Table Of ContentLecture Notes in Artificial Intelligence 6798
EditedbyR.Goebel,J.Siekmann,andW.Wahlster
Subseries of Lecture Notes in Computer Science
Ronny Hartanto
A Hybrid
Deliberative Layer
for Robotic Agents
Fusing DL Reasoning with HTN Planning
in Autonomous Robots
1 3
SeriesEditors
RandyGoebel,UniversityofAlberta,Edmonton,Canada
JörgSiekmann,UniversityofSaarland,Saarbrücken,Germany
WolfgangWahlster,DFKIandUniversityofSaarland,Saarbrücken,Germany
Author
RonnyHartanto
DFKIRoboticsInnovationCenter
28359Bremen,Germany
E-mail:[email protected],[email protected]
Thisworkwascarriedoutat:
Bonn-Rhein-SiegUniversityofAppliedSciences(BRSU)
Bonn-AachenInternationalCenterforInformationTechnology(b-it)
St.Augustin,Germany
andacceptedasPhDthesisattheUniversityofOsnabrück,Germany.
ISSN0302-9743 e-ISSN1611-3349
ISBN978-3-642-22579-6 e-ISBN978-3-642-22580-2
DOI10.1007/978-3-642-22580-2
SpringerHeidelbergDordrechtLondonNewYork
LibraryofCongressControlNumber:2011931781
CRSubjectClassification(1998):I.2.9-11,I.2,I.6,H.3,F.1,D.2,C.2,H.4-5
LNCSSublibrary:SL7–ArtificialIntelligence
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Ronny Hartanto
A Hybrid Deliberative Layer for
Robotic Agents
Bandnummer:6798
Fusing DL Reasoning with HTN Planning in
Autonomous Robots
June 16, 2011
Springer
To Meidjie and my parents
Foreword
Cognitiverobotsareallovertheplace.Well,atleastthatiswhatitlookslike
in researchlab agendas on the planet.
Some basic and fundamental problems in controlling mobile robots have
been quite well understood in recent years: Localization and map building
are chief examples. Now, as autonomous mobility as such is much less of a
challenge, researchresumes an activity that has started about half a century
ago in mainstream AI; that got out of the AI mainstream for a while, as
other challenges turned out to offer solutions faster; that got a little mocked
for some time (“good old-fashioned AI”, gofai); yet that has remained a
fascinating scientific problem, and potentially a rich source of application
projects: algorithmic models for goal-directed physical action in the world.
So in a sense, very recent and ambitious projects in cognitive robotics, or
cognitivetechnicalsystems,orrobotcognition,orwhateveryoupreferto call
them,aretakingupthatthread,which,Iwouldsay,goesbacktothepioneer-
ing shakey projectinthe AI ofthe 1960s,andhas remainedunresolvedever
since.However,thereareanumberofinterestingdifferencesbetweenthinking
aboutacognitiverobotinthe1960sanddoingsotoday:Ourmoderncomput-
ers are nearly infinitely more powerful;sensorsare much better; decent robot
hardware has become affordable even for a normal university lab; and, the
propershareofAI:50yearsofAIresearchhasyieldedquiteanumberofwell-
understoodalgorithmsandformalismsforplanning,knowledgerepresentation
and reasoning that were unknown in the early days.
And still, getting a cognitive robot to work is a challenge – which is why
cognitive robots are not yet all over the place, and the cognition in those
that are may fail to impress. Among a zoo of problems from “just” technical
(integration!) to big AI problem (symbol grounding!) is a specimen that has
todowiththeproperAIshareofthestory:Usingaplannerorreasonerwithin
the control software of an autonomous cognitive robot is different from using
them in an off-line or human-in-the-loop system.
Now this is the point where Ronny Hartanto’s thesis comes into play. A
robot working, for example, in a mundane household domain, would need to
VIII Foreword
have a huge base of knowledge and facts to be useful. Generating plans using
state-of-the-art methods is susceptible to the size of the planning domain
representation. On the other hand, robot plans would need to be generated
fast, as they need to be found and changed quickly. So there seems to be a
mismatch.
But luckily a characteristicof typicalmundane robot-planning problems is
thatoftheirpotentiallyhugeknowledgebases,onlyatinyfractionwouldoften
be necessary for generating a solution plan. Then how could a robot know,
given some planning goal, exactly which part of its knowledge is relevant for
finding this plan – and know that without finishing the planning in the first
place?
The present volume gives an answer... well, not to this precise question
(which, if youask me,probablyhas no answer),but to a relatedone:Is there
a part of the knowledge base that is foreseeably irrelevant for the planning
problem at hand?
The keyto answeringthis lies in generatingthe planning probleminstance
automaticallyfromafulldomainrepresentationindescriptionlogic(DL).But
isn’tthat cheatingthen: First,building a DL domainrepresentationthatyou
wouldn’tneedotherwise,andsecond,movingthebigeffortfromplanninginto
DLreasoning?Theanswertobothquestionsisno:First,withinthecomplete
robotcontrolsoftware,a DL representationofthe domainis neededfor other
tasks, too (e.g., for semantic perception), so it has to be present anyway.
And second, the necessary reasoning is normally done very efficiently, using
state-of-the art DL reasoners.Details are in this thesis.
ThereforeIrecommendreadingthisvolumeforallthosewhoareinterested
inmakingcognitiverobotsreal–which,asspectacularsciencesooftenwould,
requires coping with prima facie unspectacular details. And for those who
are looking for more challenges: The list of open issues at the end of the
thesis is long! Understanding goal-directed physical action, or making robots
cognitive, if you prefer, will remain with us for some time. Ronny Hartanto’s
work provides a helpful tool on the way.
May 2011 Joachim Hertzberg
Preface
Actionplanninghasbeenusedinthefieldofroboticsforsolvinglong-running
tasks.Intherobotarchitecturesfield,itisalsoknownasthedeliberativelayer.
However, there is still a gap between the symbolic representation on the one
hand and the low-levelcontroland sensor representationon the other. In ad-
dition,the definitionofa planning problemfor a complex,real-worldrobotis
nottrivial.Theplanningprocesscouldbecomeintractableasitssearchspaces
become large. Since the defined planning problem determines the complexity
andthecomputabilityforsolvingtheproblem,itshouldcontainonlyrelevant
states. In this work, a novel approach which amalgamates description logic
(dl) reasoning with hierarchical task network (htn) planning is introduced.
Theplanningdomaindescriptionaswellasfundamentalhtnplanningcon-
ceptsarerepresentedindlandcanthereforebesubjecttodlreasoning;from
these representations,concise planning problems are generatedfor htn plan-
ning. The method is presented through an example in the robot navigation
domain. In addition, a case study of the RoboCup@Home domain is given.
As proof of concept, a well-known planning problem that often serves as a
benchmark, namely, that of the blocks-world, is modeled and solved using
this approach.
Ananalysisoftheperformanceoftheapproachhasbeenconductedandthe
results show that this approach yields significantly smaller planning problem
descriptionsthanthosegeneratedbycurrentrepresentationsinhtnplanning.
This dissertation is not the result of a few weeks or months of work, but
ratherofyears.Itwouldnothavebeenpossiblewithoutthehelpandsupport
of many people. Therefore, I thank all those who have directly or indirectly
supported me in this endevor. Of these generous people, I take this opportu-
nity to thank a few by name.
First,Ithankmyfirstsupervisor,“Doktorvater,”Prof.JoachimHertzberg,
for his tremendous supportand guidance overthese past years.In particular,
for this opportunity to work on such an engaging research topic. My sincere
thanks to my secondsupervisor,Prof.MichaelBeetz, for his valuable sugges-
tions and guidance. I express my gratitude to Prof. Gerhard Kraetzschmar,
X Preface
Iman Awaad, and Christoph Mies for their time and effort in reading this
work and for their valuable feedback.
Working in robotics researchwould not be possible without robots and re-
search labs. Therefore, I thank Prof. Kurt Ulrich-Witt, Prof. Erwin Praßler,
Prof.PaulG. Plo¨ger,andProf.GerhardKraetzschmarfor providingme with
the opportunityto workinthe b-it-fundedresearchgroupandsupportingme
in my work on this dissertation. I thank my colleagues, past and present:
Walter Nowak, Iman Awaad, Azamat Shakhimardanov, Jan Paulus, and
Anastassia Ku¨stenmacher. During the time spent on this work, I have had
the pleasure of being a part of, not only the b-it researchgroup, but also the
EU-funded project, XPERO. Therefore, I extend my thanks to my XPERO-
colleagues: Timo Henne, Bj¨orn Kahl, and Katharina Hoffmann.
Ifthisisthesecondpageyouhavereadsofar,youwillnotyethavenoticed
onemoreimportantaspectofthis work,namelythe b-it-bots RoboCupteam.
I gratefully acknowledge my team members: Dirk Holz, Jan Paulus, Thomas
Breuer,GeovannyGiorganna,FrederikHegger,ChristianMu¨ller,andJinZha.
My gratitude also goes to one very special member, namely, Johnny Jack-
anapes“therobot,”withoutwhomthe teamwouldnotbe able toparticipate
in any RoboCup events.
IowemydeepestgratitudetomydearMeidjieAngforherloveandcontin-
uous encouragementand to my family in Indonesia for their endless support.
May 2011 Ronny Hartanto
