• Proceedings of the Korea Association of Information Systems Conference
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• 1996.11a
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• pp.217-227
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• 1996

The new field of learning control develops controllers that learn to improve their performance at executing a given task, based on experience performin this specific task. In a previous work［6］, the authors presented a theory of indirect learning control based on use of indirect adaptive control concepts employing simultaneous identification ad control. This paper develops improved indirect learning control algorithms, and studies the use of such controllers in decentralized systems. The original motivation of the learning control field was learning in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. The basic result of the paper is to show that stability of the indirect learning controllers for all subsystems when the coupling between subsystems is turned off, assures convergence to zero tracking error of the decentralized indirect learning control of the coupled system, provided that the sample time in the digital learning controller is sufficiently short.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1996.10a
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• pp.217-227
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• 1996

The new filed of learning control develops controllers that learn to improve their performance at executing a given task , based on experience performing this specific task. In a previous work[6], authors presented a theory of indirect learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper develops improved indirect learning control algorithms, and studies the use of such controller indecentralized systems. The original motivation of the learning control field was learning in robots doing repetitive tasks such as on an asssembly line. This paper starts with decentralized discrete time systems. and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. The resultof the paper is to show that stability of the indirect learning controllers for all subsystems when the coupling between subsystems is turned off, assures convergence to zero tracking error of the decentralized indirect learning control of the coupled system, provided that the sample tie in the digital learning controller is sufficiently short.

• Journal of Korea Game Society
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• v.16 no.1
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• pp.93-102
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• 2016

In this research, we investigated the effect of robot programming education on adaption for school life of student with learning disabilities focusing on learned helplessness and the quantity of spontaneous communication. The participant of this study was the student supported with LD really in 4th grade elementary school. Results of the study present that learned helplessness was declined, the quantity of spontaneous communication was increased throughout robot programming education. Based on results, these finding suggested ways to practice the application of strengths-based instruction, intervention utilizing gamification for school life of student with learning disabilities in educational setting.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.1058-1068
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• 2013

The purpose of this study was to examine how participation in R-Learning university club management program affects to R-Learning professionalism of pre-teachers in field of early childhood education related to knowledge, function, and attitude. Upon investigation for knowledge part, those answers: 'I know the role of teachers when education based on robot, 'I know how much education based on robot affects to development of early childhood', and 'I know the necessary of education based on robot' appear highly. 'I can give lessons by connecting robot and computer' for function part, and 'I think using robot for class positively' for attitude part show highly. Also, professionalism of the pre-teachers improved after participating in R-running club, especially, function and attitude part. Thus, R-Learning university club management program is effective by the research.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.131-137
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• 2006

In swarm robot systems, each robot must behaves by itself according to the its states and environments, and if necessary, must cooperates with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforcement learning having delayed reward ability and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforcement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper. we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.8
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• pp.56-64
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• 1997

In distributed autonomous robotic systems, each robot must behaves by itself according to the its states and environements, and if necessary, must cooperates with other orbots in order to carray out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforement learning having delayed reward ability and distributed genectic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the perfodrmance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper, we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.165-172
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• 2015

The purpose of this study was to identify the effect of Genibo program robot-based learning(R-Learning) on a pre-schooler's mental state. To achieve above study purpose, the subject of this study was selected 46(teacher 2, five years old pre-schooler 44) from pre-school childrens in Kyongki Y city(R-Learning activity participants group 21: boys 10, girls 11. non-participants 25: boys 13, girls 12). R-Learning program is consist of 5 field about 20 contents using Genibo robot, were applied to the experimental group and the pre-post test was conducted using the EQ assessment tool and observations. The data were analyzed by t-test using the SPSS(ver 18.0) program. The results were as follows: First, the exposure of robots to pre-schoolers in practical situation has shown positive influence to the children's emotional well-being. Positive improvements were observed in the four sub categories of the EQ assessment after exposure. Second, the Genibo used for this study, is a biomimetic AI based robot mimicking the behavior of a pet dog. This is related more or less to the specifications of a pre-school education where animals are used as a 'friendly medium' to facilitate the learning process. Third, the robot exposure gave benefit to all the ones in the sample, regardless of sex. Furthermore, It is suggested that promising potential for robots to be utilized as a new educational media plus facilitator, R-Learning is related more or less to the specifications of a pre-school education where animals are used as a 'friendly medium' to facilitate the learning process, and when applying them for education, stereotyping the likes of sex is overrated - instead, the focus should be more on the pre-schoolers' / childrens' individual traits, learning curve differences and alike.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.211-217
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• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the teaming control field was teaming in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Error wave propagation method will show up in the numerical simulation for five-bar linkage as a vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link at each time step in repetition domain. Those can be helped to apply to the vertical multiple dynamic systems for precision quality assurance in the industrial robots and medical equipments.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.2
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• pp.40-47
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• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work the authors presented an iterative precision of linear decentralized learning control based on p-integrated teaming method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the loaming control field was learning in robots doing repetitive tasks such as on a]1 assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Error wave propagation method will show up in the numerical simulation for five-bar linkage as a vertical dynamic robot. The methods of learning system are shown up for the iterative precision of each link at each time step in repetition domain. Those can be helped to apply to the vertical multiple dynamic systems for precision quality assurance in the industrial robots and medical equipments.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.1-22
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• 1997

Learning control refers to controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented a theory of indirect decentralized learning control based on use of indirect adaptive control concepts employing simultaneous identification and control. This paper extends these results to apply to the indirect repetitive control problem in which a periodic (i.e., repetitive) command is given to a control system. Decentralized indirect repetitive control algorithms are presented that have guaranteed convergence to zero tracking error under very general conditions. The original motivation of the repetitive control and learning control fields was learning in robots doing repetitive tasks such as on an assembly line. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the desired trajectory. Decentralized repetitive control is natural for this application because the feedback control for link rotations is normally implemented in a decentralized manner, treating each link as if it is independent of the other links.