Alle Publikationen
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2016
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(2016) : Robot reading human gaze: Why eye tracking is better than head tracking for human-robot collaboration: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Daejeon, South Korea: IEEE Robotics & Automation Society, S. 5048-5054
DOI: https://doi.org/10.1109/IROS.2016.7759741 Abstract: Robots are at the position to become our everyday companions in the near future. Still, many hurdles need to be cleared to achieve this goal. One of them is the fact that robots are still not able to perceive some important communication cues naturally used by humans, e.g. gaze. In the recent past, eye gaze in robot perception was substituted by its proxy, head orientation. Such an approach is still adopted in many applications today. In this paper we introduce performance improvements to an eye tracking system we previously developed and use it to explore if this approximation is appropriate. More precisely, we compare the impact of the use of eye- or head-based gaze estimation in a human robot interaction experiment with the iCub robot and naïve subjects. We find that the possibility to exploit the richer information carried by eye gaze has a significant impact on the interaction. As a result, our eye tracking system allows for a more efficient human-robot collaboration than a comparable head tracking approach, according to both quantitative measures and subjective evaluation by the human participants.
Keywords: Angemessen(heit) (von Technik), Cameras, communication cues, EYE GAZE, eye tracking system, eye-based gaze estimation, gaze tracking, Head, head orientation, head tracking, head-based gaze estimation, Human robot interaction, Humanoid Robots, human-robot collaboration, human-robot interaction, iCub robot, ieee xplore, Magnetic heads, performance improvements, pose estimation, quantitative measures, robot human gaze reading, Robot kinematics, robot perception, robot proxy, robot vision, Visualization 2014
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(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
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(2012) : Vision-based attention control system for socially interactive robots: 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication: Paris, France: IEEE, S. 496-502
DOI: https://doi.org/10.1109/ROMAN.2012.6343800 Abstract: A social robot needs to attract the attention of a target human and shift it from his/her current focus to what is sought by the robot. The robot should recognize the current target’s attention level to smoothly perform this attention control. In this paper, we propose a vision-based system to detect the level of attention or willingness of the target person towards the robot and to control his/her attention. The system estimates the attention level from rich visual cues of human’s face and head. Then, by timing target’s attention to determine the appropriate attention level, it generates aware signals and makes eye contact with the target. Finally, the robot shifts the target’s attention to an intended direction. The experimental results reveal that the proposed system is effective in controlling the target’s attention.
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(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
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