• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.250-252
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• 2004

서비스 로봇은 일상생활에서 사람들의 업무를 보조한다. 이때, 효과적인 서비스를 위해서는 사람과 로봇 사이의 상호작용이 매우 중요하다 대화는 사람과 로봇이 보다 유연하고 풍부한 의사전달을 하는데 도움을 준다. 전통적인 로봇 연구에서는 명령과 같은 간단한 질의 둥을 처리하는 것이 의사소통의 전부였으나, 실제 사람들 사이의 대화에서는 배경 지식이나 대화의 문맥 둥에 의해 중요한 정보가 대화에서 생략되기도 한다. 이런 상황은 여러 불확실성을 포함하게 되는데 대화의 문맥이나 불확실성을 다루는 것이 필요하다. 본 논문에서는 '상호-주도' 방식을 통해 사람이 쓰는 일상 대화를 계층적 베이지안 네트워크를 이용하여 처리하는 방법을 제안한다. 실제 로봇의 시뮬레이션 환경은 제안하는 방법의 유용함을 보여주었다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.362-365
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• 2006

본 논문에서는 인간 로봇 상호 작용을 위해 정확한 스켈레톤 특징점을 추출하는 강인한 추출기를 설계한다. 제안된 특징점 추출기는 인간의 움직임 정보로부터 얻어진 색상, 윤곽선, 시간차 정보 및 가상 신체 모델을 이용하여 정확한 특징점 위치를 찾아낸다. 또한 특징점 추출에 소요되는 탐색 시간을 줄이기 위해 격자박스를 이용한 원형 탐색 기법을 도입하였다. 최종적으로 기법의 우수성을 확인하기 위해 다양한 동작의 특징점 추출 실험을 수행하였다.

• The Journal of Korean Institute for Practical Engineering Education
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• v.1 no.1
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• pp.91-98
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• 2009

In approach of human-robot interaction, it is importance task in future robot industry to make to robot recognize, express, coping the emotions. The purpose of this study was to examination the effects unconscious positive and negative emotion of information processing of motor program. 13 participants(male=11, female=2) viewed smile-face picture and angry-face picture priming at 10ms level, and then performanced button press, button press and one tennis ball hitting, and button press and two tennis ball hitting task. The results appeared that positive emotion triggered more fast RT than negative emotion in planning complex motor program. Possible explanations for the performance differences depended on emotion are discussed and future research directions were provided.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1427-1434
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• 2021

In this paper, we propose a psychological state estimation process that computes children's attention and tension in response to training stimuli. Joint attention was adopted as the training stimulus required for behavioral intervention, and the Discrete trial training (DTT) technique was applied as the training protocol. Three types of training stimulation contents are composed to check the user's attention and tension level and provided mounted on a character-shaped tabletop robot. Then, the gaze response to the user's training stimulus is estimated with the vision-based head pose recognition and geometrical calculation model, and the nervous system response is analyzed using the PPG and GSR bio-signals using heart rate variability(HRV) and histogram techniques. Through experiments using robots, it was confirmed that the psychological response of users to training contents on joint attention could be quantified.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.243-249
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• 2015

In direct vehicle teleoperation, a human operator drives a vehicle at a distance through a pair of master and slave device. However, if there is time delay, it is difficult to remotely drive the vehicle due to slow response. In order to address this problem, we introduced a novel methodology of shared vehicle teleoperation using a virtual driving interface. The methodology was developed with four components: 1) virtual driving environment, 2) interface for virtual driving environment, 3) path generator based on virtual driving trajectory, 4) path following controller. Experimental results showed the effectiveness of the proposed approach in simple and cluttered driving environment as well. In the experiments, we compared two sampling methods, fixed sampling time and user defined instant, and finally merged method showed best remote driving performance in term of completion time and number of collision.

• Journal of the Korea Computer Graphics Society
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• v.23 no.3
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• pp.1-8
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• 2017

In this paper, we introduce a technique to retarget human motion data to the humanoid body in a constrained environment. We assume that the given motion data includes detailed interactions such as holding the object by hand or avoiding obstacles. In addition, we assume that the humanoid joint structure is different from the human joint structure, and the shape of the surrounding environment is different from that at the time of the original motion. Under such a condition, it is also difficult to preserve the context of the interaction shown in the original motion data, if the retargeting technique that considers only the change of the body shape. Our approach is to separate the problem into two smaller problems and solve them independently. One is to retarget motion data to a new skeleton, and the other is to preserve the context of interactions. We first retarget the given human motion data to the target humanoid body ignoring the interaction with the environment. Then, we precisely deform the shape of the environmental model to match with the humanoid motion so that the original interaction is reproduced. Finally, we set spatial constraints between the humanoid body and the environmental model, and restore the environmental model to the original shape. To demonstrate the usefulness of our method, we conducted an experiment by using the Boston Dynamic's Atlas robot. We expected that out method can help the humanoid motion tracking problem in the future.

• Science of Emotion and Sensibility
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• v.26 no.3
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• pp.29-40
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• 2023

The term and concept of "immortality" has garnered a considerable amount of attention worldwide. However, research on this topic is lacking, and the question of when the mind of a deceased individual survives death has yet to be answered. This research investigates whether morality and mind perception of the dead correlate with immortality. Study 1 measures the perceived immortality of people, who were good or evil in life. The results show that the perceived morality is related with the perceived immortality. Moreover, participants indicated the extent to which each person had maintained a degree of morality and agency/experience of the mind. Therefore, morality and mind perception toward a person are related to perceived immortality. In Study 2, participants were asked to read three essays on robots (good, evil, and nonmoral), and had to indicate the extent to which each robot maintains a degree of immortality, morality, and agency/experience of the mind. The results show that good spirits of a robot are related to higher scores of mind perception toward the robot, resulting in increasing tendency of perceived immortality. These results provide implications that the morality of humans and robots can mediate the relationship between mind perception and immortality. This work extends on previous research on the determinants of social robots for overcoming difficulties in human-robot interaction.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.70-75
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• 2016

Nowadays, there are many research for recognizing face of people using the machine vision. the machine vision is classification and analysis technology using machine that has sight such as human eyes. In this paper, we propose algorithm for classifying human face using this machine vision system. This algorithm consist of Convolutional Neural Network and cascade face detector. And using this algorithm, we classified the face of subjects. For training the face classification algorithm, 2,000, 3,000, and 4,000 images of each subject are used. Training iteration of Convolutional Neural Network had 10 and 20. Then we classified the images. In this paper, about 6,000 images was classified for effectiveness. And we implement the system that can classify the face of subjects in realtime using USB camera.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1458-1463
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• 2004

Tracking is one of the most important pre-required task for many application such as human-computer interaction through gesture and face recognition, motion analysis, visual servoing, augment reality, industrial assembly and robot obstacle avoidance. Recently, 3D information of object is required in realtime for many aforementioned applications. 3D tracking is difficult problem to solve because during the image formation process of the camera, explicit 3D information about objects in the scene is lost. Recently, many vision system use stereo camera especially for 3D tracking. The 3D feature based tracking(3DFBT) which is on of the 3D tracking system using stereo vision have many advantage compare to other tracking methods. If we assumed the correspondence problem which is one of the subproblem of 3DFBT is solved, the accuracy of tracking depends on the accuracy of camera calibration. However, The existing calibration method based on accurate camera model so that modelling error and weakness to lens distortion are embedded. Therefore, this thesis proposes 3D feature based tracking method using SVM which is used to solve reconstruction problem.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.313-321
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• 2017

One of the most frequently performed tasks in human-robot interaction (HRI), intelligent vehicles, and security systems is face related applications such as face recognition, facial expression recognition, driver state monitoring, and gaze estimation. In these applications, accurate head pose estimation is an important issue. However, conventional methods have been lacking in accuracy, robustness or processing speed in practical use. In this paper, we propose a novel method for estimating head pose with a monocular camera. The proposed algorithm is based on a deep neural network for multi-task learning using a small grayscale image. This network jointly detects multi-view faces and estimates head pose in hard environmental conditions such as illumination change and large pose change. The proposed framework quantitatively and qualitatively outperforms the state-of-the-art method with an average head pose mean error of less than $4.5^{\circ}$ in real-time.