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2014
Iguchi, S.; Takenouchi, H.; Tokumaru, M. (2014) : Sympathy expression model for the bystander robot in group communication: 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM): Puerto Princesa, Philippines: IEEE, S. 1-6

DOI: https://doi.org/10.1109/HNICEM.2014.7016192

Abstract: In this paper, we propose a sympathy expression model for a bystander robot that honors the concept of moral emotion. Therefore, we pay attention to the robot that is in a bystander position, which is unrelated to the communication between participants. We propose a sympathy expression model that lets a bystander robot learn the emotional display of others and enables cooperative expressiveness. This model allows the appropriate expressiveness affecting communication of a robot in the position of a bystander. To test it, we assume the interaction of three robots with the emotion generation model using the neural network. Further, we inspect the movement of this model by using a psychology model. As a result, we confirmed the appropriate actions of this model.

Keywords: bystander robot, control engineering computing, emotion generation model, Ethics, group communication, Humanoid Robots, ieee xplore, Moral & Ethik, moral emotion, neural nets, neural network, Observers, PSYCHOLOGY, psychology model, Robot kinematics, Robot sensing systems, Sympathy, sympathy expression model, Vectors
Zaraki, A.; Giuliani, M.; Dehkordi, M. B.; Mazzei, D.; D’ursi, A.; De Rossi, D. (2014) : An RGB-D based social behavior interpretation system for a humanoid social robot: 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM): Tehran, Iran: IEEE, S. 185-190

DOI: https://doi.org/10.1109/ICRoM.2014.6990898

Abstract: Humanoid social robots that interact with people need to be capable of interpreting the social behavior of their interaction partners in order to respond in a socially appropriate way. In this paper, we present a social behavior interpretation system that enables a humanoid robot to recognize human social behavior by analyzing communicative signals. The system receives the constructed RGB-D scene from a Kinect sensor, extracts information about body gesture and head pose from the scene using Microsoft Kinect SDK, and recognizes eight human social behaviors using a Hidden Markov Model (HMM). We trained the eight-state HMM with a corpus of 35 recorded human-human interaction scenes. The evaluation of the system shows a weighted average recognition rate of 81% for all states.

Keywords: Accuracy, Angemessen(heit) (von Technik), body gesture, eight-state HMM, Feature extraction, Gesture recognition, head pose, Hidden Markov model, Hidden Markov models, human social behavior, human-human interaction scenes, humanlike robot, humanoid social robot, human-robot interaction, ieee xplore, image colour analysis, image sensors, Joints, Kinect sensor, Microsoft Kinect SDK, pose estimation, RGB-D based social behavior interpretation system, RGB-D scene, Robot sensing systems, robot vision, social behavior interpretation system, social behavior recognition, Vectors, weighted average recognition rate

2014

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