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the silhouettes of objects. Other features could be
used as well, but the main objective in this work is to
show the different cases and possible confusions that
can arise in the recognition of objects and merging of
concepts, and how they can be addressed.
Numerous difficulties arise in robot teams when
learning as well as sharing concepts that represent
concrete objects. Some of these issues are discussed
by Ye and Tostsos (1996) and include, how do robots
represent their local views of the world, how is the
local knowledge updated as a consequence of the
robot’s own action, how do robots represent the local
views of other robots, and how do they organize the
knowledge about themselves and about other robots
such that new facts can be easily integrated into the
representation. This article addresses the individual
and collective representation of objects from visual
information using a team of autonomous robots.
The rest of the paper is organized as follows. Sec-
tion 2 reviews related work. Sections 3 y 4 describe,
respectively, the stages of individual learning and col-
lective learning of concepts. Section 5 describes our
experimental results, and Section 6 provides conclu-
sions and future research work.
2 RELATED WORK
Interesting experiments where physical mobile robots
learn to recognize objects from visual information
have been reported. First we review significant work
developed for individual learning, and then we review
learning approaches developed for robot teams.
Steels and Kaplan (2001) applied an instance-
based method to train a robot for object recognition
purposes. In this work objects are represented by
color histograms. Once different representations have
been learned from different views of the same object,
the recognition is performed by classifying new views
of objects using the KNN algorithm (Mitchell, 1997).
Ekvall et al. (2006) used different learning tech-
niques to acquire automatically semantic and spatial
information of the environment in a service robot sce-
nario. In this work, a mobile robot autonomously
navigates in a domestic environment, builds a map,
localizes its position in the map, recognizes objects
and locates them in the map. Background sub-
traction techniques are applied for foreground ob-
jects segmentation. Then objects are represented
by SIFT points (Lowe, 2004) and an appearance-
based method for detecting objects named Receptive
Field Co-occurrence Histograms (Ekvall and Kragic,
2005). The authors developed a method for active ob-
ject recognition which integrates both local and global
information of objects.
In the work of Mitri et al. (2004), a scheme for
fast color invariant ball detection in the RoboCup con-
text is presented. To ensure the color-invariance of
the input images, a preprocessing stage is first applied
for detecting edges using the Sobel filter, and specific
thresholds for color removal. Then, windows are ex-
tracted from images and predefined spatial features
such as edges and lines are identified in these win-
dows. These features serve as input to an AdaBoost
learning procedure that constructs a cascade of clas-
sification and regression trees (CARTs). The sys-
tem is capable of detecting different soccer balls in
RoboCup and other environments. The resulting ap-
proach is reliable and fast enough to classify objects
in real time.
Concerning the problem of collective learning of
objects using robot teams there are, as far as we know,
very few works. Montesano and Montano (2003) ad-
dress the problem of mobile object recognition based
on kinematic information. The basic idea is that if
the same object is being tracked by two different
robots, the trajectories and therefore the kinematic in-
formation observed by each robot must be compati-
ble. Therefore, location and velocities of moving ob-
jects are the features used for object recognition in-
stead of features such as color, texture, shape and size,
more appropriate for static object recognition. Robots
build maps containing the relative position of moving
objects and their velocity at a given time. A Bayesian
approach is then applied to relate the multiple views
of an object acquired by the robots.
In the work of O’Beirne and Schukat (2004), ob-
jects are represented with Principal Components (PC)
learned from a set of global features extracted from
images of objects. An object is first segmented and
its global features such as color, texture, and shape are
then extracted. Successive images in a sequence are
related to the same object by applying a Kalman fil-
ter. Finally, a 3D reconstructed model of an object is
obtained from the multiple views acquired by robots.
For that purpose, a Shape From Silhouette based tech-
nique (Cheung et al., 2003) is applied.
In contrast to previous works, in our method each
member of the robot team learns on-line individual
representations of objects without prior knowledge on
the number or nature of the objects to learn. Indi-
vidual concepts are represented as a combination of
global and local features extracted autonomously by
the robots from the training objects. A Bayesian ap-
proach is used to combine these features and used for
classification. Individual concepts are shared among
robots to improve their own concepts, combining in-
formation from other robots that saw the same object,
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