hi i am andrea dalia blaga and i will
present a study on virtual object
categorization methods for a richer
understanding of object grasping in
virtual reality
this is joint work with mighty fruits
pascual chris creed and ian williams and
was conducted at dmt lab at birmingham
city university
i am going to present object
categorization aims the study
methodology for analysis and
contributions with future work
object categorization is defined as a
process of assigning a specific object
to a certain category and is a milestone
in the human evolutionary history by
which people can explore the world
faster and better
categorization methods date back to
ancient greece when plato introduced a
grouping mechanism based on similar
properties
for example objects such as basketball
or knitting ball would be categorized
together as spherical objects
then geon theory introduces the concept
of breaking down these objects into
geons to then categorize them based on
their components
for example the mug objects would be
composed of two geons geon5 and geon3
later zing introduced the methodology
for categorizing objects that looks at
object proportional relationships based
on the three major axes of an object for
example object one two and three would
belong to the same category as they are
present roughly the same dimensions
along any axis
so object categorization methods have
been highly used for computer vision
robotic manipulation and artificial
intelligence with zinc's methodology
being useful for analyzing grasping
interaction in real and virtual
environments for understanding how
object shape influences patterns in
grasping interaction
zinc defines the object dimension a the
longest c the shortest and b the
remaining dimension as well as a
constant r which is equal to three over
two
then he defines four categories along
with mathematical relationships equine
prolate oblate and bladed
for example looking at object two and
its dimensions we can see that dimension
a of the object is greater than
dimension b and dimension c is smaller
than dimension b and smaller than
dimension c multiplied by constant r
resulting in the object being
categorized as prolate
this methodology has been highly used
for understanding how object attributes
influence grasping interaction in real
environments as well as virtual object
interaction
which is a current challenge in vr due
to the complexity of the human hand and
the many variations of interactions
possible on a range of virtual objects
and components
to determine natural grasping patterns
in vr there is a need for investigating
object attributes that might be relevant
for virtual object grasping and see how
these attributes could support objects
to be categorized into groups to present
a more common or generalizable grassing
interaction
to address this we use zinc's
methodology as a benchmark for
comparison and present two novel virtual
object characterization methods that
focus on object attributes that might
influence grasping in vr
the categorization methods are assessed
during a categorization experiment then
the results from the categorization
process are correlated to a grasp data
set from an existing grasping user study
in a vr environment to understand how
categorization methods influence
grasping patterns we restructure that
data steps to present categorized grasps
first i will present the two novel
categorization methodologies proposed in
this paper
based on previous work on grasping real
objects showing that contact bodies
influence grasping equilibrium or
stability we propose the novel virtual
object equilibrium methodology that
focuses on object stability
for this we propose two categories of
objects stable objects which are objects
that are addressed when placed on a
horizontal surface and not easily moved
if incorrectly touched
and unstable objects which are objects
that are easily moved if incorrectly
touch and do not automatically return to
initial position of rest after a small
perturbation
for example looking at object 1 we can
see that the object is at rest on a
horizontal surface and therefore would
be categorized as a stable object
then if we look at object 2 we can see
that this is not addressed on a
horizontal surface and therefore would
be categorized as unstable
inspired by geon theory the concept that
objects might be grasped differently
based on different parts of the object
and considering that things methodology
does not consider objects that have
protruding areas or different structures
we propose zinc plus methodology
covering one part and multiple part
objects to assess grass differences for
objects that provide different graspable
locations
we therefore propose the following
categories one part which presents
objects that are composed of one
geometric graspable component only and
multiple part which presents objects
that are composed of more than one
geometric shape as an example looking at
object one we can see that it is
composed of only one component being
categorized as one part while object two
is composed of four components being
categorized as multiple part
the presented categorization methods
were assessed and evaluated during a
categorization experiment
for this we used 16 3d virtual objects
highly used in grasping studies in both
real and virtual environments recruited
15 raters from a population of
university students and staff members
created two online forms for
categorizing these objects based on the
methodologies proposed and calculated
integrator agreement for each virtual
object following the categorization
process the answers from the raters were
analyzed and objects were grouped in
categories for each categorization
method proposed for example mastered
object was categorized as bladed for
zinc's methodology stable for virtual
object equilibrium and multiplied for
zinc's plus methodology
following the same approach the full set
of 16 objects were categorized in stable
and unstable categories for version of
the equilibrium and one part and
multiple parts for each zinc's category
equine prolate oblate and bladed as part
of the zinc plus categorization
methodology
now to understand how these
categorization methods influence
grasping we use the categorization
results from the previous table and
apply it using a common data set of
brass to restructure that data set into
categorized brush the grasp data set
contains
1872 grasp instances collected during a
grasping docking task conducted in vr
the study used zing's methodology for
categorizing the virtual objects which
is presented in this paper as a
benchmark in comparison
the dataset presents grasping patterns
for each of these objects individually
along with patterns for each of the
things categories the grasps in the data
set are labeled according to the human
grass taxonomy and divided in three main
categories precision which allows an
increased level of manipulation power
which are linked to stability and
security and intermediate which shows
elements of both power and precision
based on this correlation we present the
findings for grouping the grass patterns
based on the novel categorization
methods and provide a comparison to the
classical zinc methodology when looking
at virtual object equilibrium
significant differences were not found
between stable and unstable
however we found that stable objects are
predominantly crust using large diameter
while unstable objects are predominantly
grasped using power sphere
this might have been influenced by the
shape of unstable objects being
spherical or cylindrical and therefore
influencing users to curve their hand in
a position suitable for holding a round
or sphere shaped object moreover this is
consistent with real grasping literature
where it has been found that power
sphere is predominantly used for
grasping spherical objects while large
diameter is predominantly used for
cuboid objects
when comparing to zinc's categories we
can observe that the most used grasp is
from the power category for both
categorization methods which is
consistent with previous work showing
that virtual objects are predominantly
grasped with power grasps
when looking at zinc plus methodology
significant differences were found for
every subcategory between one part and
multiple part
as a general trend grasp differences
were mostly related to the grasp
aperture which is the distance between
thumb and index fingertips
for example for prolate category we can
observe that one part objects were
predominantly grasped using a medium
wrap
while multiplied objects being
predominantly grasped using a small
diameter which has a smaller graph
aperture
considering that in real environments it
has been shown that humans grasp objects
by their smallest dimension
and the theory that humans visual system
naturally decompose structured objects
in component parts
our findings suggest that the change in
grasp aperture is due to participants
visually decomposing the virtual objects
and grasping the smallest component when
more components of an object are
available to conclude in this paper we
show that object stability might
influence grasping patterns in vr due to
the cylindrical shape of unstable
objects moreover multipart objects
showed the proposed a bigger variation
of grasps due to the multiple components
available for grasping with main
differences being related to grass
aperture
overall our results show that object
attributes can influence grasping
patterns in vr and provide insights into
how categorization methods are an
important tool in supporting the
development of an automatic object
classification and could support
automated adaptation and improvement of
vr grasping methods
however future work should include a
larger data set of objects for an
in-depth analysis and consider
investigating the transferability of
this work into mr to understand the
influencing factor between virtual and
real object interaction
thank you