The work accomplished in this thesis concerns development of an autonomous machine cognition system. The proposed solution reposes on the assumption that it is the curiosity which motivates a cognitive system to acquire new knowledge. Further, two distinct kinds of curiosity are identified in conformity to human cognitive system. On this I build a two level cognitive architecture. I identify its lower level with the perceptual saliency mechanism, while the higher level performs knowledge acquisition from observation and interaction with the environment. This thesis brings the following contribution: A) Investigation of the state of the art in autonomous knowledge acquisition. B) Realization of a lower cognitive level in the ensemble of the mentioned system, which is realizing the perceptual curiosity mechanism through a novel fast, real-world robust algorithm for salient object detection and learning. C) Realization of a higher cognitive level through a general framework for knowledge acquisition from observation and interaction with the environment including humans. Based on the epistemic curiosity, the high-level cognitive system enables a machine (e.g. a robot) to be itself the actor of its learning. An important consequence of this system is the possibility to confer high level multimodal cognitive capabilities to robots to increase their autonomy in real-world environment (human environment). D) Realization of the strategy proposed in the context of autonomous robotics. The studies and experimental validations done had confirmed notably that our approach allows increasing the autonomy of robots in real-world environment

Le travail effectué lors de cette thèse concerne le développement d'un système cognitif artificiel autonome. La solution proposée repose sur l'hypothèse que la curiosité est une source de motivation d'un système cognitif dans le processus d'acquisition des nouvelles connaissances. En outre, deux types distincts de curiosité ont été identifiés conformément au système cognitif humain. Sur ce principe, une architecture cognitive à deux niveaux a été proposée. Le bas-niveau repose sur le principe de la saillance perceptive, tandis que le haut-niveau réalise l'acquisition des connaissances par l'observation et l'interaction avec l'environnement. Cette thèse apporte les contributions suivantes : A) Un état de l'art sur l'acquisition autonome de connaissance. B) L'étude, la conception et la réalisation d'un système cognitif bas-niveau basé sur le principe de la curiosité perceptive. L'approche proposée repose sur la saillance visuelle réalisée grâce au développement d'un algorithme rapide et robuste permettant la détection et l'apprentissage d'objets saillants. C) La conception d'un système cognitif haut-niveau, basé sur une approche générique, permettant l'acquisition de connaissance à partir de l'observation et de l'interaction avec son environnent (y compris avec les êtres humains). Basé sur la curiosité épistémique, le système cognitif haut-niveau développé permet à une machine (par exemple un robot) de devenir l'acteur de son propre apprentissage. Une conséquence substantielle d'un tel système est la possibilité de conférer des capacités cognitives haut-niveau multimodales à des robots pour accroître leur autonomie dans un environnement réel (environnement humain). D) La mise en œuvre de la stratégie proposée dans le cadre de la robotique autonome. Les études et les validations expérimentales réalisées ont notamment confirmé que notre approche permet d'accroître l'autonomie des robots dans un environnement réel / The work accomplished in this thesis concerns development of an autonomous machine cognition system. The proposed solution reposes on the assumption that it is the curiosity which motivates a cognitive system to acquire new knowledge. Further, two distinct kinds of curiosity are identified in conformity to human cognitive system. On this I build a two level cognitive architecture. I identify its lower level with the perceptual saliency mechanism, while the higher level performs knowledge acquisition from observation and interaction with the environment. This thesis brings the following contribution: A) Investigation of the state of the art in autonomous knowledge acquisition. B) Realization of a lower cognitive level in the ensemble of the mentioned system, which is realizing the perceptual curiosity mechanism through a novel fast, real-world robust algorithm for salient object detection and learning. C) Realization of a higher cognitive level through a general framework for knowledge acquisition from observation and interaction with the environment including humans. Based on the epistemic curiosity, the high-level cognitive system enables a machine (e.g. a robot) to be itself the actor of its learning. An important consequence of this system is the possibility to confer high level multimodal cognitive capabilities to robots to increase their autonomy in real-world environment (human environment). D) Realization of the strategy proposed in the context of autonomous robotics. The studies and experimental validations done had confirmed notably that our approach allows increasing the autonomy of robots in real-world environment