Recently, education game contents with advanced mobile and Web technology have been widely researched. However, considering of interaction involved for children, it is necessary user friendly interfaces and interesting interactions. In addition, it is important to design to encourage the user’s learning motivation as a way to the effect of educational game contents. Therefore, in this study, we designed and implemented the educational game contents system using inter-working between KINECT camera, PC, and android platform based humanoid robot. The designed system consisted of KINECT system for capturing hand gestures, game contents system for playing games, and android humanoid robot system for interacting with users. This system can lead to interests and willingness of users through handy interaction such as hand gestures and interesting quiz game contents. To improve effectiveness and userability, we plant to advance designed educational game contents system that can be operated on a smart TV system including a built-in camera as a further study.

최근 국내에서는 로봇에 대한 일반인들의 관심이 높아지고 있다. 로봇은 사람과 유사한 모습과 기능을 가진 기계, 또는 무엇인가 스스로 작업하는 능력을 가진 기계를 말한다. 본 논문에서는 지능형 휴머노이드 로봇을 이용한 보드게임을 설계 개발 하고자 한다. 제안하는 주사위 게임은 휴머노이드 로봇에서 주사위를 던지는 모션을 마이컴 임베디드 시스템에서 랜덤한 동작으로 제어하는 프로그램을 작성 하여 실행 시켜 보았다. 주사위 게임의 모션은 왼쪽던지기, 오른쪽던지기, 정면던지기 동작으로 구성한다. 이를 통하여 로봇 게임을 통한 유아 및 노인들의 인지 훈련에 도움을 주고자 한다.

정보통신기술의 발달과 함께 학습자에게 재미와 흥미를 통한 즐거운 교육환경을 제공하고자 다양한 방법이 시도 되고 있다. 에듀테인먼트와 게임기반학습 등에서 게임이나 로봇과 같은 기술을 교육에 활용하는 것은 좋은 예이다. 본 연구에서는 휴머노이드 로봇의 율동 생성을 위한 사용자 데이터 수집과 분석을 통한 지능형 율동 교육 시스템을 제안한다. 이를 위하여 사용자는 음악을 선택하고, 선택한 음악에 따라 율동 정보를 입력한다. 이러한 사용자의 로봇 활용 데이터는 분석을 통하여 지능화된 서비스를 위한 패턴의 역할을 한다. 분석 결과는 빈도에 기반을 두며, 과거 정보가 부족한 경우 FFT 유사도 비교 방법을 적용하였다. 제안하는 방법은 유치원 아이들을 대상으로 하는 실험을 통하여 유효함을 확인하였다.

This paper proposes combination of a cognitive agent architecture named Soar (State, operator, and result) and ROS (Robot Operating System), which can be a basic framework for a robot agent to interact and cope with its environment more intelligently and appropriately. The proposed Soar-ROS human-robot interaction (HRI) agent understands a set of human’s commands by voice recognition and chooses to properly react to the command according to the symbol detected by image recognition, implemented on a humanoid robot. The robotic agent is allowed to refuse to follow an inappropriate command like “go” after it has seen the symbol ‘X’ which represents that an abnormal or immoral situation has occurred. This simple but meaningful HRI task is successfully experimented on the proposed Soar-ROS platform with a small humanoid robot, which implies that extending the present hybrid platform to artificial moral agent is possible.

The main goal of e-learning systems is just-in-time knowledge acquisition. Rule-based elearning systems, however, suffer from the mesa effect and the cold start problem, which both result in low user acceptance. E-learning systems suffer a further drawback in rendering the implementation of a natural interface in humanoids difficult. To address these concerns, even exceptional questions of the learner must be answerable. This paper aims to propose a method that can understand the learner’s verbal cues and then intelligently explore additional domains of knowledge based on crowd data sources such as Wikipedia and social media, ultimately allowing for better answers in real-time. A prototype system was implemented using the NAO platform.

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.

Humanoid robot is the most intimate robot platform suitable for human interaction and services. Biped walking is its basic locomotion method, which is performed with combination of joint actuator’s rotations in the lower extremity. The present work employs humanoid robot simulator and numerical optimization method to generate optimal joint trajectories for biped walking. The simulator is developed with Matlab based on the robot structure constructed with the Denavit-Hartenberg (DH) convention. Particle swarm optimization method minimizes the cost function for biped walking associated with performance index such as altitude trajectory of clearance foot and stability index concerning zero moment point (ZMP) trajectory. In this paper, instead of checking whether ZMP’s position is inside the stable region or not, reference ZMP trajectory is approximately configured with feature points by which piece-wise linear trajectory can be drawn, and difference of reference ZMP and actual one at each sampling time is added to the cost function. The optimized joint trajectories realize three phases of stable gait including initial, periodic, and final steps. For validation of the proposed approach, a small-sized humanoid robot named DARwIn-OP is commanded to walk with the optimized joint trajectories, and the walking result is successful.

Nowadays, research on human-robot interaction has been getting increasing attention. In the research field of human-robot interaction, speech signal processing in particular is the source of much interest. In this paper, we report a speaker localization system with six microphones for a humanoid robot called MAHRU from KIST and propose a time delay of arrival (TDOA)-based feature matrix with its algorithm based on the minimum sum of absolute errors (MSAE) for sound source localization. The TDOA-based feature matrix is defined as a simple database matrix calculated from pairs of microphones installed on a humanoid robot. The proposed method, using the TDOA-based feature matrix and its algorithm based on MSAE, effortlessly localizes a sound source without any requirement for calculating approximate nonlinear equations. To verify the solid performance of our speaker localization system for a humanoid robot, we present various experimental results for the speech sources at all directions within 5 m distance and the height divided into three parts.

Abstract Many researchers are studying on humanoid robots in all over the world. However the humanoid robots are still limited in doing works like picking up objects on the ground or moving rapidly. In this study, a humanoid robot based on the wheel-driving was developed. It can operate with a human working area keeping the stability. Also, the developed robot can take up the object on the floor since it has knee(1DoF) and waist(3DoF), and do service quickly and steadily. The hardware and software structure and algorithms of the developed robot, SEROPI are introduced in this paper.

We present the synergy effect of humanoid robot walking down on a slope and support vector machines in this paper. The biped robot architecture is highly suitable for the working in the human environment due to its advantages in obstacle avoidance and ability to be employed as human substitutes. But the complex dynamics in the robot and ground makes robot control difficult. The trajectory of the zero moment point (ZMP) in a biped walking robot is an important criterion used for the balance of the walking robots. The ZMP trajectory as dynamic stability of motion will be handled by support vector machines (SVM). Three kinds of kernels are also employed, and each result from these kernels is compared to one another.