Skip to Main content Skip to Navigation

Modulation de Mouvements de Tête pour l'Analyse Multimodale d'un Environnement Inconnu

Abstract : The exploration of an unknown environement by a mobile robot is a vast research domain aiming at understanding and implementing efficient, fast and relevant exploration models. However, since the 80s, exploration is no longer restricted to the sole determination of the topography of a space: to the spatial component has been coupled a semantic one of the explored world. Indeed, in addition to the physical characteristics of the environment — walls, obstacles, usable paths or not, entrances and exits — allowing the robot to create its own internal representation of the world through which it can move in it, exist dynamic components such as the apparition of audiovisual events. These events are of high importance for they can modulate the robot's behavior through their location in space — topographic aspect — and the information they carry — semantic aspect. Although impredictible by nature (since the environment is unknown) all these events are not of equal importance: some carry valuable information for the robot's exploration task, some don't. Following the work on intrinsic motivations to explore an unknown environment, and being rooted in neurological phenomenons, this thesis work consisted in the elaboration of the Head Turning Modulation (HTM) model aiming at giving to a robot capable of head movements, the ability to determine the relative importance of the apparition of an audiovisual event. This "importance" has been formalized through the notion of "Congruence" which is mainly inspired from (i) Shannon's entropy, (ii) the Mismatch Negativity phenomenon, and (iii) the Reverse Hierarchy Theory. The HTM model, created within the Two!Ears european project, is a learning paradigm based on (i) an auto-supervision (the robot decides when it is necessary or not to learn), (ii) a real-time constraint (the robot learns and reacts as soon as data is perceived), and (iii) an absence of prior knowledge about the environment (there is no "truth" to learn, only the reality of the environment to explore). This model, integrated in the overal Two!Ears framework, has been entirely implemented in a mobile robot with binocular vision and binaural audition. The HTM model thus gather the traditional approach of ascending analysis of perceived signals (extraction of caracteristics, visual or audio recognition etc.) to a descending approach that enables, via motor actions generation in order to deal with perception deficiency (such as visual occlusion), to understand and interprete the audiovisual environment of the robot. This bottom-up/top-down active approach is then exploited to modulate the head movements of a humanoid robot and to study the impact of the Congruence on these movements. The system has been evaluated via realistic simulations, and in real conditions, on the two robotic platforms of the Two!Ears project.
Complete list of metadatas

Cited literature [272 references]  Display  Hide  Download
Contributor : Benjamin Cohen-Lhyver <>
Submitted on : Friday, September 28, 2018 - 9:33:11 PM
Last modification on : Monday, October 19, 2020 - 11:05:53 AM
Long-term archiving on: : Monday, December 31, 2018 - 11:09:20 AM


Files produced by the author(s)


  • HAL Id : tel-01883899, version 1


Benjamin Cohen-Lhyver. Modulation de Mouvements de Tête pour l'Analyse Multimodale d'un Environnement Inconnu. Robotique [cs.RO]. Université Pierre and Marie Curie, Paris VI; Ecole doctorale Sciences Mécaniques, Acoustique, Electronique et Robotique de Paris, 2017. Français. ⟨tel-01883899⟩



Record views


Files downloads