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

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1 Joural of Robotics, Networkig ad Artificial Life, Vol. 1, No. 2 (September 2014), Applicatio of Self-Orgaizig Map for aalyzig robotic arm s actio with Cosciousess-Based Architecture module Wisau Jitviriya Departmet of Mechaical Iformatio Sciece ad Techology, Kyushu Istitute of Techology, 680-4, Kawazu, Iizuka, Fukuoka, , Japa wisau@mmcs.mse.kyutech.ac.jp Eiji Hayashi Departmet of Mechaical Iformatio Sciece ad Techology, Kyushu Istitute of Techology, 680-4, Kawazu, Iizuka, Fukuoka, , Japa haya@mse.kyutech.ac.jp Abstract Our research has bee focused o developig huma-robot iteractios, the robots eed to perform a high level of itellectual activity ad user compatibility. Therefore, we cosidered the primary structure of a coscious huma/aimal actio, which ca be represeted by the sequece process, Perceptio Motivatio Actio. We have improved a hierarchical structure model, which defies the relatioship betwee the cosciousess field ad the behavior module. This model is called Cosciousess-Based Architecture (CBA). Furthermore, the robot should select the actio itself, we have ivestigated the applicatio of brai-ispired techology so we itroduced a Self- Orgaizig Map (SOM) eural etwork that is traied usig usupervised learig to classify its behavior accordig to the motivatio value i the CBA module. I this paper, we attempt to describe the itegratio of a Self-Orgaizig Map (SOM) method ito a CBA module i order to classify ad select autoomous behavior. We cofirmed the effectiveess of the proposed system by the experimetal results. Keywords: Cosciousess-Based Architecture (CBA), Huma-robot iteractios, Self-Orgaizig Map (SOM). 1. Itroductio Nowadays, the rapid developmet of high techology has produced o-idustrial robots such as robotic pets for kids, home robots, therapeutic robots ad service robots. These robots are desiged for etertaimet, guide i museums, educatio, welfare ad other purposes. The ecessary challege i the developmet of these robots is a high level of itellectual ability ad user compatibility. User compatibility i the future robot is a importat for ease of use, robot friedliess, huma-like capricious behavior ad the robots are more attractive to people if their movemets are ot edlessly repeated. However, so far the developmet of the robots has met with problems i the recogitio system, memorizig the situatio, the actio selectio ad commuicatio etc., whe the robots iteract with huma. Therefore, our research has bee focused o developig the robot that has process of the actios ad cosciousess resemblig that of a huma or a aimal which is able to ehace the liveliess ad the likelihood of their iteractios with users [1]-[3]. We have costructed a hierarchical structure model, which defies the relatioship betwee the cosciousess field Published by Atlatis Press 135

2 usupervised learig, the robot could select a actio by fidig the maximum similarity of the each actio. 2. System Structure of Cobe-I Fig.1. System Structure of Cobe-I Joit 3 θ 3 Joit 4 θ 4 Joit 7 θ 6 θ 7 Joit 5 θ 5 Joit 6 θ 2 Joit 2 Joit 1 θ 1 Figers ad behavior module i the robot. This model is called Cosciousess-Based Architecture (CBA). We have improved the itegratio of a Self-Orgaizig Map (SOM), oe kid of usupervised artificial eural etworks (ANNs) proposed by Kohoe [4]-[6], ad based o the euro orgaizatio ad decisio-makig process i the huma brai ito CBA module. I order to realize a practical robot that ca autoomously choose its behavior ad adapt to its eviromet. I this paper proposes a usupervised classificatio method or Self Orgaizig Map (SOM) combied with the CBA module for use with a robotic arm. First, we showed the outlie of the proposed overview system ad described a autoomous behavior system of a coscious behavior robot (Cobe-I), cosistig of a recogitio of the exteral situatio usig a web camera, calculatig the aturally occurrig dopamie ad determiig the robot s motivatio, selectig a actio usig Self Orgaizig Map combied with CBA module ad the drive of the actuator which accepted a actio. Next, we described the SOM cotext that cosists of the classificatio as likig behavior ad dislikig behavior whe the robot recogizes the gree ad blue object, respectively, ad the ormalizatio process for preparig the iput data to be suitable for traiig. Fially, the kids of actios such as look aroud, iterest, be alert, approach, catch the object ad avoid were classified usig a weight matrix derived by dividig the cells o the map based o O7 T2 T1 O6 T6 O5 T5 O2 O1 T3 Wrist O3 T4 Elbow O4 Shoulder z y x Fig.2. Degree of freedom i the robotic arm ad the robotic arm is divided ito 4 parts. The figure 1 shows the appearace of a coscious behavior robot (Cobe-I), the robotic arm is divided ito 2 parts, a arm ad a had. The arm part has 6 levels of flexibility: the shoulder (Joit1, Joit2), the elbow (Joit3, Joit4) ad the wrist (Joit5, Joit6). The had part has 3 figers ad oe level of flexibility. So thus the robotic arm has a total of 7 degrees of freedom as show i Fig.2 ad a small web camera equipped at the palm of the robotic arm which is able to recogize a exteral situatio. The web camera ad the multiple actuators are cotrolled usig the RS-485 serial data commuicatio. Flowchart for Autoomous Behavior System The mai system Recogize the exteral situatio usig a web camera Calculatig the aturally occurrig dopamie ad Determie the robot s motivatio Actio selectio accordig to the SOM-CBA module The drive of the motor which accepted a actio 3. Autoomous Behavior System SOM-CBA module Iitialize all the euros with radom values ad data ormalizatio Calculate the distace betwee the iput patter ad weights of each euro. Ad select the earest euros as wier (BMU) Update each euro aroud the wier euro Max iteratios Fig.3. The flowchart of autoomous behavior system McCarthy has idicated that a robot will eed to cosider ad itrospect i order to operate i the commo sese world, it will eed to have a cosciousess ad itrospective kowledge ad some philosophy [7], [8], so we describe the overview system by the flowchart of the autoomous behavior system is show i Fig.3 ad the details of each item are described below Recogize a exteral situatio by a web camera I this research, the images were simplified by divided ito three color groups: gree, blue ad flesh-color, which were distiguished ad recogitio i terms of the shape, size ad ceter-of-gravity positio as show Yes No Published by Atlatis Press 136

3 w = ( w, w,..., w ) R j 1j 2j j Postive dopamie Fig.4. Simplified image. Motivatio model x = ( x1, x2,..., x) R Iput layer Positive Output space Fig.6. A basic rectagular SOM topology Negative Negative dopamie The total of dopamie time Equatio (3). Here, motivatio() t is the robot s motivatio ad ut () is the total of aturally occurrig dopamie. Fig.5. Motivatio model. motivatio''( t) + 2 ζω motivatio'( t) ω 2 2 motivatio t = ω () u() t (3) i Fig.4. The robot could recogize the positio ad its distace from the target object i order to calculate the aturally occurrig dopamie model Calculatig the aturally occurrig dopamie ad determiig the robot s motivatio Whe humas ad aimals take various actios, chages occur i the dopamie i the brai. Dopamie affects brai processes that cotrol movemet, emotio respose, ad ability to experiece pleasure ad pai, so we used the typical patter of aturally occurrig dopamie for determiig the robot s motivatio. The waveform of aturally occurrig dopamie divided ito the rise ad fall parts, which are give by Equatio (1) ad (2) respectively, where yt () is the aturally occurrig dopamie, xt () is the iput data from image recogitio, ω is the atural agular frequecy, ζ is the dampig factor ad T is the time costat. 2 2 y''() t + 2 ζω y'() t + ω yt () ω xt () = 0 (1) Ty'() t + y() t = x() t (2) The total of dopamie is calculated by the sum of their positive (the gree objects) ad egative values (the blue objects), the the total of dopamie is cosidered as the iput variable for determiig the robot s motivatio as show i Fig.5 ad expressed by a 2d order system of liear differece equatio as show i 3.3. Choose a actio usig a Self-Orgaizig Map method with CBA module I this sectio, we are aimig for a itegrated developmet the autoomous behavior system that combies a sample the Self-Orgaizig Map ad CBA module i order to classify ad select its behavior correspod to its eviromet ad the details are explaied below Self-Orgaizig Map Algorithm The basic Self-Orgaizig Map (SOM) cosists of the iput layer ad the output layer which is fully coected with the iput layer by the adjusted weights. After learig of the SOM, the weight vector of a particular cell defies the ceter of a certai class of the iput. I Fig.6 a typical two-dimesioal rectagular SOM is show. The iput vector x = ( x1, x2,..., x) R is coected to cell j ad the respective coectio has a weight wj = ( w1j, w2j,..., wj ) R. The learig of the SOM is performed i the followig steps: Step 1. Iitially, we assig to the weight vectors w j (0) radom values, ad we ormalize the values of w j (0) i [0,1]. Step 2. Preset a iput vector x to the etwork, the we compute the cell j that is closest to it by meas of the Euclidea distace ad the fid the wier of uit c (Best Matchig Uit: BMU) which has the Published by Atlatis Press 137

4 miimum Euclidea distace as expressed by Equatio (4). U = mi xj wj( t) (4) c Step 3. The adaptatio process adjusts the weight vector of cell j ad the weight vectors of its eighborig cells by Equatio (5). wj( t + 1) = wj() t + η() t x() t wj() t (5) Step 4. The time t icreases to t+1, if t < T the go to step 2; otherwise stop the traiig. Where, it is the time of the iteratio, T is the predefied umber of iteratios ad η() t is the learig rate, which is decreasig fuctio of time by Equatio (6). η( t) = η0 exp( t T ) (6) Combie the SOM with CBA module Our previous research, the CBA model decides o the coscious level that the robot must strogly cosider ad select the behavior correspodig to the coscious level. I this paper, the CBA has bee developed as show i Fig.7, the CBA model has a 3-level hierarchy i the motivatio module that coects with a SOM module for clusterig actios i a 3-level hierarchy i the behavior module. 4. Experimetal results 4.1. Classificatio results The actios of the robotic arm are divided ito two characteristic behaviors, accordig to the value of the motivatio ad the effect of surface color o the object recogitio. For istace, whe the robot sees a gree object (Likig behavior), the motivatio value rises ad the robot looks aroud ad iterests the target object after that the robot attempts to approach or catch the object as show i Fig.8. O the other had, whe the robot recogizes a blue object (Dislikig behavior), the motivatio value drops ad the robot should be alert ad avoid the object as show i Fig.9. I additio, whe the robot recogized the flesh color, the actios are clustered aroud iterested behavior ad alert behavior. I this paper, the Mi-Max Normalizatio techique is used to scale the iput sample i the rage of 0 ad 1 depedig o its relative size. The parameters of SOM as show i Table 1. To make it easy to uderstad, Fig.10 show the all actios of the robotic arm, which were classified usig a Exteral situatio Recogize: Color Shape Distace CBA with Self-Orgaizig Map Module Motivatio Level 3 Level 2 Level SOM Behavior Search Escape Gaze Ope-mouthed Stad Wake Look Rest Sleep Fig.7. Combie SOM ito CBA module. Look aroud Iterest the gree object to the gree object Grasp the gree object Fig.8 Likig behavior : recogize the gree object Look aroud to the blue object Avoid the blue object Actio Motor cotrol Fig.9 Dislikig behavior : recogize the blue object Table 1. The parameters of SOM. Value Descriptio Moti_flag 0 ad 1 The positive value is 1, ad the egative value is 0. Moti_value 0 to 1 The motivatio value Gree 0 to 1 Pixel of gree color Blue 0 to 1 Pixel of blue color Flesh 0 to 1 Pixel of flesh color t 1000 The umber of learig η The iitial learig rate Map size 20 x 20 The rectagular SOM topology size weight matrix derived by dividig the cells o the map based o usupervised learig SOM. For istace, if the iput data was Moti_flag = 1, Moti_value = 0.5, Gree = 0.8, Blue = 0 ad Flesh = The robot recogized the gree object ad the target object was ear the robot s had, whe the system fiished the SOM learig. The blue cells were show i Fig.10, which were the wier ode of each behavior ad the Published by Atlatis Press 138

5 gree cell was the respose actio from SOM learig, so i this state the robot autoomously selected the Catch the object actio Verificatio experimet We let the robotic arm recogized a gree ball (Likig) ad a blue ball (Dislikig). We observed the actio of the Cobe-I util it took a gree ball from the huma s had. The trasitio of the motivatio is show i Fig.11. After the Cobe-I was started it recogized the gree ball at T1, the the value of motivatio icreased steadily ad the robot selected the actio betwee lookig aroud behavior ad a iterestig behavior. From T2-T3, it was selected actio ad it bega to approach the gree ball ad opeed the figers after that the robot came close eough to the gree ball to catch it i a time iterval (T3-T4), but o the other had, the blue ball was show i order to decrease the robot s motivatio ad the motivatio became to the egative value. I the time iterval from T5 to T8 that show the dislikig behavior that cosists of Look aroud, Alertess ad Avoidace respectively. Motivatio level Iterest (Gree) (Gree) Look aroud (Gree) Iterest (Flesh) Catch the ball (Gree) Look aroud Fig.10. The classificatio o the map. Look aroud + Iterest Catch Look aroud (Flesh) Avoid 5. Coclusio I this paper, we preset the sample applicatio of Self Orgaizig Map (SOM) for aalyzig ad clusterig the robotic arm s actio. From the experimetal results, 9 motios of the robotic arm could be classified o the map of SOM ad the robotic arm could autoomously select its behavior ad adapt to its surroudig eviromet. I the future study, our research will cotiue to develop the system of Cobe-I such as it becomes possible to create a emotioal system ad apply the memorizatio system based o a artificial eural etwork ito the robot s system. Refereces 1. N. Goto ad E. Hayashi, Desig of Robotic Behavior that imitates aimal cosciousess (Joural of Artificial Life ad Robotics Vol.12, Spriger 2008 ), pps Eiji Hayashi, Takahiro Yamasaki ad Koichiro Kuroki, Autoomous Behavior System Combig Motivatio with Cosciousess Usig Dopamie (IEEE Iteratioal Symposium o Computatioal Itelligece i Robotics ad Automatio, 2009), pp Motoki Yoshida, Eiji Hayashi, Desig of robotic behavior that imitates aimal cosciousess: Costructio of the user-recogitio systems (Artificial Life Robotics, AROB(17), 2012). T1 T2 T3 T4 T5 T6 Time(s) Avoid Fig.11. Trasitio of the motivatio. 4. T. Kohoe, Self-Orgaizig Maps (Series i Iformatio Scieces, Vol. 30, Spriger, Heidelberg. Secoded, 1995). 5. T. Kohoe, The self-orgaizig map (Neurocomputig, Vol.21, 1998), pp T. Kohoe, Essetials of the self-orgaizig map (Neural Networks, Vol. 37, 2013), pp McCarthy J (1995) Proceedigs of the fourteeth iteratioal joit coferece o Artificial Itelligece, IJCAI 95, pp McCarthy J (1996) Makig robots coscious of their metal states (I: Muggleto S (ed) Machie itelligece, vol. 15, Oxford Uiversity Press, Oxford), pp T7 T8 Published by Atlatis Press 139

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