The Web 2.0 concept brought up the trends of growing rapidly interactive websites which were based on the internet characteristics such as upload creations, member votes, and even the the exchange of the gifts. In a brief summery, all the incredible activities are growing mainly under the encouragement of the diversity and accumulation of the users¡¦ engagements. This study is based on the reference to the user experience and interface activities of kinds of popular Web2.0 websites. Also, through the 480 questionnaires samples and researches, this study tried to find the possible motivative activities and knowledge sharing models according to the users¡¦ motives, behaviors and self-gratification. Furthermore, this study also focused on the evaluation to the users¡¦ self-gratification after the extrinsic motivate rewards which were published to the general users. The study results appeared that when the virtual community platforms adopt different kinds of motivate rewards; the positive influence is always existed between the user motive and the outside motivation rewards. To particularly point out, the extrinsic rewards of praising in public and community usefulness has the highest correlation through the Pearson correlation analysis. Also, the outside motivate activities will affect the interactive behaviors between the users accordingly. On the personal characteristics observation, the ages and educations have significant differences to the users¡¦ motive. And the user experience of virtual community websites such as the quantities of accounts, frequency to visit the websites, and the average time of staying on the website have significant differences to user motives. And on the perspective of motivation and user behaviors, the results basically are similar with the theory of uses and gratification. There is above 50% samples pointed out that trust and identification are the important key factors on all the websites activities between users. Especially the human-human interaction is the most popular one during the research. To conclude all the research efforts, this study made a prior research on the new internet activities and provided some ideas on the correlations of the motive, behaviors and gratification on users¡¦ side. Through the concrete results hope may have the study bases for further market researches or much practical reference to the website business management.

Un robot d'assistance sociale (SAR) est destiné à engager les gens dans une interaction située comme la surveillance de l'exercice physique, la réadaptation neuropsychologique ou l'entraînement cognitif. Alors que les comportements interactifs de ces systèmes sont généralement scriptés, nous discutons ici du cadre d’apprentissage de comportements interactifs multimodaux qui est proposé par le projet SOMBRERO.Dans notre travail, nous avons utilisé l'apprentissage par démonstration afin de fournir au robot des compétences nécessaires pour effectuer des tâches collaboratives avec des partenaires humains. Il y a trois étapes principales d'apprentissage de l'interaction par démonstration: (1) recueillir des comportements interactifs représentatifs démontrés par des tuteurs humains; (2) construire des modèles des comportements observés tout en tenant compte des connaissances a priori (modèle de tâche et d'utilisateur, etc.); et ensuite (3) fournir au robot-cible des contrôleurs de gestes appropriés pour exécuter les comportements souhaités.Les modèles multimodaux HRI (Human-Robot Interaction) sont fortement inspirés des interactions humain-humain (HHI). Le transfert des comportements HHI aux modèles HRI se heurte à plusieurs problèmes: (1) adapter les comportements humains aux capacités interactives du robot en ce qui concerne ses limitations physiques et ses capacités de perception, d'action et de raisonnement limitées; (2) les changements drastiques des comportements des partenaires humains face aux robots ou aux agents virtuels; (3) la modélisation des comportements interactifs conjoints; (4) la validation des comportements robotiques par les partenaires humains jusqu'à ce qu'ils soient perçus comme adéquats et significatifs.Dans cette thèse, nous étudions et faisons des progrès sur ces quatre défis. En particulier, nous traitons les deux premiers problèmes (transfert de HHI vers HRI) en adaptant le scénario et en utilisant la téléopération immersive. En outre, nous utilisons des réseaux neuronaux récurrents pour modéliser les comportements interactifs multimodaux (tels que le discours, le regard, les mouvements de bras, les mouvements de la tête, les canaux). Ces techniques récentes surpassent les méthodes traditionnelles (Hidden Markov Model, Dynamic Bayesian Network, etc.) en termes de précision et de coordination inter-modalités. A la fin de cette thèse, nous évaluons une première version de robot autonome équipé des modèles construits par apprentissage. / A socially assistive robot (SAR) is meant to engage people into situated interaction such as monitoring physical exercise, neuropsychological rehabilitation or cognitive training. While the interactive behavioral policies of such systems are mainly hand-scripted, we discuss here key features of the training of multimodal interactive behaviors in the framework of the SOMBRERO project.In our work, we used learning by demonstration in order to provide the robot with adequate skills for performing collaborative tasks in human centered environments. There are three main steps of learning interaction by demonstration: we should (1) collect representative interactive behaviors from human coaches; (2) build comprehensive models of these overt behaviors while taking into account a priori knowledge (task and user model, etc.); and then (3) provide the target robot with appropriate gesture controllers to execute the desired behaviors.Multimodal HRI (Human-Robot Interaction) models are mostly inspired by Human-Human interaction (HHI) behaviors. Transferring HHI behaviors to HRI models faces several issues: (1) adapting the human behaviors to the robot’s interactive capabilities with regards to its physical limitations and impoverished perception, action and reasoning capabilities; (2) the drastic changes of human partner behaviors in front of robots or virtual agents; (3) the modeling of joint interactive behaviors; (4) the validation of the robotic behaviors by human partners until they are perceived as adequate and meaningful.In this thesis, we study and make progress over those four challenges. In particular, we solve the two first issues (transfer from HHI to HRI) by adapting the scenario and using immersive teleoperation. In addition, we use Recurrent Neural Networks to model multimodal interactive behaviors (such as speech, gaze, arm movements, head motion, backchannels) that surpass traditional methods (Hidden Markov Model, Dynamic Bayesian Network, etc.) in both accuracy and coordination between the modalities. We also build and evaluate a proof-of-concept autonomous robot to perform the tasks.