Towards Affordance-Based Robot Control: International Seminar, Dagstuhl Castle, Germany, June 5-9, 2006. Revised Papers

218 Pages·2008·37.31 MB·English

by  Verena V. Hafner

Lecture Notes in Artificial Intelligence 4760
EditedbyJ.G.CarbonellandJ.Siekmann
Subseries of Lecture Notes in Computer Science
Erich Rome Joachim Hertzberg
Georg Dorffner (Eds.)
Towards
Affordance-Based
Robot Control
International Seminar
Dagstuhl Castle, Germany, June 5-9, 2006
Revised Papers
1 3
SeriesEditors
JaimeG.Carbonell,CarnegieMellonUniversity,Pittsburgh,PA,USA
JörgSiekmann,UniversityofSaarland,Saarbrücken,Germany
VolumeEditors
ErichRome
FraunhoferInstituteforIntelligentAnalysisandInformationSystems(IAIS)
AdaptiveReflectiveTeamsDepartment(ART)
SchlossBirlinghoven,53754SanktAugustin,Germany
E-mail:[email protected]
JoachimHertzberg
UniversityofOsnabrück
InstituteofComputerScience
49069Osnabrück,Germany
E-mail:[email protected]
GeorgDorffner
AustrianResearchInstituteforArtificialIntelligence(OFAI)
NeuralComputationandRobotics
and
InstituteofMedicalCyberneticsandArtificialIntelligence
CenterforBrainResearch,MedicalUniversityVienna
Vienna,Austria
E-mail:[email protected]
LibraryofCongressControlNumber:2008920268
CRSubjectClassification(1998):I.2.9,I.2.10,I.2.8
LNCSSublibrary:SL7–ArtificialIntelligence
ISSN 0302-9743
ISBN-10 3-540-77914-0SpringerBerlinHeidelbergNewYork
ISBN-13 978-3-540-77914-8SpringerBerlinHeidelbergNewYork
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Preface
Today’s mobile robot perception is insufficient for acting goal-directedly in un-
constrained, dynamic everyday environments like a home, a factory, or a city.
Subject to restrictions in bandwidth, computer power, and computation time,
a robot has to react to a wealth of dynamically changing stimuli in such en-
vironments, requiring rapid, selective attention to decisive, action-relevant in-
formation of high current utility. Robust and general engineering methods for
effectivelyandefficientlycouplingperception,action,andreasoningareunavail-
able.Interesting performance,if any,is currentlyonly achievedby sophisticated
robot programming exploiting domain features and specialties, which leaves or-
dinary users no chance of changing how the robot acts.
The latter facts arehigh barriersforintroducing,for example,servicerobots
into human living or work environments. In order to overcome these barriers,
additonal R&D efforts are required. The European Commission is undertak-
ing a determined effort to fund related basic, inter-disciplinary research in a
line of Strategic Objectives, including the Cognitive Systems calls in their 6th
Framework Programme (FP6, [1]), and continuing in the 7th Framework Pro-
gramme.OneofthefundedCognitiveSystemsprojectsisMACS(“multi-sensory
autonomous cognitive systems interacting with dynamic environments for per-
ceiving and using affordances”).
In cognitive science, an affordance in the sense of perceptual psychologist
J.J. Gibson [2] is a resourceor support that the environment offers an agentfor
action,andthattheagentcandirectlyperceiveandemploy.Onlyrarelyhasthis
concept been used in robotics and AI, although it offers an original perspective
on coupling perception, action, and reasoning, differing notably from standard
hybrid robot-controlarchitectures.Taking it literally as a means or a metaphor
for coupling perception and action directly, the potential that affordances offer
for designing new powerful and intuitive robot-controlarchitectures is obvious.
Perceiving affordances in the environment means that perception is filtered
through the individual capabilities for physical action and through the current
goals or intentions, thereby coupling perception and action deep down in the
control architecture and providing an action-oriented interpretation of percepts
inrealtime.Moreover,affordancesprovideonahighgranularitylevelabasisfor
agent interaction and for learning or adapting context-dependent, goal-directed
action.
The main objective of the MACS project is to explore and exploit the con-
cept of affordances for the design and implementation of autonomous mobile
robots acting goal-directedly in a dynamic environment. The aim is to develop
affordance-based control as a method for robotics. The potential of this new
methodology will be shownby going beyond navigation-liketasks towardsgoal-
directed autonomous manipulation in the project demonstrators.
VI Preface
During the MACS proposal phase in late 2003, the idea of organizing an
interdisciplinary Dagstuhl seminar related to the core MACS topics emerged.
The planned purpose of the seminar was threefold, namely, (1) to disseminate
the MACS project ideas and concepts into related scientific communities, (2)
to receive feedback on and discuss these ideas, and (3) to discuss the usage of
affordances in other researchareas.
The organizers saw researchers in four broad areas (philosophy and logic,
artificialintelligenceandcomputerscience,psychology,andeconomicsandgame
theory) addressing highly related (in some cases, the same) problems, in which
workinoneareainalllikelihoodwouldbenefitresearchinanother.Henceforthe
Dagstuhlseminar,theorganizersfeltthatvaluableinteractionsandcontributions
could be anticipated by bringing people together from these areas. The aim of
the seminar was to bring together researchers from robotics, informatics, and
the cognitive sciences to exchange their experiences and opinions, and generate
new ideas regarding the following essential questions:
– Howcouldorshouldarobot-controlarchitecturelooklikethatmakesuseof
affordances in perceiving the environment?
– Howcouldorshouldsuchanarchitecturemakeuse ofaffordancesforaction
and reasoning?
– Is there more to affordances than function-oriented perception, action, and
reasoning?
The answers to these questions are currently wide open. Two points can be
statedwithcertainty,however.First,anaffordance-basedoraffordance-inspired
robot-control architecture cannot simply be an extension (an “added layer,” so
to speak) to existing modern control architectures. The reason is that affor-
dances would spring into existence in low-level perception, would have to pass
filters in the control, such as attentional mechanisms, in order not to flood the
robot’s higher processing levels, and serve in some explicitly represented form
of a structured result of perception as a resource for action selection, deliber-
ation, and learning. So if there is such a thing as an affordance-based control
architecture, affordances will have to play a role in all of its layers.
Second, the answers to the seminar questions do not depend on whether
or not the cognitive sciences agree that Gibson is “right” in the sense that
affordancesexistinbiologicalbrainsormindsorexistintheinteractionbetween
biologicalindividualsandtheirenvironment.Thepointis,ifGibson’sdescription
of phenomena of functional coupling between perception and action is correct,
then it is of high interest for robot control designers, independent of how it is
bestunderstoodaccordingtocognitivesciencestandards.Therefore,theseminar
would profit from either proponents or opponents of the affordance model. The
aim here was discussion and exchange, not unanimity.
The organizersbroughttogether 32 researchersfrom different scientific com-
munities to attend the seminar. Given that the scientific background of the
participantswasnothomogeneous,andthattherewasonlylittle technicalwork
that directly fit the seminar topic (as remarked above, there are only relatively
fewexamplesofusingexplicitlytheconceptofaffordances),theprogram(cf.[3])
Preface VII
wascomposedof sixoverviewtalks centeredaroundthe state ofthe art,serving
to inform the heterogeneous audience, and 13 technical presentations of mainly
young researchers working in related areas.
Presentations, an abstract collection of all contributions, and an executive
seminarsummarycanbefoundattheDagstuhlWebsite[3].Twelveofthesem-
inar contributions havebeen elaboratedasfull articles for this post-proceedings
volume. Additionally, a highly relevant paper from Alex Stoytchev has been
invited to complement the seminar contributions.
The organizers express their gratitude to the Dagstuhl foundation for their
support and for hosting this seminar in their exceptional facilities, and to the
participants for their contributions and for making the seminar successful and
enjoyable.Theworkoforganizingandconductingtheseminarwaspartlyfunded
by the European Commission’s 6th Framework ProgrammeIST Project MACS
under contract/grant number FP6-004381.The Commission’s support is grate-
fully acknowledged.
September 2007 Erich Rome
Joachim Hertzberg
Georg Dorffner
References
1. IST priority European Commission. 2nd call, cognitive systems, work programme
2003-4 reference (2003),
http://cordis.europa.eu/ist/workprogramme/en/2\ 3\ 2\ 4.htm
2. Gibson, J.J.: The theory of affordances. In: Shaw, R.E., Bransford, J. (eds.) Per-
ceiving, Acting,and Knowing, Lawrence Erlbaum Associates, Hillsdale (1977)
3. Dagstuhl Foundation. Towards affordance-based robot control. Dagstuhl Seminar
06231 (2006), http://www.dagstuhl.de/06231/
Table of Contents
Interpersonal Maps: How to Map Affordances for Interaction
Behaviour....................................................... 1
Verena V. Hafner and Fr´ed´eric Kaplan
Does It Help a Robot Navigate to Call Navigability an Affordance? .... 16
Joachim Hertzberg, Kai Lingemann, Christopher Lo¨rken,
Andreas Nu¨chter, and Stefan Stiene
Learning Causality and Intentional Actions.......................... 27
Somboon Hongeng and Jeremy Wyatt
GrAM: Reasoning with Grounded Action Models by Combining
Knowledge Representation and Data Mining......................... 47
Nicolai v. Hoyningen-Huene, Bernhard Kirchlechner, and
Michael Beetz
Affordance-Based Human-Robot Interaction ........................ 63
Reinhard Moratz and Thora Tenbrink
Reinforcement Learning of Predictive Features in Affordance
Perception ...................................................... 77
Lucas Paletta and Gerald Fritz
A Functional Model for Affordance-Based Agents .................... 91
Martin Raubal and Reinhard Moratz
Affordances in an Ecology of Physically Embedded Intelligent
Systems ........................................................ 106
Alessandro Saffiotti and Mathias Broxvall
Use of Affordances in Geospatial Ontologies ......................... 122
Sumit Sen
Learning the Affordances of Tools Using a Behavior-Grounded
Approach ....................................................... 140
Alexander Stoytchev
Function-Based Reasoning for Goal-Oriented Image Segmentation...... 159
Melanie A. Sutton and Louise Stark
The MACS Project: An Approach to Affordance-Inspired Robot
Control......................................................... 173
Erich Rome, Lucas Paletta, Erol S¸ahin, Georg Dorffner,
Joachim Hertzberg, Ralph Breithaupt, Gerald Fritz, Jo¨rg Irran,
Florian Kintzler, Christopher Lo¨rken, Stefan May, and Emre U˘gur
Author Index.................................................. 211
Interpersonal Maps: How to Map Affordances for
Interaction Behaviour
VerenaV.Hafner1andFre´de´ricKaplan2
1Humboldt-Universita¨tzuBerlin,Institutfu¨rInformatik,Berlin,Germany
[email protected]
2EcolePolytechniqueFederaledeLausanne,CRAFT,Lausanne,Switzerland
[email protected]
Abstract. Inastudyofhowtheconceptofaffordancescouldbeappliedtointer-
actionbehaviour,weintroducethenotionof“interpersonalmaps”,ageometrical
representation of the relationships between a set of proprioceptive and hetero-
ceptive information sources, thus creating a common representation space for
comparingone’sownbehaviourandthebehaviourofothers.Suchmapscanbe
used to detect specific types of interactions between agents such as imitation.
Moreover,incasesofstrongcouplingsbetweenagents,suchrepresentationsper-
mittomapdirectlyanagent’sbody structureontothestructureofanobserved
body,thusaddressingthebodycorrespondenceproblem.Thesevariouscasesare
studied with several robotic experiments using four-legged robots either acting
independentlyorbeingengagedindelayedimitation.Throughaprecisestudyof
theeffectsoftheimitationdelayonthestructureoftheinterpersonalmaps,we
show the potential of this“we-centric” space toaccount for both imitativeand
nonimitativeinteractions.
1 Introduction
In 1977,perceptualpsychologistJ. J. Gibson defined an affordanceas “a resource or
supportthattheenvironmentoffersanagentforaction,andthattheagentcandirectly
perceiveand employ”[1]. Even thoughpeople are disagreeingaboutthe possible ap-
plicationsofthis theoryin the cognitivesciences, affordancescan be seenas a useful
theoryofinteractionformanydisciplinesincludingrobotics.Affordanceslinkpercep-
tion and action dependingon the currentgoalsor intentionsof an agent. Gibson also
statedthataffordancesarenotclassificationsofobjects,butratherafunction-centered
view, and thereforeprovidea more intuitive view of oneself in a certain environment
orsituation.Aneverydayexampleforthisviewaresortingsystemsinhumanenviron-
ments.FollowingGibson’stheoryofaffordances,puttingapriorityontheaffordanceof
anobject(e.g.cutting,connecting)ismoreintuitivethansortingobjectsbytheirname
orappearance.Humanscanalso employattentionalprocessestofocusonthepercep-
tionofaparticularaffordance.Thisleadstojointattention[2]inthecaseofinteraction
behaviour.
Mostoftheresearchinaffordanceshasfocusedoninteractionsbetweenanagentand
itsenvironment[3].However,webelievethataffordancesarealsoarelevantconceptin
thecase oftheinteractionbetweentwoagents.Inanotherresearch,we haveexplored
E.Romeetal.(Eds.):Affordance-BasedRobotControl,LNAI4760,pp.1–15,2008.
(cid:2)c Springer-VerlagBerlinHeidelberg2008
2 V.V.HafnerandF.Kaplan
anovelframeworkpermittingunsupervisedactivityclassification[4]basedoncoordi-
nation patterns. Here, we introduce and explore the conceptof “interpersonalmaps”,
theapplicationofthesameideatointeractionbehaviour.Thisconceptisdefinedinthe
frameworkofinformationtheoryandcanbeappliedinthecontextofinteractionsbe-
tweenlivingentitiesaswellasbetweenartifacts.Althoughinformationtheoryhashis-
toricallybeenmainlyconcernedwithinformationtransmissionbetweenasenderanda
receiver[5,6], severallinesof researchhave focusedon addressing issues concerning
relationshipsbetweeninformationsources[7,8,9].Inparticular,ithasbeenshownthat
the space of information can be equipped with a metric [10]. It is therefore possible
to adapt some of the vocabulary and tools of geometry to the domain of information
theory.Interpersonalmaps are geometricalrepresentationsof relationshipsbetween a
setofinformationsources.
Thenotionofinterpersonalmapsisrelatedtoseveralexistingconceptsinpsychol-
ogyandneuroscience.Toaccountforearlyimitation,MeltzoffandMooreargueforthe
existenceofanintermodalmappingestablishingequivalencerelationsbetweendiffer-
entmodalitiessuchasvisionormotoractions[11,12].Suchamodelsuggeststhatboth
perceived(self)andobserved(others)behaviourcouldberepresentedinasharedneu-
ralformat.Similarly,Gallesehasarguedthatsincethebeginningofourlifeweinhabit
asharedmultidimensionalinterpersonalspace.Whenweobserveotherindividuals,“a
meaningfulembodiedinterpersonallinkis established”.Gallese refersto thisformof
intersubjectivityasthesharedmanifoldspace.Furthermore,histheorypredictstheex-
istence of “somatosensorymirror neurons” givingthe capacity to map differentbody
locationsduringtheobservationofthebodiesofothers[13].However,fewmodelstry
togivea preciseaccountonhowsuchinterpersonalorintermodalmappingscouldbe
developed.
The approach presented in this article is directly inspired by several methodscon-
cerning unsupervised map building recently described in the field of artificial intel-
ligence and autonomous robotics. Pierce and Kuipers present a method for building
maps of a sensory apparatus out of raw uninterpreted sensory data [14,15]. This so-
called sensory reconstruction method is based on various distances between sensors
such as a normalised Hamming distance metric and a frequency metric. Sensors are
clustered into subgroupsbased on their relative distance. The dimensionality of each
subgroupcanthenbecomputed,relatedsensorscanbeprojectedtoformasensormap.
Buildingonthissensoryreconstructionmethod,Olsson,NehanivandPolani[16]have
suggested to use the information metric defined by Crutchfield [10] as a more inter-
estingmeasureofthedistancebetweentwoinformationsources.Theyhaveconducted
experimentswithvarioussensorsetsincludingvisualandproprioceptivesensorsonan
AIBOrobot.RelatedapproacheswerealsoinvestigatedbyKuniyoshi’sresearchteam
[17].Mostoftheseapproachesinterpretsuchsensoryreconstructionmethodsasaway
ofbuildingmapsofsensorsinanunsupervisedmanner.Someoftheseworksmakethe
comparisonwithsomatosensorymapsdiscoveredinthebrain.
We extend and, more importantly, reinterpret the sensory reconstruction method.
Thesensoryreconstructionmethodiswell-adaptedtoaddressprocessesunderlyingthe
emergenceofbehaviouralcomplexity,butitmaybemisleadingtointerpretitonlyasa
formationofabodymap.Aparticularsetofdistancescapturesnotonlyaspectsofan
InterpersonalMaps:HowtoMapAffordancesforInteractionBehaviour 3
agent’sembodiment,butcanalso reflectthe agent’scurrentactivitiesandthesituated
natureofitsinteractionwiththeenvironment.Inparticular,aspecificconfigurationmay
appearinthecaseofcouplingswithotheragentsorincasesofremarkablecoordination
patterns, thusallowing the system to be interpretedas an applicationof the theory of
affordances.Wewillnowpresenttheapproachinamoreformalmannerandgiveresults
ofpreliminaryexperimentsshowinghowthisframeworkcanbeusedinthecontextof
robot-robotimitation.
2 Maps Based onInformationDistances
Thissection reviewsandillustrates the basic principlesof map constructionbased on
informationdistances as it is used in several other articles (e.g. [14,16,4]) . This will
providethebasicelementsforintroducinginthenextsectionthenotionofinterpersonal
maps.
2.1 Definition
DistanceBetweenInformationSources. LetusassumethattherobotR isequipped
X
withnsensors(proprioceptiveanddistancesensors).Atanytimetitssensorystatecan
becapturedbythevectorX(t)
X(t)=(X (t),X (t),...,X (t)) (1)
1 2 n
ForanysensorX theentropyH(X )canbecalculatedas
i i
(cid:2)
H(X )=− p(x )log p(x )
i i 2 i
xi
wherep(x )istheprobabilitymassfunctionoverallpossiblediscretisedvaluesx .To
i i
calculateit,thedistributionofthevaluesofX hastobecomputedwithacarefulchoice
i
ofthenumberofbins(see[18]).Agoodsolutiontoavoidthisproblemistointroduce
adaptive binning[19]. In such a case, the size of the bins is variable and chosen in a
waythatmaximisestheentropyforeachsensor.
TheconditionalentropyfortwosensorsX andX canbecalculatedas
i j
(cid:2)(cid:2)
H(X |X )=− p(x ,x )log p(x |x )
j i i j 2 j i
xi xj
wherep(x |x )=p(x ,x )/p(x ).
j i j i i
Crutchfielddefinestheinformationdistancebetweentwoinformationsourcesas:
d(X ,X )=H(X |X )+H(X |X ) (2)
j i i j j i
andthenormalisedinformationdistanceas
H(X |X )+H(X |X )
d (X ,X )= i j j i (3)
N j i H(X ,X )
i j