No abstract

For service robots to be able to enter a multi-human office environment, it is important to find a group of human users' social patterns and then to provide a proper service to them in time. Usually, human users' social patterns are represented in terms of nonverbal social signals. In this paper, a new integrated approach on recognizing multi-human social signals is proposed. Specifically, the nonverbal social signals are detected by a laser range finder and a RGB-D camera and are processed to find the multi-human (spatial) social patterns. Those recognized patterns are then applied to human-to-human, human-to-robot or multi-human-to-robot interactive formation. Experimental results shows that our robot successfully recognizes the aforementioned users' social patterns followed by appropriate services.

This paper presents our new intelligent interactive robot, which is constructed to eagerly provide multi-functional services in an office environment. In order to endow a full interactive capability of our robots for realizing so-called human-robot interaction (HRI) , we propose sensor fusion based human detection and tracking system and human pose estimation to deal with a number of situations which may take place in the office environment. Not only by these perceptions, human interact with the robot also by some natural way, such as touching the interface screen and talking with the robot through microphone. Finally, the effectiveness of the proposed work is tested and validated by some of experiments.

In this paper, we propose a framework to perceive the level of intimacy in dyadic human interactions from both robot perspective and first-person perspective. First of all, for catching the insight of human interaction with three different degrees of intimacy persons, namely normal, familiar and close, we have done a preliminary user study of social interaction. Next, from the field of social science and our study, we design four types of social interaction features, consisting of proxemics, non-verbal, verbal, and temporal features to categorize the intimacy level from observations, but including three of them reaches the best result. Finally, to validate our work here, several experiments have been conducted, and the results show that the framework perceives the aforementioned intimacy level with accuracy up to 86.11% in average.