Alle Publikationen
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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.
2006
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(2006) : Multimodal Human-Robot Interaction Framework for a Personal Robot: The 15th IEEE International Symposium on Robot and Human Interactive Communication, 2006: RO-MAN 2006 ; 6-8 Sept. 2006, University of Hertfordshire, Hatfield, United Kingdom ; proceedings: RO-MAN 2006: The 15th IEEE International Symposium on Robot and Human Interactive Communication: Hatfield: 9/6/2006 - 9/8/2006. IEEE International Symposium on Robot and Human Interactive Communication; IEEE Ro-Man: Piscataway, NJ: IEEE, S. 39-44
Abstract: This paper presents a framework for multimodal human-robot interaction. The proposed framework is being implemented in a personal robot called Maggie, developed at RoboticsLab of the University Carlos III of Madrid for social interaction research. The control architecture of this personal robot is a hybrid control architecture called AD (automatic-deliberative) that incorporates an emotion control system (ECS). Maggie's main goal is to interact establish a peer-to-peer relationship with humans. To achieve this goal, a set of human-robot interaction skills are developed based on the proposed framework. The human-robot interaction skills imply tactile, visual, remote voice and sound modes. The multi-modal fusion and synchronization are also presented in this paper.
2004
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(2004) : Evaluation of supervisory vs. peer-peer interaction with human-robot teams In: Sprague, Ralph H.: Proceedings of the 37th Annual Hawaii International Conference on System Sciences: Abstracts and CD-ROM of full papers : 5-8 January, 2004, Big Island, Hawaii: 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the: Big Island, HI, USA: 1/8/2004 - 1/8/2004. Annual Hawaii International Conference on System Sciences; IEEE Computer Society: Los Alamitos, Calif: IEEE Computer Society Press
Abstract: We submit that the most interesting and fruitful human-robot interaction (HRI) may be possible when the robot is able to interact with the human as a true team member, rather than a tool. However, the benefits of shared control can all too easily be overshadowed by challenges inherent to blending human and robot initiative. The most important requirements for peer-peer interaction are system trust and ability to predict system behavior. The human must be able to understand the reason for and effects of robot initiative. These requirements can only be met through careful application of human factors principles and usability testing to determine how users interact with the system. This paper discusses the recent human participant usability testing, which took our current implementation to task using a search and rescue scenario within a complex, real-world environment. The purpose of testing was to examine how human operators work with the robotic system at each level of autonomy, and how interaction with the robot should be structured to enable situation awareness and task completion. Analyses revealed that our architecture equally supported situation awareness and target detection by novices and experts, although experienced users were more likely to have more performance expectations of the interface. Results also had implications regarding the ability of participants to effectively utilize the collaborative workspace and, most importantly, their ability to understand and willingness to accept robot initiative.
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