A. and ;. , Application sur l'iCub réel 1 %En utilisant une modelisation : 2 [alphaTraj,type

]. =. ,

=. , 5 %En utilisant le maximum de vraisemblance : 6 [alphaTraj,type

, 7 %En utilisant la moyenne des parametres de modulation du temps calcules lors

A. , Application sur l'iCub réel Dans cette expérience, le robot récupère 10 trajectoires de démonstrations par primitive de mouvement, toutes fournies par un même utilisateur. Les données enregistrées correspondent à la position cartésienne de la main gauche du robot ainsi que les forces et moments qu'elle reçoit lors de ces mouvements

, A.5.1 Trois actions simples, avec informations sur les forces et les couples Les données sont récupérées à l'aide de la fonction du sous-dossier "used_functions" retrieveRealDataWithoutOrientation.m. Les paramètres de sorties de cette fonction correspondent à trois objets (un par ProMP) qui contiennent toutes les informations requises afin d'apprendre les ProMPs

, les informations des forces et des couples sont filtrées à l'aide d'une fonction Matlab appelée envelope.m 49 : pour chaque trajectoire traj et sa sous-matrice M = F

M. ,

, Ce script est organisé de la même manière que ceux présentés dans les Sections A.2 et 4. En lançant ce script, les données récupérées sont d'abord représentées. Puis, les ProMPs sont apprises et affichées, comme présenté dans la Figure 4.8. Dans cette figure, les distributions sont visiblement superposées :-le long de toutes les trajectoires en ce qui concerne les informations des couples, Ces trois objets sont sauvegardés dans 'Data/realIcub.mat'

, Les informations de cette fonction peuvent être récupérées ici

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, Dans un premier temps, il s'agit de faire apprendre au robot des gestes par démonstration : un utilisateur prend le robot par le bras et lui fait réaliser les gestes à apprendre et ce, plusieurs fois. Le robot doit ensuite être capable de reproduire ces différents mouvements tout en les généralisant pour s'adapter au contexte. Pour cela, à l'aide de ses capteurs proprioceptifs, il interprète les signaux perçus pour comprendre le mouvement que lui fait réaliser l'utilisateur, afin d'en générer des similaires par la suite. Dans un second temps, le robot apprend à reconnaître l'intention de l'humain avec lequel il interagit et cela, à partir des gestes que ce dernier initie : il s'agit ensuite pour le robot de produire les gestes adaptés à la situation et correspondant aux attentes de l'utilisateur. Cela nécessite que le robot comprenne la gestuelle de l'utilisateur. Pour cela, différentes modalités perceptives ont été explorées. À l'aide de capteurs proprioceptifs, le robot ressent les gestes de l'utilisateur au travers de son propre corps : il s'agit alors d'interaction physique humain-robot. À l'aide de capteurs visuels, le robot interprète le mouvement de la tête de l'utilisateur. Enfin, à l'aide de capteurs externes, le robot reconnaît et prédit le mouvement corps entier de l'utilisateur, Résumé Cette thèse se situe à l'intersection de l'apprentissage automatique et de la robotique humanoïde, dans la thématique de l'interaction homme-robot, et dans le domaine de la cobotique

D. 'un-point-de-vue-méthodologique, Pour cela, deux approches ont été développées. La première est fondée sur la modélisation statistique de primitives de mouvements (correspondant aux gestes) : les ProMPs. La seconde,ajoute à la première du Deep Learning, par l'utilisation d'auto-encodeurs, afin de modéliser des gestes corps entier contenant beaucoup d'informations, tout en permettant une prédiction en temps réel mou. Lors de cette thèse, différents enjeux ont notamment été pris en compte pour la création et le développement de nos méthodes, nous venons de voir que les questions d'apprentissage et de reconnaissance de séries temporelles (les gestes) ont été centrales dans cette thèse

, Mots-clés: Prédiction, primitives de mouvements, apprentissage automatique, Interaction HommeRobot, cobotique