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).

An intuitive and robust user recognition system is at the key of a natural interaction between a social robot and its users. The gender of a new user can be guessed without explicitly asking it of her, which can then be used to personalize the interaction flow. In this LBR, a novel algorithm is used to estimate the gender of a person based on its morphological shape. More specifically, the vertical outline of the breast of the user is used to estimate his or her gender, based on similar shapes seen during training.

User simultaneous detection and tracking is an issue at the core of human–robot interaction (HRI). Several methods exist and give good results; many use image processing techniques on images provided by the camera. The increasing presence in mobile robots of range-imaging cameras (such as structured light devices as Microsoft Kinects) allows us to develop image processing on depth maps. In this article, a fast and lightweight algorithm is presented for the detection and tracking of 3D clusters thanks to classic 2D techniques such as edge detection and connected components applied to the depth maps. The recognition of clusters is made using their 2D shape. An algorithm for the compression of depth maps has been specifically developed, allowing the distribution of the whole processing among several computers. The algorithm is then applied to a mobile robot for chasing an object selected by the user. The algorithm is coupled with laser-based tracking to make up for the narrow field of view of the range-imaging camera. The workload created by the method is light enough to enable its use even with processors with limited capabilities. Extensive experimental results are given for verifying the usefulness of the proposed method.

Advances in voice recognition have made possible applications in robotics controlled by voice only. However, user input through gestures and robot output gestures both create a more vivid interaction experience. In this article, we present an aloud reading application offering all these interaction methods for the HRI-research robot Maggie. It gives us a testbed for user studies investigating the effect of these additional interaction methods.

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 Blue - Depth) 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.