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.

No abstract

The main activity of social robots is to interact with people. In order to do that, the robot must be able to understand what the user is saying or doing. Typically, this capability consists of pre-programmed behaviors or is acquired through controlled learning processes, which are executed before the social interaction begins. This paper presents a software architecture that enables a robot to learn poses in a similar way as people do. That is, hearing its teacher's explanations and acquiring new knowledge in real time. The architecture leans on two main components: an RGB-D (Red-, Green-, Blue- Depth) -based visual system, which gathers the user examples, and an Automatic Speech Recognition (ASR) system, which processes the speech describing those examples. The robot is able to naturally learn the poses the teacher is showing to it by maintaining a natural interaction with the teacher. We evaluate our system with 24 users who teach the robot a predetermined set of poses. The experimental results show that, with a few training examples, the system reaches high accuracy and robustness. This method shows how to combine data from the visual and auditory systems for the acquisition of new knowledge in a natural manner. Such a natural way of training enables robots to learn from users, even if they are not experts in robotics.

No abstract