yoda publication
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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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46 AIMAGAZINE