• 제어로봇시스템학회논문지
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• 제13권4호
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• pp.358-364
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• 2007

Intelligent walking modeling of humanoid robot using learning based neuro-fuzzy system is presented in this paper. Walking pattern, trajectory of the zero moment point (ZMP) in a humanoid robot is used as an important criterion for the balance of the walking robots but its complex dynamics makes robot control difficult. In addition, it is difficult to generate stable and natural walking motion for a robot. To handle these difficulties and explain empirical laws of the humanoid robot, we are modeling practical humanoid robot using neuro-fuzzy system based on the two types of natural motions which are walking trajectories on a t1at floor and on an ascent. Learning based neuro-fuzzy system employed has good learning capability and computational performance. The results from neuro-fuzzy system are compared with previous approach.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.174-174
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• 2000

Q-learning, based on discrete state and action space, is a most widely used reinforcement Learning. However, this requires a lot of memory and much time for learning all actions of each state when it is applied to a real mobile robot navigation using continuous state and action space Region-based Q-learning is a reinforcement learning method that estimates action values of real state by using triangular-type action distribution model and relationship with its neighboring state which was defined and learned before. This paper proposes a new Region-based Q-learning which uses a reward assigned only when the agent reached the target, and get out of the Local optimal path with adjustment of random action rate. If this is applied to mobile robot navigation, less memory can be used and robot can move smoothly, and optimal solution can be learned fast. To show the validity of our method, computer simulations are illusrated.

• Journal of Information Processing Systems
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• 제4권4호
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• pp.159-168
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• 2008

Human-Robot Interaction (HRI), based on already well-researched Human-Computer Interaction (HCI), has been under vigorous scrutiny since recent developments in robot technology. Robots may be more successful in establishing common ground in project-based education or foreign language learning for children than in traditional media. Backed by its strong IT environment and advances in robot technology, Korea has developed the world's first available e-Learning home robot. This has demonstrated the potential for robots to be used as a new educational media - robot-learning, referred to as 'r-Learning'. Robot technology is expected to become more interactive and user-friendly than computers. Also, robots can exhibit various forms of communication such as gestures, motions and facial expressions. This study compared the effects of non-computer based (NCB) media (using a book with audiotape) and Web-Based Instruction (WBI), with the effects of Home Robot-Assisted Learning (HRL) for children. The robot gestured and spoke in English, and children could touch its monitor if it did not recognize their voice command. Compared to other learning programs, the HRL was superior in promoting and improving children's concentration, interest, and academic achievement. In addition, the children felt that a home robot was friendlier than other types of instructional media. The HRL group had longer concentration spans than the other groups, and the p-value demonstrated a significant difference in concentration among the groups. In regard to the children's interest in learning, the HRL group showed the highest level of interest, the NCB group and the WBI group came next in order. Also, academic achievement was the highest in the HRL group, followed by the WBI group and the NCB group respectively. However, a significant difference was also found in the children's academic achievement among the groups. These results suggest that home robots are more effective as regards children's learning concentration, learning interest and academic achievement than other types of instructional media (such as: books with audiotape and WBI) for English as a foreign language.

• 한국컴퓨터정보학회논문지
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• 제24권1호
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• pp.247-255
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• 2019

In this paper, we propose the effect of project-based robot education program on the interest toward robots and the computational thinking of elementary school students. Software education is being actively carried out around the world in order to cultivate software talents in accordance with the 4th industrial revolution. As a result, the importance of robots in education has increased, and education using robots has been actively introduced. However, the activities of simply assembling and repeating robots in schools were not effective in enhancing elementary school students' interest toward robots and computational thinking. Therefore, it is necessary to overcome traditional teaching-learning methods and to develop robot education. So, in this study, the robot education program that introduces project-based learning was developed for improvement of interest toward robots and computational thinking of elementary school students. In order to verify the developed education program, 114 elementary six grade students were selected as research subjects and the traditional teaching-learning method and project-based learning were applied to the experimental and control group. As a result, project-based learning was more effective for elementary school students' interest toward robot than traditional teaching-learning method. In computing thinking, the experimental group showed a significant improvement, but there was no statistically significant difference in the post-test.

• International Journal of Control, Automation, and Systems
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• 제6권2호
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• pp.282-287
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• 2008

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.

• 디지털콘텐츠학회 논문지
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• 제19권1호
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• pp.35-41
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• 2018

강화학습기반 Q-learning 알고리즘은 이산적인 상태와 액션의 조합을 사용하여, 한 번에 하나의 행위에 대한 목표를 학습하는데 유용하다. 여러 액션을 학습하기 위해서는 행위 기반 아키텍처를 적용하고 적절한 행위 조절 방법을 사용하면 로봇으로 하여금 빠르고 신뢰성 있는 액션을 가능하게 할 수 있다. Q-learning은 인기 있는 강화학습 방법으로 단순하고, 수렴성이 있고 사전 훈련 환경에 영향을 덜 받는 특성(off-policy)으로 인해 로봇 학습에 많이 사용되고 있다. 본 논문에서는 Q-learning 알고리즘을 램프 로봇에 적용하여 복수 행위(사람인식, 책상의 물체 인식)를 학습시키는데 사용하였다. Q-learning의 학습속도(learning rate)는 복수 행위 학습 단계의 로봇 성능에 영향을 줄 수 있으므로 학습속도 변경을 통해 최적의 복수 행위 학습 모델을 제시한다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.518-521
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• 1995

In this paper, a learning control problem is formulated for cooperating multiple-robot manipulators with uncertain system parameters. The commonly held object is also assumed to be unknown and the multiple-robots themselfs experience uncertain operating conditions such as link parameters, viscous friction parameters, suctions, actuator bias, and etc. Under these conditions, the learning controllers designed for learning of uncertain parameters and robot control inputs for multiple-robot systems are shown to drive the multiple-robot manipulators to follow the desired Cartesian trajectory with the desired internal forces to the unknown object.

• 수산해양교육연구
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• 제27권2호
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• pp.430-440
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• 2015

The purpose of this study was to analyze student's metacognition interaction based on a robot lesson. For this research as an analytical metacognition interaction tool was utilized. The results of this study revealed that, first, elementary school students had more metacognition interaction in middle learning levels but middle school students had more in the low learning level. Second, in the low learning level, middle school students revised the initiated goal strategy of the robot lesson. Third, in all learning levels, students showed much diagnosis and assesment metacognition interaction in the robot lesson. According to this study's results, the robot lesson has a positive effect in facilitating diagnosis meta cognition for processing of task performance. These results could provide effective cues and information on how to improve the robot lesson.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1459-1466
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• 2018

Robots which detect human and recognize action are important factors for human interaction, and many researches have been conducted. Recently, deep learning technology has developed and learning based robot's technology is a major research area. These studies require a database to learn and evaluate for intelligent human perception. In this paper, we propose a multi-modal sensor-based image database condition considering the security task by analyzing the image database to detect the person in the outdoor environment and to recognize the behavior during the running of the robot.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.47-49
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• 2022

Robot navigation has seen a major improvement since the the rediscovery of the potential of Artificial Intelligence (AI) and the attention it has garnered in research circles. A notable achievement in the area was Deep Learning (DL) application in computer vision with outstanding daily life applications such as face-recognition, object detection, and more. However, robotics in general still depend on human inputs in certain areas such as localization, navigation, etc. In this paper, we propose a study case of robot navigation based on deep reinforcement technology. We look into the benefits of switching from traditional ROS-based navigation algorithms towards machine learning approaches and methods. We describe the state-of-the-art technology by introducing the concepts of Reinforcement Learning (RL), Deep Learning (DL) and DRL before before focusing on visual navigation based on DRL. The case study preludes further real life deployment in which mobile navigational agent learns to navigate unbeknownst areas.