp"A physical symbol system has the necessary and

ffi i t f i t lli t ti “sufficient means for intelligent action.“

Newell/Simon: "Computer Science as Empirical Inquiry: Symbols and Search“

Needs:GOFAI= „good old fashioned AI“

Needs:• Complete Descriptions of the Worlds• Algorithms for actions

Many critics(Dreyfus, Searle, Penrose, ..., Brooks, Maes, Pfeiffer...)


Physical Grounding Hypothesisy g ypThis hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in theit is necessary to have its representations grounded in the physical world. Our experience with this approach is that once this commitment is made, the need for traditional symbolic representations fades entirely. The key observation is that the world is its own best model. It is always exactly up to date. It always contains every detail there is to be known The trick isalways contains every detail there is to be known. The trick is to sense it appropriately and often enough.

T b ild t b d th h i l di h th iTo build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer ofand actuators. Typed input and output are no longer of interest. They are not physically grounded.R.A. Brooks: Elephants Don´t Play Chess


R.A. Brooks: Elephants Don t Play Chess

Physical Grounding Hypothesisy g ypThis hypothesis states that to build a system that is intelligent it is necessary to have its representations grounded in theNewit is necessary to have its representations grounded in the physical world. Our experience with this approach is that once this commitment is made, the need for traditional symbolic

NewP blrepresentations fades entirely. The key observation is that the

world is its own best model. It is always exactly up to date. It always contains every detail there is to be known The trick is

Problemalways contains every detail there is to be known. The trick is to sense it appropriately and often enough.

T b ild t b d th h i l di h th iTo build a system based on the physical grounding hypothesis it is necessary to connect it to the world via a set of sensors and actuators Typed input and output are no longer of

But: To bring the Beer from the basement, the and actuators. Typed input and output are no longer of

interest. They are not physically grounded.R.A. Brooks: Elephants Don´t Play Chess

robot should have an idea about the location etc...


R.A. Brooks: Elephants Don t Play Chess

Subsumption Architecture (Brooks):p ( )Example:

Sense Act

Collect can

Drive forward

Avoid obstacles

Sensors Actuators


Subsumption Architecture (Brooks)p ( )

• Behaviors realized by simple AFSM (augmented finite state machines)

• No other internal modellingLa ers Hierarchical collection of beha iors• Layers: Hierarchical collection of behaviors

• Parallel control by all layers• In case of conflicts:In case of conflicts: higher layer overwrights („subsumes“) other layers

First successful robot designs for simple tasks.p

Problems with too many behaviors:


Design and prediction of resulting behavior?

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