Journal of Robotics, Networking and Artificial Life, Vol. 1, No. 2 (September 2014), 135-139

Application of Self-Organizing Map for analyzing robotic arm’s action
with Consciousness-Based Architecture module
Wisanu Jitviriya

Department of Mechanical Information Science and Technology, Kyushu Institute of Technology,
680-4, Kawazu, Iizuka, Fukuoka, 820-8502, Japan
E-mail: wisanu@mmcs.mse.kyutech.ac.jp
Eiji Hayashi

Department of Mechanical Information Science and Technology, Kyushu Institute of Technology,
680-4, Kawazu, Iizuka, Fukuoka, 820-8502, Japan
E-mail: haya@mse.kyutech.ac.jp

Abstract
Our research has been focused on developing human-robot interactions, the robots need to perform a high level ofintellectual activity and user compatibility. Therefore, we considered the primary structure of a conscious
human/animal action, which can be represented by the sequence process, Perception  Motivation  Action. We
have improved a hierarchical structure model, which defines the relationship between the consciousness field andthe behavior module. This model is called Consciousness-Based Architecture (CBA). Furthermore, the robot should
select the action itself, we have investigated the application of brain-inspired technology so we introduced a SelfOrganizing Map (SOM) neural network that is trained using unsupervised learning to classify its behavior
according to the motivation value in the CBA module. In this paper, we attempt to describe the integration of a
Self-Organizing Map (SOM) method into a CBA module in order to classify and select autonomous behavior. We
confirmed the effectiveness of the proposed system by the experimental results.
Keywords: Consciousness-Based Architecture (CBA), Human-robot interactions, Self-Organizing Map (SOM).

1. Introduction
Nowadays, the rapid development of high technology
has produced non-industrial robots such as robotic petsfor kids, home robots, therapeutic robots and service
robots. These robots are designed for entertainment,
guide in museums, education, welfare and otherpurposes. The necessary challenge in the developmentof these robots is a high level of intellectual ability anduser compatibility. “User compatibility” in the future
robot is an important for ease of use, robot friendliness,
human-like capricious behavior and the robots are more

attractive to people if their movements are not endlessly
repeated. However, so far the development of the robots
has met with problems in the recognition system,
memorizing the situation, the action selection andcommunication etc., when the robots interact withhuman. Therefore, our research has been focused on
developing the robot that has process of the actions andconsciousness resembling that of a human or an animal
which is able to enhance the liveliness and thelikelihood of their interactions with users [1]-[3]. We
have constructed a hierarchical structure model, which
defines the relationship between the consciousness field

Published by Atlantis Press
Copyright: the authors
135

unsupervised learning, the robot could select an action
by finding the maximum similarity of the each action.
2. System Structure of Conbe-I

Fig.1. System Structure of Conbe-I
Joint 3

θ3

θ2

Joint 4

Joint 2

WristO6
T6

Joint 1θ4Fingers

O5
T5

O7

θ1Elbow
T4T3

Joint 5

Joint 7

θ7

θ6

Joint 6

O4O3

θ5

T2

O2

T1

O1

Shoulderzyx

Fig.2. Degree of freedom in the robotic arm and the roboticarm is divided into 4 parts.

and behavior module in the robot. This model is called
Consciousness-Based Architecture (CBA). We have
improved the integration of a Self-Organizing Map(SOM), one kind of unsupervised artificial neuralnetworks (ANNs) proposed by Kohonen [4]-[6], andbased on the neuron organization and decision-making
process in the human brain into CBA module. In orderto realize a practical robot that can autonomously
choose its behavior and adapt to its environment.
In this paper proposes an unsupervised classification
method or Self Organizing Map (SOM) combined with
the CBA module for use with a robotic arm. First, weshowed the outline of the proposed overview systemand described an autonomous behavior system of aconscious behavior robot (Conbe-I), consisting of arecognition of the external situation using a web
camera, calculating the naturally occurring dopamineand determining the robot’s motivation, selecting anaction using Self Organizing Map combined with CBA
module and the drive of the actuator which accepted anaction. Next, we described the SOM context thatconsists of the classification as “liking” behavior and“disliking” behavior when the robot recognizes thegreen and blue object, respectively, and thenormalization process for preparing the input data to besuitable for training. Finally, the kinds of actions suchas look around, interest, be alert, approach, catch theobject and avoid were classified using a weight matrix
derived by dividing the cells on the map based on

The figure 1 shows the appearance of a conscious
behavior robot (Conbe-I), the robotic arm is divided into
2 parts, an arm and a hand. The arm part has 6 levels offlexibility: the shoulder (Joint1, Joint2), the elbow(Joint3, Joint4) and the wrist (Joint5, Joint6). The handpart has 3 fingers and one level of flexibility. So thus
the robotic arm has a total of 7 degrees of freedom asshown in Fig.2 and a small web camera equipped at thepalm of the robotic arm which is able to recognize anexternal situation. The web camera and the multiple
actuators are controlled using the RS-485 serial data
communication.
Flowchart for Autonomous Behavior System
The main system

SOM-CBA module

Recognize the external situation
using a web camera

Initialize all the neurons with random
values and data normalization

Calculating the naturally occurring dopamineand Determine the robot’s motivation

Calculate the distance between the input
pattern and weights of each neuron. And
select the nearest neurons as winner(BMU)

Action selection according to theSOM-CBA module

Update each neuron aroundthe winner neuron

The drive of the motor which
accepted an action

Max iterations

NoYes

Fig.3. The flowchart of autonomous behavior system

3. Autonomous Behavior System
McCarthy has indicated that a robot will need toconsider and introspect in order to operate in thecommon sense world, it will need to have aconsciousness and introspective knowledge and some
philosophy [7], [8], so we describe the overview systemby the flowchart of the autonomous behavior system isshown in Fig.3 and the details of each item aredescribed below.
3.1. Recognize an external situation by a web
cameraIn this research, the images were simplified by divided
into three color groups: green, blue and flesh-color,which were distinguished and recognition in terms of
the shape, size and center-of-gravity position as shown

Published by Atlantis PressCopyright: the authors136wj( w1,w2jnR,wn, ...

j)∈j=

Fig.4. Simplified image.
=x ( x1, x2 ,..., xn ) ∈ R n
Input layer

Motivation model

Positive

Postive dopamine

Output space

Fig.6. A basic rectangular SOM topology
timeEquation (3). Here, motivation(t ) is the robot’s

Negative

The total of dopamine

motivation and u (t ) is the total of naturally occurring
dopamine.

Negative dopamine

motivation ''(t ) + 2ζωn motivation '(t )ωn2 motivation(t ) = ωn2u (t )Fig.5. Motivation model.

in Fig.4. The robot could recognize the position and its
distance from the target object in order to calculate thenaturally occurring dopamine model.
3.2. Calculating the naturally occurring dopamineand determining the robot’s motivation
When humans and animals take various actions,changes occur in the dopamine in the brain. Dopamine
affects brain processes that control movement, emotion
response, and ability to experience pleasure and pain, so
we used the typical pattern of naturally occurring
dopamine for determining the robot’s motivation. Thewaveform of naturally occurring dopamine divided into
the rise and fall parts, which are given by Equation (1)and (2) respectively, where y (t ) is the naturallyoccurring dopamine, x(t ) is the input data from image
recognition, ωn is the natural angular frequency, ζ is thedamping factor and T is the time constant.
y ''(t ) + 2ζωn y '(t ) + ωn2 y (t ) − ωn2 x(t ) =0(1)
Ty '(t ) + y (t ) =x(t )(2)

The total of dopamine is calculated by the sum of theirpositive (the green objects) and negative values (theblue objects), then the total of dopamine is consideredas the input variable for determining the robot’s
motivation as shown in Fig.5 and expressed by a 2ndorder system of linear difference equation as shown in(3)3.3. Choose an action using a Self-Organizing
Map method with CBA module
In this section, we are aiming for an integrated
development the autonomous behavior system thatcombines a sample the Self-Organizing Map and CBA
module in order to classify and select its behavior
correspond to its environment and the details are
explained below.
3.3.1. Self-Organizing Map Algorithm
The basic Self-Organizing Map (SOM) consists of theinput layer and the output layer which is fully connectedwith the input layer by the adjusted weights. Afterlearning of the SOM, the weight vector of a particularcell defines the center of a certain class of the input. InFig.6 a typical two-dimensional rectangular SOM isshown. The input=vector x ( x1, x2 ,..., xn ) ∈ R n isconnected to cell j and the respective connection has an=weight w j ( w1 j , w2 j ,..., wnj ) ∈ R . The learning of theSOM is performed in the following steps:
Step 1. Initially, we assign to the weight vectors

w j (0) random values, and we normalize the values ofw j (0) in [0,1].

Step 2. Present an input vector x to the network,
then we compute the cell j that is closest to it by meansof the Euclidean distance and then find the winner ofunit c (Best Matching Unit: BMU) which has thePublished by Atlantis Press
Copyright: the authors137minimum Euclidean distance as expressed by Equation(4).
=U c min x j − w j (t )(4)

Step 3. The adaptation process adjusts the weightvector of cell j and the weight vectors of its neighboring
cells by Equation (5).
w j (t + 1)
= w j (t ) + η (t ) ⋅  x(t ) − w j (t ) 

CBA with Self-Organizing Map Module
Motivation

( )

SOM

Externalsituation
Recognize:
ColorShapeDistanceLevel 2++

(5)

Gaze
Open-mouthedStandAction
Motor
control

Fig.7. Combine SOM into CBA module.
Look around

Interest the green object
Approach tothe green object(6)

3.3.2. Combine the SOM with CBA module

Grasp the green object

Our previous research, the CBA model decides on theconscious level that the robot must strongly consider
and select the behavior corresponding to the conscious
level. In this paper, the CBA has been developed asshown in Fig.7, the CBA model has a 3-level hierarchyin the motivation module that connects with a SOM
module for clustering actions in a 3-level hierarchy inthe behavior module.
4.

SearchApproach
EscapeWakeLookRestSleep

Level 1

Step 4. The time t increases to t+1, if t < T then go
to step 2; otherwise stop the training.
Where, it is the time of the iteration, T is the predefined
number of iterations and η (t ) is the learning rate, which
is decreasing function of time by Equation (6).

η (t=) η0 ⋅ exp −t
TBehavior

Level 3Fig.8 “Liking behavior”: recognize the green object

Look aroundBe alert tothe blue object
Avoid the blue object

Experimental results
Fig.9 “Disliking behavior”: recognize the blue object

4.1. Classification results
The actions of the robotic arm are divided into twocharacteristic behaviors, according to the value of themotivation and the effect of surface color on the object
recognition. For instance, when the robot sees a greenobject (Liking behavior), the motivation value rises andthe robot looks around and interests the target object
after that the robot attempts to approach or catch theobject as shown in Fig.8. On the other hand, when therobot recognizes a blue object (Disliking behavior), themotivation value drops and the robot should be alert andavoid the object as shown in Fig.9. In addition, whenthe robot recognized the flesh color, the actions areclustered around interested behavior and alert behavior.
In this paper, the Min-Max Normalization technique isused to scale the input sample in the range of 0 and 1
depending on its relative size. The parameters of SOM
as shown in Table 1.
To make it easy to understand, Fig.10 shown the all
actions of the robotic arm, which were classified using aTable 1. The parameters of SOM.
Value
Moti_flag

0 and 1

Moti_value
GreenBlueFlesht
η0

0 to 1
0 to 1
0 to 1
0 to 1
10000.35

Map size20 x 20

Description
The positive value is 1, andthe negative value is 0.
The motivation value
Pixel of green color
Pixel of blue color
Pixel of flesh color
The number of learning
The initial learning rate
The rectangular SOM
topology size

weight matrix derived by dividing the cells on the map
based on unsupervised learning SOM. For instance, if
the input data was Moti_flag = 1, Moti_value = 0.5,
Green = 0.8, Blue = 0 and Flesh = 0.1404. The robot
recognized the green object and the target object wasnear the robot’s hand, when the system finished theSOM learning. The blue cells were shown in Fig.10,
which were the winner node of each behavior and thePublished by Atlantis Press
Copyright: the authors
138

green cell was the response action from SOM learning,
so in this state the robot autonomously selected the“Catch the object” action.

Look around(Green)Interest(Green)
Be alert(Flesh)4.2. Verification experiment

Avoid(Blue)

Interest(Flesh)

Be alert(Blue)

Approach(Green)Look around(Blue)

Catch the ball(Green)

Fig.10. The classification on the map.

CatchApproach

Motivation level

We let the robotic arm recognized a green ball (Liking)and a blue ball (Disliking). We observed the action ofthe Conbe-I until it took a green ball from the human’shand. The transition of the motivation is shown in
Fig.11. After the Conbe-I was started it recognized thegreen ball at T1, then the value of motivation increased
steadily and the robot selected the action between
looking around behavior and an interesting behavior.
From T2-T3, it was selected “Approach” action and it
began to approach the green ball and opened the fingers
after that the robot came close enough to the green ballto catch it in a time interval (T3-T4), but on the otherhand, the blue ball was shown in order to decrease therobot’s motivation and the motivation became to thenegative value. In the time interval from T5 to T8 thatshown the disliking behavior that consists of “Lookaround”, “Alertness” and “Avoidance” respectively.

Look around

Look around + Interest

Be alert

5.

Conclusion

Avoid

In this paper, we present the sample application of Self
Organizing Map (SOM) for analyzing and clustering therobotic arm’s action. From the experimental results, 9
motions of the robotic arm could be classified on themap of SOM and the robotic arm could autonomously
select its behavior and adapt to its surrounding
environment. In the future study, our research willcontinue to develop the system of Conbe-I such as it
becomes possible to create an emotional system andapply the memorization system based on an artificial
neural network into the robot’s system.
References
1. N. Goto and E. Hayashi, Design of Robotic Behavior that
imitates animal consciousness (Journal of Artificial Lifeand Robotics Vol.12, Springer 2008 ), pps. 97-101.
2. Eiji Hayashi, Takahiro Yamasaki and Koichiro Kuroki,
Autonomous Behavior System Combing Motivation withConsciousness Using Dopamine (IEEE International
Symposium on Computational Intelligence in Roboticsand Automation, 2009), pp.126-131.
3. Motoki Yoshida, Eiji Hayashi, Design of robotic
behaviorthat
imitatesanimalconsciousness:
Construction of the user-recognition systems (ArtificialLife Robotics, AROB(17), 2012).

T1 T2

T3

T4

T5

Time(s)

T6

T7

T8

Fig.11. Transition of the motivation.
4. T. Kohonen, Self-Organizing Maps (Series inInformation Sciences, Vol. 30, Springer, Heidelberg.
Seconded, 1995).
5. T. Kohonen, The self-organizing map (Neurocomputing,Vol.21, 1998), pp.1-6.
6. T. Kohonen, Essentials of the self-organizing map(Neural Networks, Vol. 37, 2013), pp.52-65.
7. McCarthy J (1995) Proceedings of the fourteenthinternational joint conference on Artificial Intelligence,
IJCAI 95, pp.2041–2044.
8. McCarthy J (1996) Making robots conscious of theirmental states (In: Muggleton S (ed) Machine intelligence,
vol. 15, Oxford University Press, Oxford), pp. 3–17.

Published by Atlantis PressCopyright: the authors
139