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The YODA RobotProject at theUniversityof

SouthernCalifornia/InformationSciences Insti-

tuteconsistsofa groupofyoungresearcherswho

share a passionfor autonomoussystemsthatcanbootstrap itsknowledge from realenvironments

byexploration, experimentation, learning, and

discovery. Ourgoal is tocreate a mobile agent

thatcan autonomously learn from itsenviron-

mentbasedonitsownactions, percepts, andmis-

sions. OurparticipationintheFifthAnnualAAAI

MobileRobotCompetition andExhibition, held

aspartoftheThirteenthNationalConferenceon

ArtificialIntelligence, served asthefirstmilestone

in advancingustowardthisgoal. YODAssoftware

architecture is a hierarchyofabstraction layers,

rangingfrom a setofbehaviors atthebottomlay-

erto a dynamic, mission-orientedplanner atthe

top. Theplanneruses a mapoftheenvironment

tode

ter

mine a sequenceofgoalstobe accom-

plishedby the robot anddelegates thedetailed

executions to the setofbehaviors at the lower

layer. This abstraction architecturehasprovenro-

bust indynamic andnoisyenvironments, as

shownbyYODAsperformance at the robotcom-

petition.

The suspense ishigh. We stare intensely

at the robotwithone eye, keeping the

otheroneoutfor anysurprises. AsYODA

approachesthedirectorsoffice, itseemstobe

moving slower thaneverbefore. It looks for

thedoor and slowly startsmoving into the

room. Ourminds seem tobe sharing the

samethoughtYODA, dontfailusnow. YO-

DA announces the room for themeeting and

thenthetime:Themeetingwillstartinone

minute. Perfect!Wescream, anditis allover.

YODAsfinalrunintheFifthAnnualAAAIMo-

bileRobotCompetition andExhibition(held

aspartoftheThirteenthNationalConference

onArtificial Intelligence [AAAI-96]) wasper-

fectanexcitingclimax tooursix monthsof

hardwork.

TheYODA teamwas formedwhen a fewof

usfelttheurgetodosomethingwiththebigDenning robot at the Information Sciences

Institute (ISI). Thefinalpushoccurred when

Rodney Brookscame to the Universityof

SouthernCalifornia (USC) and showed the

videoclipsofhisrobots attheMassachusetts

InstituteofTechnology (MIT). These clips

demonstratedsomeinterestingideas aboutAI

andlookedlike a lotoffun. Ourgoalbecame

totransformourthen-lifelessrobotintoYODA

(figure 1), an autonomous agent that would

learn to explore and interact in a realenvi-

ronment.

Wedecided that the OfficeNavigation

eventintherobotcompetitionwastobeour

firstmilestoneinworkingtowardthisgoal. It

wouldprovideus a contextinwhichtodirect

ourefforts. Wedeveloped a general architec-

ture thatwould allowYODA toperform the

competitiontaskand accommodatethelearn-

ing anddiscovery tasks that we would later

add. The following sectionsdescribe this ar-

chitecture inmoredetail andprovide an ac-

countofYODAsperformance at thecompeti-

tion andthechallengesthatwefacedthere.

GeneralArchitecture

ThecurrentYODA systemcomprises a Denning

MRV-3 mobile robot and anon-boardpor-

tablepersonalcomputer. Therobotis a three-

wheelcylindricalsystemwithseparatemotors

formotion and steering. It isequippedwith

24 long-range sonar sensors, 3 cameras for

stereovision, a speaker for soundemission,

and a voice-recognitionsystem. Thecommu-

nicationbetween the robot and the control

Articles

SPRING 1997 37

YODATheYoungObservantDiscoveryAgent

Wei-Min Shen, JafarAdibi, Bonghan Cho,

GalKaminka, JihieKim, BehnamSalemi, andSheilaTejada

Copyright 1997,AmericanAssociationforArtificialIntelligence. Allrightsreserved. 0738-4602-1997 / $2.00

AI Magazine Volume 18 Number 1 (1997) ( AAAI)


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theshortestpathbetween anypairof rooms

on themap. Because the empty conference

rooms arenotknown at the start, the robot

musthavethecapabilityoffindingtheshort-

estpath toeachconference roomuntil an

emptyroom isfound, thenplantheshortest

routebetween theprofessor anddirector

rooms. At themiddle layerof the architec-

ture, eachshortestpathfoundbytheplanner

is expressed as a sequenceofbehavioral ac-

tionswith appropriately assignedparameters.

YODAs fourgenericbehaviors are (1)passing

through a doorway, (2) traveling to a land-

mark, (3) audio communicating, and (4)de-

tecting anemptyroom. Eachofthesebehav-

iors is implemented at thebottom layerof

the architecture intermsofthebasic actions

(forward, backward, and turn) andpercep-

tions (sonar vectors, x-y locations, and an-

gles).

Notice that themain idea behind this ar-

chitecture is abstraction, with each layerbe-

ing an abstractionofthelayerthatisimmedi-atelybelow. Thetoplayer, asshowninfigure

2, only reasons about the relationshipsbe-

tweenrooms;so, whenYODA startsout atthe

directorsroom, thedynamicplannerdecides

which conference room to visitfirst. The

landmarkplanner expands thehigh-level

planbydetermining the routebetween

rooms in termsof landmarks, such asdoor-

ways, hallways, and corners. Once the route

hasbeenplanned, then thebehaviorcon-

trolleriscalledtomovetherobotsafelyfrom

landmark to landmark. Thisconfiguration

was a largecontribution tothebuildingofa

robustperformance system, asdemonstratedbyYODAssuccessinthecompetition.

DynamicPlanner

On the top layerof the architecture, thedy-

namicplannerdetermines allthemission-ori-

ented long-termbehaviorsof the robot. For

the OfficeNavigationevent, there are two

mission-orientedbehaviorsorgoals: (1)find

anemptyroom and (2)notify theprofessors

ofthemeetingtime andplace. To accomplish

thesegoals, theplannermustfindtheshort-

estpathbetween a setofrooms aswell asde-

terminethenecessary actionstointeractwith

the environment. Theplannerneeds tobe

dynamicbecause itmustdecide thecurrent

planbasedoninformationthatitis acquiring

from theenvironment. For example, when

trying tofind the empty conference room,

YODA needstomovefromitscurrentroomto

thenearestconferenceroom andthendetect

if the room is empty. If it isoccupied, then

the robotmoves to thenearestunchecked

computer is accomplishedthrough anRS232

serialportusing a remoteprogramminginter-

face (Denning 1989). The robot iscontrolled

by a setof commands, and the sensor read-

ings include sonar ranges, motor status, and

positionvectors (visionwasnotused in this

competition). Aswith anyrealsensingdevice,

thesensorreadingsfromtherobot arenot al-

ways reliable , whichposeschallenges for

building a robustsystem.

YODAs software is implemented inMCL2.0

on a MACINTOSH POWERBOOK computer. The

control architecture (figure 2) consistsof

threelayers andisdesignedtointegratedelib-

erateplanningwith reactivebehaviors. The

top layer is a dynamicplanner thatcanfind

Articles

38 AIMAGAZINE

Figure1. YODA WanderingtheHallsofthe

Information SciencesInstitute.


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conference room. However, if the room is

empty, thenthecurrentplanistosatisfythe

nextgoaloffindingtheshortestroutetono-

tifytheprofessors.

Thecurrentplan isdeterminedusing theinformation acquired from theenvironment

inconjunctionwith a setof tables thatpro-

vide the shortest-path information corre-

sponding to the current situation. These ta-

bles arebuilt fromparsing the inputmap.

The inputmap (figure 3)consistsof a listof

records, onerecordforeachlocationornode.

A record contains thenode type (corridor,

room, foyer), adjacentnodes, and thedis-

tances to adjacentnodes. Theplannerbuilds

threetablesbyparsingthismap. Itfirstcom-

putes the shortestpaths among the confer-

ence andprofessor roomsbasedon thecon-

nections anddistancesof thenodes. These

paths are stored in a table called thepath

table. Eachpathconsistsofa listofnodes and

thelengthofthepath. Oncethepathtableis

created, the system thenbuilds thenotify

tablebycomputingtheshortestroutetovisit

theprofessors rooms. Thenotify tablecon-

sistsofa listofallthenodesintheroute and

theroutelength. Itcanbeusedtonotifythe

professorsgiventheemptyconferenceroom.

Finally, the scenario table isbuiltbasedon

thesetwotables.

A scenario is a permutationof the setof

conferencerooms. Eachscenariodenotestheorderinwhichtheconferencerooms arevis-

ited. For example, given three conference

rooms, C1, C2, andC3, oneofthepermuta-

tionsis(C1, C2, C3), meaningthatC1 isvis-

itedfirst, thenC2, thenC3. Thescenariotable

records the total route lengths for thediffer-

entpossibilitiesof emptyconference rooms

foreachscenario. Forthisexamplescenario,

theplannercomputesthetotalroutelengths

for threecases: (1) the total route length for

visiting C1 first and then theprofessors

rooms, assuming C1 isempty; (2) the total

route length forvisitingC1 first, thenC2,

andthentheprofessorsrooms, assumingC1

isoccupied, andC2 isempty; and, finally, (3)

the total route length forvisiting C1 first,

thenC2, then C3, and then theprofessors

rooms, assuming C1 and C2 areoccupied,

andC3 is empty. These three route lengths

arestoredinthetablewiththescenario. The

numberofcasesdependson thenumberof

conferencerooms.

Articles

SPRING 1997 39

Dynam ic room p lanner D C P D

Landmark / node p lanner

Behavior Controller

door Hallway Corner door

findDoor passDoor 2walls 1wall foyer sound

Figure2. TheThreeAbstraction LayersofYODAsControlArchitecture.


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ence room is foundempty, theplan execu-

tion isbasedon a sequenceofprofessors

roomsthatwere alreadyplanned astheshort-

estpath. The room-to-room traveling, in

turn, isexecuted astravelthrough a sequence

ofnodesintheroom-to-roompath.

There are various typesofnavigationbe-

tween twonodes. The landmarkplanner ex-

pands thehigh-levelplan into a setof low-

levelnavigationbehaviorsdependingon thetypesofthetwonodes. Forexample, passing

throughthedoorwayisthenavigationtype

necessarytoconnectfrom a hallwaytypeto a

room type andrecognizing a landmark to

connect from a hallway type to a hallway

type (or foyer). Althoughmost low-levelbe-

haviors arespecified atthisleveloftheexecu-

tionhierarchy, somebehaviors, such asde-

tecting anempty room, have alreadybeen

specifiedinthehigh-levelplan.

Thishierarchicalplanexecutionenables

theplan tobe safely recovered in caseof a

crash. Theplan canbe executed from the

pointof the crash insteadof thebeginning.

Thehierarchical executionkeeps the current

statushierarchically(forexample, thecurrent

room, thecurrentnode)sothatthepointof

theexecution at the timeof the crashcan

easily be located in thewhole sequenceof

theoverallplan.

Our time estimation isbasedon the time

data recordedduring theplanexecution. YO-

Giventhescenario table, there are at least

threewaystoselectoneofthescenarios:We

canselectthescenariowiththeminimumto-

tal route length when thefirstconference

roomisempty. Inthegivenexample, thesys-

tem will select thefirst scenario shown in

figure 4. The second strategy selects the sce-

nario thathas theminimum total route

lengthforthecasewhereonlythelastconfer-

ence room in the sequence is empty. In thegiven example, this strategy will select the

third scenario. The third strategy is to select

the scenariowith an ave rageminimum ,

whichisthesecondscenariointheexample.

Weusedthefirststrategytoselect a scenario

for thecompetition. Bybuilding the tables

frombottom to top (frompath table to sce-

nario table), wenotonly avoid redundant

computations in the future (during execu-

tion)but also save recomputations while we

buildthetables.

LandmarkPlanner

At themiddle layerof the architecture, the

landmarkplanner reasons about eachplan

foundby thehigh-levelplanner in termsof

behavioral actions. This layer also controls

theexecutionofthehigh-levelplanand the

time-estimation task involved innotifying

theprofessors. Once a scenarioisselected, the

scenario isexecuted as travel through a se-

quenceofconferencerooms. When a confer-

Articles

40 AIMAGAZINE

R5Foyer

R4

R3R9

R2R7R6 R1

C1 C2 C3 C4 C5 C6

C12 C13 C14 F1 C17

C1 1C1 0

C8

C9

C7

R8

C15 C16

D irector

Co nf. 1 Conf. 2

Pro f. 1

Pro f. 2

((setq *con ference-rooms*'(R4 R2))

(setq *pro fessor-rooms*'(R1 R8))

(setq *director-room*'R5)

(setq *start ing-room*'R5)

)

(setq *map*

((C1 C (C2 E 100) (C7 S 100))

(C2 C (C1W 100) (R6 S 0) (C3 E 100))

(C3 C (C4 E 100) (R7 S 0) (C2 W 100))

(C4 C (C8 S 100) (C3 W 100) (C5 E 200)

(C5 C (C4 W 200) (C6 E 160) (R2 S 0))

(C6 C (C5 W 160) (C11 S 230))

(C7 C (C1 N 100) (C9 S 160) (R6 E 0))

...........

)

Figure3. An ExampleoftheInputMap.


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BonghanCho is a Ph.D. candi-

date in theDepartmentofCom-

puterScience attheUniversityof

SouthernCalifornia (USC). Here-

ceivedhisM.S. fromUSCin 1989

andhisB.S. fromtheDepartment

ofComputerScience and Statis-

tics, SeoulNational University,

Korea, in 1987. He is currently

working for the SOAR Project. His areasof interest

includeconstraint-satisfactionproblems, the scal-

ingupofknowledgebase systems, and computer

networks.

GalKaminka is a graduate re-

search assistant at the Informa-

tionSciencesInstitute, University

ofSouthern Cali fornia (USC),

and a Ph.D. student intheCom-

puter Sci ence Department at

USC. Hecompletedhisunder-

graduateeducation incomputer

science attheOpenUniversityof

Israel. Hisinterests areinthe areasofagentmodel-

in

g,agen

ts tha

t reason

about t

hemselves, f

ailure

and anomalydetection, andfuzzysettheory.

JihieKim is a computerscientist

in the InformationSciencesInsti-

tute attheUniversityofSouthern

California (USC). Shereceivedher

Ph.D. incomputer science from

USCin 1996 andherM.S. andB.S.

incomputer science fromSeoul

NationalUniversity in 1990 and

1988, respectively. Her research

interests includemachine learning, intelligent

agents, rule-basedsystems, knowledge-basedsystems

forinformationretrieval, andelectroniccommerce.

BehnamSalemi is a graduate

student in the Departmentof

ComputerScience at theUniver-

sityofSouthernCalifornia and a

graduateresearch assistant atthe

InformationSciences Institute.

He receivedhisB.S. incomputer

science from Shahid-Beheshti

University, Tehran, Iran, in 1991.

Hisresearchinterestsinclude autonomouslearning

and intelligent agents in thedomainsof robotics

andeducation.

SheilaTejada is a Ph.D. student

in the DepartmentofComputer

Sci ence at the Univ ersity of

SouthernCalifornia and a gradu-

ateresearch assistant attheInfor-

mationSci ence s Institute. In

1993, she receivedherB.S. in

computer science from theUni-

versityofCalifornia atLosAnge-

les. Her research interests includemachine learn-

ing, planning, intelligent agents, anddata mining.

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