Apprentissage ouvert de representations et de fonctionnalites en robotique : anayse, modeles et implementationReport as inadecuate




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1 LAAS - Laboratoire d-analyse et d-architecture des systèmes Toulouse

Abstract : Autonomous acquisition of representations and functionalities by a machine address several theoretical questions. Today-s autonomous robots are developed around a set of functionalities. Their representations of the world are deduced from the analysis and modeling of a given problem, and are initially given by the developers. This limits the learning capabilities of robots. In this thesis, we propose an approach and a system able to build open-ended representation and functionalities. This system learns through its experimentations of the environment and aims to augment a value function. Its objective consists in acting to reactivate the representations it has already learnt to connote positively. An analysis of the generalization capabilities to produce appropriate actions enable define a minimal set of properties needed by such a system. The open-ended representation system is composed of a network of homogeneous processing units and is based on position coding. The meaning of a processing unit depends on its position in the global network. This representation system presents similarities with the principle of numeration by position. A representation is given by a set of active units. This system is implemented in a suite of software called NeuSter, which is able to simulate million unit networks with billions of connections on heterogeneous clusters of POSIX machines. The first results permit to validate the constraints provided by the analysis. Such a system is able to learn hierarchically and without supervision, within a unique network, various feature extractors such as borders and lines, corners, end stops, view dependant faces, directions of motion, optical flow patterns such as rotation, expansions, contractions, and phonemes. NeuSter learns online using the data given by its sensors. It has been tested on mobile robots for learning and tracking of objects.

Résumé : L-acquisition autonome de representations et de fonctionnalites en robotique pose de nombreux problemes theoriques. Aujourd-hui, les systemes robotiques autonomes sont concus autour d-un ensemble de fonctionnalites. Leurs representations du monde sont issues de l-analyse d-un probleme et d-une modelisation prealablement donnees par les concepteurs. Cette approche limite les capacites d-apprentissage. Nous proposons dans cette these un systeme ouvert de representations et de fonctionnalites. Ce systeme apprend en experimentant son environnement et est guide par l-augmentation d-une fonction de valeur. L-objectif du systeme consiste a agir sur son environnement pour reactiver les representations dont il avait appris une connotation positive. Une analyse de la capacite a generaliser la production d-actions appropriees pour ces reactivations conduit a definir un ensemble de proprietes necessaires pour un tel systeme. Le systeme de representation est constitue d-un reseau d-unites de traitement semblables et utilise un codage par position. Le sens de l-etat d-une unite depend de sa position dans le reseau. Ce systeme de representation possede des similitudes avec le principe de numeration par position. Une representation correspond a l-activation d-un ensemble d-unites. Ce systeme a ete implemente dans une suite logicielle appelee NeuSter qui permet de simuler des reseaux de plusieurs millions d-unites et milliard de connexions sur des grappes heterogenes de machines POSIX. Les premiers resultats permettent de valider les contraintes deduites de l-analyse. Un tel systeme permet d-apprendre dans un meme reseau, de facon hierarchique et non supervisee, des detecteurs de bords et de traits, de coins, de terminaisons de traits, de visages, de directions de mouvement, de rotations, d-expansions, et de phonemes. NeuSter apprend en ligne en utilisant uniquement les donnees de ses capteurs. Il a ete teste sur des robots mobiles pour l-apprentissage et le suivi d-objets.

Keywords : Systemes ouverts Apprentissage Categorisation Auto-organisation Apprentissage de representations Apprentissage de fonctionnalites Reseaux de neurones Boucle sensori-motrice Open-ended systems Machine learning Categorization Self-organization Learning representations Functionality learning Online learning Self-reinforcement learning Neural Networks Sensory-motor loop





Author: Williams Paquier -

Source: https://hal.archives-ouvertes.fr/



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