• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.653-660
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• 2000

The automatization by industrial robot of today is merely rely on to the simple position repeating works, but requirements of research and development to the force control which would adapt positively to various restriction or contacting works to environment. In this paper, a learning control algorithm using, neural networks is proposed for the position and force control by a direct-drive robot. The proposed controller is the feedback controller to which the learning function of neural network is added on to and has a character of improving controller's efficiency by learning. The effectiveness of the proposed algorithm is demonstrated by the experiment on the hybrid position and force control of a parallelogram link robot with a force sensor.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1228-1230
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• 1996

A neural network(NN) mechanism is proposed to modify the gait of a biped robot that walks on sloping surface using sensory inputs. The robot starts walking on a surface with no priori knowledge of the inclination of the surface. By accumulating experience during walking, the robot improves its walking gait and finally forms a gait that is adapted to the surface inclination. A neural controller is proposed to control the gait which has 72 reciprocally inhibited and excited neurons. PI control is used for position control, and the neurons are trained by a reinforcement learning mechanism. Experiments of static gait learning and pseudo dynamic learning are performed to show the validity of the proposed reinforcement learning mechanism.

• Journal of Korean society of Dental Hygiene
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• v.18 no.5
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• pp.729-740
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• 2018

Objectives: The purpose of this study was to investigate the factors affecting robot utilization in the education of pre-dental hygienists. Methods: A self-reported questionnaire was completed by 238 dental hygiene students studying in the Daejeon, Chungcheong, and Jeolla provinces during the period March 1-31, 2017. Results: Future oral health education media had high selection of 'movies,' 'video,' '3D printer,' 'robot,' and 'drone' In general education and oral health education, robots were appropriate as educators, assistant teachers, and media. This group had high levels of interest, experience, attitude, and learning scope of robots. Robot utilization education showed a significant positive correlation with the 'interest,' 'experience,' 'attitude,' and 'learning' subfactors (p<0.01). Factors influencing robot utilization education were the relationships among actual experience of robot, learning of robot production, social influence of robot, emotional exchange with robot, and the predictive power was 25.5% (p<0.05). Conclusions: Oral health education curricula using robots should be developed considering the emotional exchange and social influence between educator and learner.

• ETRI Journal
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• v.38 no.6
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• pp.1229-1239
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• 2016

For efficient interaction between humans and robots, robots should be able to understand the meaning and intention of human behaviors as well as recognize them. This paper proposes an interactive human intention reading method in which a robot develops its own knowledge about the human intention for an object. A robot needs to understand different human behavior structures for different objects. To this end, this paper proposes a hierarchical behavior knowledge network that consists of behavior nodes and directional edges between them. In addition, a human intention reading algorithm that incorporates reinforcement learning is proposed to interactively learn the hierarchical behavior knowledge networks based on context information and human feedback through human behaviors. The effectiveness of the proposed method is demonstrated through play-based experiments between a human and a virtual teddy bear robot with two virtual objects. Experiments with multiple participants are also conducted.

• Journal of the Korea Society of Computer and Information
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• v.19 no.6
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• pp.139-149
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• 2014

Much research has been conducted in educational robot, a new instructional technology, for K- 12 education. Several studies have shown that educational robot provides effective learning opportunities for students in both content areas of STEM(science, technology, engineering, and mathematics) and critical academic skills, such as collaboration, problem solving and communication skills. However, most studies to date on applications of educational robots have been conducted outside the formal education setting. This study analyzed the influence of using robots in an elementary school science class in Korea with regard to science learning motivation. A total of 121 students in fourth and fifth grades participated in the study. The experimental group was taught using robots in the science class, while the control group was taught using traditional methods. Analysis of covariance (ANCOVA) was conducted to compare the between-group differences in learning motivation before and after the experiment; an interview was also conducted for the experimental group. The study results showed a significant improvement (p<.05) in both learning motivation in the experimental compared with the control group. There was also positive response to learning with a robot. This study will play an important role in research on the use of educational robot in formal education in the future.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.1-7
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• 2022

Robots are widely used in industries and services. Traditional robots have been used to perform repetitive tasks in a fixed environment, and it is very difficult to solve a problem in which the physical interaction of the surrounding environment or other objects is complicated with the existing control method. Reinforcement learning has been actively studied as a method of machine learning to solve such problems, and provides answers to problems that robots have not solved in the conventional way. Studies on the learning of all physical robots are commonly affected by noise. Complex noises, such as control errors of robots, limitations in performance of measurement equipment, and complexity of physical interactions with surrounding environments and objects, can act as factors that degrade learning. A learning method that works well in a virtual environment may not very effective in a real robot. Therefore, this paper proposes a weighted sum method and a linear regression method as an effective and accurate learning method in a noisy environment. In addition, the bottle flipping was trained on a robot and compared with the existing learning method, the validity of the proposed method was verified.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.267-270
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• 2001

In this paper, we propose the self-autonomous algorithm for mobile robot system. The proposed mobile robot system which is teamed by learning with the neural networks can trace the target at the same distances. The mobile robot can evaluate the distance between robot and target with ultrasonic sensors. By teaming the setup distance, current distance and command velocity, the robot can do intelligent self-autonomous drive. We use the neural network and back-propagation algorithm as a tool of learning. As a result, we confirm the ability of tracing the target with proposed mobile robot.

• The Journal of Korea Robotics Society
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• v.9 no.3
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• pp.185-195
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• 2014

This study aims to look into students' and teachers' recognition about learning with a humanoid robot and seek for a policy implication for the direction of education using humanoid robot. To achieve this goal, a survey with elementary school students and teachers was used as the method of analysis. The main results are as follows: There was a difference in the recognition of the teachers and the students regarding the most effective subject through the use of humanoid robot. While the students consider Physical Education as the major subject, the teachers consider Science as the one. The students recognize that the use of humanoid is most effective in helping their learning while the teachers recognize that it is most effective in helping their teaching. As an expected positive effect, both of them choose an increase in interest in learning as the main effect of the use of humanoid robot, but the students, unlike the teachers, consider the improvement of their academic achievement as its main effect as well. These results show differences in the recognition of the use of humanoid between the teachers and the students, and in addition, confirm the difference between them depending on their background.

• Journal of The Korean Association of Information Education
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• v.15 no.3
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• pp.483-492
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• 2011

'Learning with a robot' is now considered as one of the best candidates for STEAM education, which is recently growing its importance. Most of the 'learning with a robot' programs in elementary schools serve as afterschool classes. The students participating in the afterschool classes are mostly boys who are interested in science and robots. This paper mainly concerns that a robot can be helpful for improving students' interest in STEAM education. We divided the robot utilizable aspects into 5 areas with educational points of view; abstract concept understanding type, structure-oriented type, athletics-oriented type, intelligence-oriented type and value-orientated type. We extracted all robot utilizable subjects and units from elementary school curriculum, and developed lesson plans which can be applicable to regular classes. And we also verified them by applying into an elementary school for 5 months. As the result of the analysis, we can conclude that 'learning with a robot' can encourage students' interest in STEAM, and it is more effective for girls than boys. Finally, we discuss problems that teachers may face in using a robot for regular classes, and make suggestions about the use of a robot for STEAM education.

• Journal of the Korean Society of Industry Convergence
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• v.19 no.1
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• pp.1-9
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• 2016

In this paper, we propose a new learning control scheme for various walk motion control of biped robot with same learning-base by neural network. We show that learning control algorithm based on the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multi layer back propagation neural network identification is simulated to obtain a dynamic model of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The biped robots have been received increased attention due to several properties such as its human like mobility and the high-order dynamic equation. These properties enable the biped robots to perform the dangerous works instead of human beings. Thus, the stable walking control of the biped robots is a fundamentally hot issue and has been studied by many researchers. However, legged locomotion, it is difficult to control the biped robots. Besides, unlike the robot manipulator, the biped robot has an uncontrollable degree of freedom playing a dominant role for the stability of their locomotion in the biped robot dynamics. From the simulation and experiments the reliability of iterative learning control was illustrated.