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2014
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
2012
Okita, S. Y.; Ng-Thow-Hing, V.; Sarvadevabhatla, R. K. (2012) : Captain may I? Proxemics study examining factors that influence distance between humanoid robots, children, and adults, during human-robot interaction: 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI): Boston, MA: IEEE, S. 203-204

Abstract: This proxemics study examines whether the physical distance between robots and humans differ based on the following factors: 1) age: children vs. adults, 2) who initiates the approach: humans approaching the robot vs. robot approaching humans, 3) prompting: verbal invitation vs. non-verbal gesture (e.g., beckoning), and 4) informing: announcement vs. permission vs. nothing. Results showed that both verbal and non-verbal prompting had significant influence on physical distance. Physiological data is also used to detect the appropriate timing of approach for a more natural and comfortable interaction.

Keywords: Angemessen(heit) (von Technik), Human robot interaction, Humanoid Robots, human-robot interaction, Humans, ieee xplore, image sensors, Measurement, nonverbal gesture, Pediatrics, physiological data, physiology, proxemics study, Robot sensing systems, robot vision, USA Councils, verbal invitation, Young Children

2014

2012

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