Contribution to complex visual information processing and autonomous knowledge extraction : application to autonomous robotics
type: Dataset
description: 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
landing page: https://theses.hal.science/tel-00802399
identifier: NNT: 2012PEST1100
keyword: apprentissage-artificiel
keyword: artificial-cognitive-system
keyword: autonomous-robotics
keyword: cognitive-system
keyword: extraction-des-connaissances
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: informations-visuelles
keyword: knowledge-extraction
keyword: machine-learning
keyword: robotique-autonome
keyword: systeme-cognitif
keyword: systeme-cognitif-artificiel
keyword: theses
keyword: visual-information
issued: 2026-05-12T06:48:39.525751
modified: 2026-05-12T06:48:39.525758
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Contribution to complex visual information processing and autonomous knowledge extraction : application to autonomous robotics
type: Distribution
format: HTML
access URL: https://theses.hal.science/tel-00802399
issued: 2026-05-12T06:48:39.625901
modified: 2026-05-12T06:48:39.492826