An objective of natural Human-Robot Interaction (HRI) is to enable humans to communicate with robots in the same manner humans do between themselves. This includes the use of natural gestures to support and expand the information that is exchanged in the spoken language. To achieve that, robots need robust gesture recognition systems to detect the non-verbal information that is sent to them by the human gestures. Traditional gesture recognition systems highly depend on the light conditions and often require a training process before they can be used. We have integrated a low-cost commercial RGB-D (Red Green BlueDepth) sensor in a social robot to allow it to recognise dynamic gestures by tracking a skeleton model of the subject and coding the temporal signature of the gestures in a FSM (Finite State Machine). The vision system is independent of low light conditions and does not require a training process.

Edutainment robots are robots designed to participate in people's education and in their entertainment. One of the tasks of edutainment robots is to play with their human partners, but most of them offer a limited pool of games. Moreover, it is difficult to add new games to them. This lack of flexibility could shorten their life cycle. This paper presents a social robot on which several robotic games have been developed. Our robot uses a flexible and modular architecture that allows the creation of new skills by the composition of existing and simpler skills. With this architecture, the development of a new game mainly consists in the composition of the skills that are needed for this specific game. In this paper, we present the robot, its hardware and its software architecture, including its interaction capabilities. We also provide a detailed description of the development of five of the games the robot can play.

This LBR describes a novel method for user recognition in HRI, based on analyzing the peculiarities of users voices, and specially focused at being used in a robotic system. The method is inspired by acoustic fingerprinting techniques, and is made of two phases: a)enrollment in the system: the features of the user's voice are stored in files called voiceprints, b)searching phase: the features extracted in real time are compared with the voiceprints using a pattern matching method to obtain the most likely user (match). The audio samples are described thanks to features in three different signal domains: time, frequency, and time-frequency. Using the combination of these three domains has enabled significant increases in the accuracy of user identification compared to existing techniques. Several tests using an independent user voice database show that only half a second of user voice is enough to identify the speaker. The recognition is text-independent: users do not need to say a specific sentence (key-pass) to get identified for the robot.

No abstract

Robots are starting to be applied in areas which involve sharing space with humans. In particular, social robots and people will coexist closely because the former are intended to interact with the latter. In this context, it is crucial that robots are aware of the presence of people around them. Traditionally, people detection has been performed using a flow of twodimensional images. However, in nature, animals' sight perceives their surroundings using color and depth information. In this work, we present new people detectors that make use of the data provided by depth sensors and red-green-blue images to deal with the characteristics of human-robot interaction scenarios. These people detectors are based on previous works using two-dimensional images and existing people detectors from different areas. The disparity of the input and output data used by these types of algorithms usually complicates their integration into robot control architectures. We propose a common interface that can be used by any people detector, resulting in numerous advantages. Several people detectors using depth information and the common interface have been implemented and evaluated. The results show a great diversity among the different algorithms. Each one has a particular domain of use, which is reflected in the results. A clever combination of several algorithms appears as a promising solution to achieve a flexible, reliable people detector.