Feel the beat: using cross-modal rhythm to integrate perception of objects, others, and self

For a robot to be capable of development, it must be able to explore its environment and learn from its experiences. It must find (or create) opportunities to experience the unfamiliar in ways that reveal properties valid beyond the immediate context. In this paper, we develop a novel method for using the rhythm of everyday actions as a basis for identifying the characteristic appearance and sounds associated with objects, people, and the robot itself. Our approach is to identify and segment groups of signals in individual modalities (sight, hearing, and proprioception) based on their rhythmic variation, then to identify and bind causally-related groups of signals across different modalities. By including proprioception as a modality, this cross-modal binding method applies to the robot itself, and we report a series of experiments in which the robot learns about the characteristics of its own body

A mechatronic approach to supernormal auditory localisation

Remote audio perception is a fundamental requirement for telepresence and teleoperation in applications that range from work in hostile environments to security and entertainment. The following paper presents the use of a mechatronic system to test the efficacy of audio for telepresence. It describes work to determine whether the use of supernormal inter-aural distance is a valid means of approaching an enhanced method of hearing for telepresence. The particular audio variable investigated is the azimuth angle of error and the construction of a dedicated mechatronic test rig is reported and the results obtained. The paper concludes by observing that the combination of the mechatronic system and supernormal audition does enhance the ability to localise sound sources and that further work in this area is justified

Vision-Guided Robot Hearing

International audienceNatural human-robot interaction (HRI) in complex and unpredictable environments is important with many potential applicatons. While vision-based HRI has been thoroughly investigated, robot hearing and audio-based HRI are emerging research topics in robotics. In typical real-world scenarios, humans are at some distance from the robot and hence the sensory (microphone) data are strongly impaired by background noise, reverberations and competing auditory sources. In this context, the detection and localization of speakers plays a key role that enables several tasks, such as improving the signal-to-noise ratio for speech recognition, speaker recognition, speaker tracking, etc. In this paper we address the problem of how to detect and localize people that are both seen and heard. We introduce a hybrid deterministic/probabilistic model. The deterministic component allows us to map 3D visual data onto an 1D auditory space. The probabilistic component of the model enables the visual features to guide the grouping of the auditory features in order to form audiovisual (AV) objects. The proposed model and the associated algorithms are implemented in real-time (17 FPS) using a stereoscopic camera pair and two microphones embedded into the head of the humanoid robot NAO. We perform experiments with (i)~synthetic data, (ii)~publicly available data gathered with an audiovisual robotic head, and (iii)~data acquired using the NAO robot. The results validate the approach and are an encouragement to investigate how vision and hearing could be further combined for robust HRI