Modulation de Mouvements de Tête pour l'Analyse Multimodale d'un Environnement Inconnu. (Head Turning Modulation for the Multimodal Analysis of Unknown Environments)

L'exploration d'un environnement inconnu par un robot mobile est un vaste domaine de recherche visant a comprendre et implementer des modeles d'exploration efficaces, rapides et pertinents. Cependant, depuis les annees 80, l'exploration ne s'est plus contentee de la seule determination de la topographie d'un espace : a la composante spatiale a ete couplee une composante semantique du monde explore. En effet, en addition aux caracteristiques physiques de l'environnement — murs, obstacles, chemins empruntables ou non, entrees et sorties — permettant au robot de se creer une representation interne du monde grâce a laquelle il peut s'y deplacer, existent des caracteristiques dynamiques telles que l'apparition d'evenements audiovisuels. Ces evenements sont d'une grande importance en cela qu'ils peuvent moduler le comportement du robot en fonction de leur localisation dans l'espace — aspect topographique — et de l'information qu'ils portent — aspect semantique. Bien qu'impredictibles par nature (puisque l'environnement est inconnu) tous ces evenements ne sont pas d'egale importance : certains peuvent porter une information utile au robot et a sa tâche d'exploration, d'autres non. Suivant les travaux sur les motivations intrinseques a explorer un environnement inconnu et puisant son inspiration de phenomenes neurologiques, ce travail de these a consiste en l'elaboration du modele Head Turning Modulation (HTM) visant a donner a un robot dote de mouvements de tete la capacite de determiner l'importance relative de l'apparition d'un evenement audiovisuel dans un environnement inconnu en cours d'exploration. Cette importance a ete formalisee sous la forme de la notion de Congruence s'inspirant principalement (i) de l'entropie de Shannon, (ii) du phenomene de Mismatch Negativity et (iii) de la Reverse Hierarchy Theory. Le modele HTM, cree dans le cadre du projet europeen Two!Ears, est un paradigme d'apprentissage base sur (i) une auto-supervision (le robot decide lorsqu'il est necessaire d'apprendre ou non), (ii) une contrainte de temps reel (le robot apprend et reagit aussitot que des donnees sont percues), et (iii) une absence de donnees a priori sur l'environnement (il n'existe pas de verite a apprendre, seulement la realite percue de l'environnement a explorer). Ce modele, integre a l’ensemble du framework Two!Ears, a ete entierement porte sur un robot mobile pourvu d'une vision binoculaire et d'une audition binaurale. Le modele HTM couple ainsi une approche montante traditionnelle d’analyse des signaux perceptifs (extractions de caracteristiques, reconnaissance visuelle ou auditive, etc.) a une approche descendante permettant, via la generation d’une action motrice, de comprendre et interpreter l’environnement audiovisuel du robot. Cette approche bottom-up/top-down active est ainsi exploitee pour moduler les mouvements de tete d’un robot humanoide et etudier l'impact de la Congruence sur ces mouvements. Le systeme a ete evalue via des simulations realistes, ainsi que dans des conditions reelles, sur les deux plateformes robotiques du projet Two!Ears.

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