=Paper=
{{Paper
|id=Vol-2418/AIC18_paper11
|storemode=property
|title=A Review of Knowledge Bases for Service Robots in Household Environments
|pdfUrl=https://ceur-ws.org/Vol-2418/paper11.pdf
|volume=Vol-2418
|authors=Madhura Thosar,Sebastian Zug,Alpha Mary Skaria,Akshay Jain
|dblpUrl=https://dblp.org/rec/conf/aic/ThosarZSJ18
}}
==A Review of Knowledge Bases for Service Robots in Household Environments==
https://ceur-ws.org/Vol-2418/paper11.pdf
A Review of Knowledge Bases
for Service Robots in Household Environments
Madhura Thosar[0000−0002−3290−7294] , Sebastain Zug[0000−0001−9949−6963] ,
Alpha Mary Skaria[0000−0001−8221−4675] , and Akshay Jain[0000−0003−2087−7427]
Otto-von-Guericke University, Magdeburg, Germany
thosar@iks.cs.ovgu.de zug@iks.cs.ovgu.de
{jain.akshay,alpha.skaria}@st.ovgu.de
Abstract. Consider a typical task-solving scenario where a robot is per-
forming a task involving tool use. When a robot is operating in a dynamic
environment, it can not be assumed that a tool required in the task will
always be available. Our research work concerns the development of a
knowledge-based computation system to determine a substitute for the
unavailable tool. During the development, we identified the requirements
regarding the knowledge base for our scenario and selected 9 existing
knowledge bases for review. In this article, we review existing knowledge
bases developed for the service robotics and investigate their suitabil-
ity for this specific application. The knowledge bases are reviewed with
respect to various criteria corresponding to the categories knowledge ac-
quisition, knowledge representation, and knowledge processing. Our main
contribution is to facilitate the selection of a knowledge base according
to one’s requirements of a target application for service robots involving
household-objects.
Keywords: Service Robotics · Knowledge Base · Knowledge Represen-
tation · Reasoning
1 Motivation
It is not uncommon to find a tool needed for a certain task unavailable. However,
humans tend to circumvent such hurdle by improvising the usability of a suitable
existing object in the environment. For a robot who is expected to work alongside
humans in the real word is bound to face such obstacles and an effective way to
carry on with the task for it in such situations would be to find a substitute. A
selection of an appropriate substitute requires a knowledge driven non-invasive
deliberation to determine its suitability. Baber in [1] suggested that humans are
aided by ontological knowledge about objects during the deliberation process.
Our research work aims at developing a computation system to determine a
substitute which is aided by ontological knowledge about objects.
For instance, consider a scenario in which a robot has to choose between a
plate and a mouse pad as an alternative for a tray. A tray, in general, can be
defined as a rigid, rectangular, flat, wooden, brown colored object while a plate
can be defined as a rigid, circular, semi-flat, white colored object and a mouse
pad as soft, rectangular, flat, leather-based object. Bear in mind, however, that
some properties are more relevant than the others with respect to the primary
purpose of the tool. For a tray whose primary purpose is to carry, rigid and flat
are more relevant to carry than a material or a color of a tray. Consequently, to
find the most appropriate substitute, the relevant properties of the unavailable
tool needs to correspond to as large a degree as possible to the properties of the
possible choices for a substitute.
The proposed approach performs a knowledge-driven reasoning to identify
the relevant properties of the unavailable tool and determines the most similar
substitute on the basis of those properties. Since the computation requires an
access to the ontological knowledge about properties of a missing tool and of
existing objects in the environment, we set out to explore the existing knowl-
edge bases. The primary objective of this exploration was aimed at determining
whether the knowledge about objects from the existing knowledge bases can be
exploited in our approach.
The demand for such ontological knowledge about objects has been increasing
(see Table 2). Especially, for the developers of the reasoning systems such as
tool selection, task planning or an action selection aimed at a service robot
who is expected to perform household tasks, an unhindered access to a stack of
knowledge about objects or the environment is a primary concern. Since there
are many knowledge bases developed for service robots, it can be cumbersome to
scrutinize each one of them to examine the usefulness to the intended system. The
objective of this review article is to provide an overview of the existing knowledge
bases which can facilitate the selection of a knowledge base according to one’s
requirements of a target computation system involving household-objects for
service robots.
2 Knowledge Base Selection
There has been an increasing interest in the knowledge-based systems aimed
at various applications in robotics such as human-robot interaction [12], action
recognition [8], task planning [25], robot navigation [23]. While there are myriad
amount of knowledge bases designed for either specific application or for wider
range of applications, it is a challenging task to identify the most suitable one for
our specific demands. After determining that there is no comparison of knowledge
bases containing the relevant information for the robotic applications exists, we
executed a systematic investigation of the state of the art into three phases to
identify the relevant knowledge bases:
b) Literature Search: In order to find the relevant papers for this review ar-
ticle, we automatically aggregated publications from publication databases by
referencing the following combinations of keywords : knowledge engine robot,
knowledge database robot, knowledge household objects, knowledge data
household and knowledge base robot. The crawler provided 313 papers after
removing the duplicates.
Table 1. List of selected knowledge bases and their names
Knowledge Base Name
Knowledge processing system for Robots KNOWROB [19]
Knowledge Base using Markov Logic Network MLN-KB [26]
Non-Monotonic Knowledge-Base NMKB [15]
Open Mind Indoor Common Sense OMICS [7]
Ontology-based Multi-layered Robot Knowledge Framework OMRKF [18]
OpenRobots Ontology ORO [11]
Ontology-based Unified Robot Knowledge OUR-K [13]
Physically Embedded Intelligent Systems PEIS [3]
Knowledge Engine for Robots RoboBrain [17]
c) Literature Filtering: In this phase, the paper selection was manually evaluated
and assessed. The papers without any relation to the aforementioned required
knowledge bases were rejected while the remaining papers were ordered according
to the knowledge base. We removed the papers which:
– focused on the development of knowledge bases for non-robotic applications.
– were written from the application perspective, without a discussion of the
underlying knowledge base.
– do not cover knowledge about household objects.
– focused primarily on knowledge acquisition without a framework in place to
store the acquired knowledge or update the existing knowledge.
As a result, we selected 39 papers covering 9 knowledge bases for evaluation
1
. The involved knowledge bases are summarized in Table 1 along with their
acronyms by which they are identified. The plot in Figure 1 illustrates the life
span of each knowledge base.
d) Final Literature Selection: In the last step we revised the final list and ex-
tracted the most important papers according to:
1. Content - we looked for papers providing detailed descriptions of configu-
rations, content, performance, interfaces, etc. of the knowledge base. This
information is necessary to assess the knowledge bases with respect to dif-
ferent criteria.
2. Impact - we examined the impact of each paper on the basis of the number
of citations the selected papers have received and how those numbers have
evolved over the years as illustrated in ’Impact of the paper’ column of table
2. In terms of the number of citations, KNOWROB is so far the most influ-
ential knowledge base since its inception while individual papers referencing
OMICS and OMRKF are continuously cited.
1
This complete list is available at https://essfiles.ivs.cs.ovgu.de/index.php/
s/PgdgM19V6GSQ5IW
Fig. 1. The plot illustrating the knowledge bases that are still actively researched
according to the published work indicating the life span of each knowledge base.
For KNOWROB, however, we have isolated 4 from over 40 papers. For the
comprehensive list of the paper, please visit the web page of KnowRob (see
Appendix A) As a result, the original list was filtered and eventually 20 research
papers were selected covering the 9 knowledge bases (see Table 2).
3 Knowledge Base Review
For reviewing, we focus on three primary components to characterize a knowl-
edge base: how is the knowledge acquired, how is it represented and how is it
processed. This section provides an aerial view of the knowledge bases along
the same characteristics. For each characteristic: we have selected the following
criteria (see Table 3)
The real-world is differently perceived by a robot than its human counterpart
due to the limited perception capabilities of the robot. It is, therefore, necessary
to distinguish between what knowledge was acquired from the sensory data or
from the robotic perspective and what knowledge was acquired from non-sensory
sources such as web pages, manually encoded, or from a human perspective. For
the proposed approach, the acquisition of knowledge from a robotic perspec-
tive is prudent. Note that a substitute selection is a decision relative to an
agent’s (human or robot) understanding of an object. We believe that a substi-
tute should be chosen based on a robot’s understanding of an object instead of
human’s understanding since it is a robot that is supposed to manipulate the
substitute. Thus, when reviewing the ’knowledge acquisition’ of the knowledge
bases, we focused on the source of the knowledge and what kind of knowledge
was acquired from this source. These two aspects inform what knowledge is from
human perspective and what knowledge is from robot perspective. For instance,
Table 2. The selected knowledge bases, along with the pointers to the papers about
and an overview about their impact (overall count of references on this paper(s), year
of publication, distribution of the references between 2004 and 2018 )
Knowledge Base Pointers Impact of selected papers
KNOWROB [19], [22], [20], [21] 761 -
MLN-KB [26] 95
NMKB [15] 0
OMICS [7], [5], [6] 98
OMRKF [24], [9], [18] 91
ORO [11], [10], [16] 105
OUR-K [13], [4] 88
PEIS [3], [2] 80
RoboBrain [17] 57
Table 3. The list of criteria corresponding to the characteristics Knowledge Acquisi-
tion, Knowledge Representation and Knowledge Processing used to review the knowl-
edge bases
Characteristic Criteria
Knowledge Source
Knowledge Acquisition
Knowledge Type
Representation Formalism
Knowledge Representation Modeling of Uncertainty
Symbol Grounding
Knowledge Processing Inference or Query Mechanism
some knowledge bases have extracted common sense knowledge about objects
from WordNet or OpenCyc and geometric data about an object, spatial rela-
tion between objects or metric map of an environment are acquired using vision
sensors such as a camera or laser scanner.
The knowledge acquired from various resources needs to be accumulated and
encoded in a formal language such that it provides meaningful description of the
world and can be processed smoothly. The ’representation formalism’ criteria
examines the different formalisms used to represent the knowledge in the knowl-
edge bases (see Table 5). Since the proposed approach deliberates on a possible
substitute non-invasively, we are interested in a logic-based representation of the
world that will allow a reasoning-based computation.
On one hand our proposed approach requires the understanding of the envi-
ronment and the objects in it from the robotic perspective; on the other hand,
the knowledge about the objects need to be represented in a logic-based for-
malism. These two requirements can co-exist when knowledge is grounded in
the sensory data. The grounding of knowledge is popularly known as symbol
grounding or symbol anchoring. For a developer who wishes to use knowledge
representation and reasoning techniques for a robotic application, it is recom-
mended that knowledge is grounded into the robot’s reality of the world. There-
Table 4. Comparison of the selected knowledge bases with respect to knowledge ac-
quisition: what is the source of knowledge and what kind of knowledge was acquired
using the source.
Contents
Map of the Environment
How to Perform Task
Topological Relations
Properties of Objects
Temporal Relations
Spatial Relations
Uses of Objects
Appearance
Actions
Object
Other
Knowledge Base Source of knowledge
○ ○ ○ ○
Multi-Modal Sensor Sys- ○ ○ ○
tems
KNOWROB ○ ○ ○ ○ ○ ○ A OpenCyc, WordNet,
OMICS
○ ○ Online Shops
○ Observation of Human Ac-
tivities or Shared by Other
Robots
○ Web Instructions
○ ○ ImageNet
○ ○ ○ Freebase, Amazon, Ebay
○ ○ WordNet
MLN-KB
○ ○ Manually Encoded
B Standford 40 Action Dataset
NMKB ○ ○ ○ ○ ○ ○ ○ ○ An Interaction-Oriented
Cognitive Architecture [14]
OMICS ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ A non-expert users, WordNet
OMRKF ○ ○ ○ ○ ○ ○ Multi-modal Sensors
○ ○ ○ ○ ○ ○ Manually Hand-coded
ORO ○ ○ ○ ○ ○ OpenCyc
○ ○ ○ ○ ○ Multi-modal sensor system
○ ○ ○ ○ ○ Human Interaction
OUR-K ○ ○ ○ ○ ○ ○ Multi-modal sensor system
○ ○ ○ ○ ○ ○ ○ Manually Hand-coded
PEIS ○ ○ ○ ○ ○ Cyc
○ ○ ○ ○ ○ Vision and Localization Sys-
tem
RoboBrain ○ ○ ○ ○ ○ Robot Interaction
○ ○ A WordNet, OpenCyc, Free-
base
○ ○ ImageNet
A = Common Sense Knowledge about the objects and the environment
B = Human-poses and human-object relative position during object manipulation
Table 5. Comparison of the selected knowledge bases with respect to Representation
Formalism
Knowledge Formalism
Base
KNOWROB OWL-RDF
MLN-KB Markov logic network
NMKB Prolog - Horn Clause
OMICS Relational Database
OMRKF OWL-RDF
ORO OWL-RDF
OUR-K OWL-RDF
PEIS Second Order Predicate Logic
RoboBrain Graph Database
fore, the selected knowledge bases are reviewed to examine what knowledge in
the knowledge base is grounded (see Table 6).
Table 6. Comparison of the selected knowledge bases with respect to Symbol Ground-
ing.
Grounded Knowledge
Topological Relations
Properties of Objects
Spatial Relations
Affordances
Location
Actions
Object
Other
Task
Knowledge Base
KNOWROB ○ ○ ○ ○ ○ ○ ○ ○
MLN-KB ○ ○ ○ ○ ○ ○ A
NMKB ○ ○ ○
OMICS B
OMRKF ○ ○ ○ ○ B
ORO ○ ○ ○ ○
OUR-K ○ ○ ○ ○
PEIS ○ ○ ○ ○ ○
RoboBrain ○ ○
A = Weights of the objects
B = Knowledge is not grounded
Understanding the environment or objects in it from a robot perspective has
its own share of difficulties. For instance, the knowledge that is acquired from
the sensors carries a baggage of uncertainty. The uncertainty can be due to the
Table 7. Comparison of the selected knowledge bases with respect to Modeling of
Uncertainty: what knowledge is modeled and what mechanism is used
Knowledge Mechanism Knowledge Content
Base
Probabilistic Model Noisy sensor information
KNOWROB
Statistical Relational Mod- Relations between objects, types of objects
els
MLN-KB Median-based Noise in the web data
NMKB Principle of Specificity Incomplete Knowledge
OMICS - Uncertainty not considered
OMRKF - Uncertainty not separately modeled
ORO Validation by Users Unknown objects and its properties
OUR-K Bayesian Inference Unknown objects, action selection, context
recognition
PEIS Validation by Users Disambiguate multiple groundings of a
symbol
RoboBrain Validation by Users Inconsistencies due to knowledge coming
from different resources, Disambiguate due
to the same word having different meaning
noisy data or partial observability and can manifest into various forms such as
incompleteness, inconsistency, ambiguities that can affect the overall quality of
the knowledge. One way to deal with the uncertainty is, for instance, by repre-
senting the uncertain knowledge probabilistically. In order to do that, one needs
to identify what knowledge is uncertain. The ’modeling of uncertainty’ criteria
focuses on these two issues: what kind of knowledge is modeled for uncertainty
and what mechanism is used for modeling (see Table 7).
Not all knowledge can be perceived using the sensory sources, for instance,
typical topological relations between objects and places such as cups are usually
in the kitchen or the similarity relations between objects which can not perceived
visually in its entirety. The possible source for such type of knowledge would be
by reasoning about the existing knowledge and drawing inferences from it or by
querying the knowledge base. The knowledge processing criteria looks into both
the aspects of processing: what knowledge in the knowledge base is inferred or
can be queried and what inference or query mechanism is used to achieve that
(see Table 8).
So far, we have discussed the characteristics of the knowledge bases with re-
spect to knowledge acquisition, representation and processing. For a knowledge
base to be useful, size of the knowledge base is a critical piece of information.
The size of the knowledge base can be measured in terms of quantities in which
different kind of knowledge is available, for instance, number of objects, prop-
erties, relations etc. In table 9, we have provided the information on the size of
each knowledge base as reported in the respective literature.
Accessibility is the quality of being easily available to use. The knowledge
bases should be developed such that they can be used by the developers around
Table 8. Comparison of the selected knowledge bases with respect to Inference/Query
Mechanism: what kind of knowledge is inferred and what mechanism is used.
Contents
Topological Relations
Properties of Objects
Spatial Relations
Localization
Affordances
Context
Actions
Object
Other
Task
Knowledge Base Mechanism
○ ○ ○ ○ ○ ○ ○ ○ ○ A Prolog Query
KNOWROB
○ ○ ○ ○ ○ ○ ○ A Probabilistic Inference
MLN-KB ○ ○ ImageNet
NMKB B Prolog Query and Logic In-
ference
OMICS ○ ○ ○ ○ ○ ○ ○ A SQL query
OMRKF ○ ○ ○ ○ ○ ○ Logical Inference
ORO ○ ○ ○ ○ Pellet
OUR-K ○ ○ ○ ○ ○ ○ Bayesian Inference
PEIS ○ ○ ○ ○ ○ ○ ○ OWL Query
RoboBrain A RoboBrain Query Library
A = Retrieve knowledge from the knowledge base
B = Conceptual Inferences
Table 9. This table comprises the information about the size of knowledge bases
reviewed in this paper. The size of knowledge bases is mainly quantified based on the
number of objects, number of classes, instances etc.
Knowledge Quantification of size of KB
Base
KnowRob Around 8000 classes that describe events, actions, objects, mathe-
matical concepts and so on
MLN-KB 40 objects comprise 100 images and on average 4.25 affordance for
each objects
NMKB Not available
OMICS As of 2004, 400 users with 26,000 accepted submissions, 400 images
of indoor objects (current number of images unknown) comprising
a total of 100000 entries in the form of objects, actions, senses.
OMRKF Knowledge about approximately 300 objects as per 2005
ORO 56 object classes and 60 predicates that states relation with objects
OUR-K Knowledge about approximately 300 objects as per 2005
PEIS 15 objects that comprise 2 to 5 images for each object
RoboBrain 44347 concepts and 98465 relations
the world in various applications.The knowledge base accessibility criteria exam-
ines the ways in which each knowledge base is made accessible to the develop-
ers.In the accessibility, we have examined, if the knowledge bases are available to
download or install, if there are tutorials or any other documentation available
to get the user started and if there is information on API available. Additionally,
we also check what kind of licensing is made available. The Table 10 summarizes
the accessibility of each knowledge base. Since for OMICS, OMRKF, OUR-K
and PEIS, we were not able to find the required information, we have indicated
NA (Not Applicable) in the table. Additionally, we have provided the available
web pages for the knowledge bases in appendix A.
Table 10. Compendium of Knowledge bases accessibility features.
Knowledge Download? Install? License Documentation API
Base
KnowRob yes yes Apache yes yes
License
MLN-KB yes no Open no yes
source
NMKB yes yes Golem yes yes
Group
License
OMICS NA NA NA NA NA
OMRKF NA NA NA NA NA
ORO yes yes GNU yes yes
General
Public
License
OUR-K NA NA NA NA NA
PEIS NA NA NA NA NA
RoboBrain yes yes Creative Yes yes
Commons
license
4 Conclusion
In this article, we have investigated the existing knowledge base approaches for
service robot applications and evaluated their capabilities related to a specific
research project. For this purpose we searched for knowledge bases that are de-
veloped for household robots and contain knowledge about household objects
and we identified 9 existing knowledge bases. In addition to the life span of each
knowledge base, the information concerning which knowledge bases have made
impact in the community was measured in the form of a number of citations
the literature related to each knowledge base has received. The knowledge bases
were reviewed with respect to the amount of knowledge it holds. Each knowl-
edge base was further examined with respect to the following criteria: acquisition
of knowledge (what knowledge is acquired and what is the source), representa-
tion formalism, symbol grounding, modeling of uncertainty (what knowledge is
modeled and what mechanism is used) and lastly, inference mechanism (what
knowledge was inferred or queried and what mechanism was used?). We con-
cluded our review by evaluating the accessibility of each knowledge base to the
users.
For our approach, we are interested in the knowledge base that has ontological
knowledge about household objects, especially knowledge about the properties
and uses of the objects. It is also prudent that the required knowledge is grounded
and the uncertainty caused by the partial observability of the environment due
to the noisy sensor is modeled using probability or fuzzy logic. Based on our
investigation, we have concluded that the knowledge bases KnowRob and MLN-
KB seems suitable to our purpose. In the future work, we will conduct the
experiments where the knowledge extracted from them will be used to evaluate
the performance of our approach.
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A Appendix
1. http://www.aass.oru.se/Research/Robots/projects.html (PEIS)
2. https://www.openrobots.org/wiki/oro-server (ORO)
3. https://web.stanford.edu/~yukez/eccv2014.html (MLNKB)
4. http://robobrain.me/about.html (Robobrain)
5. https://github.com/RoboBrainCode (Robobrain)
6. https://tinyurl.com/y9uboh62 (NMKB)