Motion planning & feedback control of bi-steerable robots (an approach based on differential flatness)

Cette thèse s'attaque aux problèmes de planification et d'exécution de trajectoires pour un robot mobile à deux essieux de direction: nous appelons voiture Bi-guidable un véhicule capable d'orienter ses roues arrières en fonction de l'angle de direction avant. Les équations différentielles décrivant le système de commande de ce robot posent de nouveaux problèmes en planification et contrôle en robotique mobile. Cette thèse montre d'abord que la voiture Bi-guidable appartient à la classe des systèmes dits différentiellement plats, pour laquelle il est possible de trouver des solutions efficaces. Nous déterminons ensuite les transformations plates de la voiture Bi-guidable, principale difficulté à la synthèse de ces solutions. Nous validons enfin ces résultats théoriques par des expérimentations sur une voiture Bi-guidable réelle.GRENOBLE1-BU Sciences (384212103) / SudocSudocFranceF

A Self-Organized Internal Models Architecture for Coding Sensory–Motor Schemes

Cognitive robotics research draws inspiration from theories and models on cognition, as conceived by neuroscience or cognitive psychology, to investigate biologically plausible computational models in artificial agents. In this field, the theoretical framework of Grounded Cognition provides epistemological and methodological grounds for the computational modeling of cognition. It has been stressed in the literature that simulation, prediction, and multi-modal integration are key aspects of cognition and that computational architectures capable of putting them into play in a biologically plausible way are a necessity. Research in this direction has brought extensive empirical evidence, suggesting that Internal Models are suitable mechanisms for sensory–motor integration. However, current Internal Models architectures show several drawbacks, mainly due to the lack of a unified substrate allowing for a true sensory–motor integration space, enabling flexible and scalable ways to model cognition under the embodiment hypothesis constraints. We propose the Self-Organized Internal Models Architecture (SOIMA), a computational cognitive architecture coded by means of a network of self-organized maps, implementing coupled internal models that allow modeling multi-modal sensory–motor schemes. Our approach addresses integrally the issues of current implementations of Internal Models. We discuss the design and features of the architecture, and provide empirical results on a humanoid robot that demonstrate the benefits and potentialities of the SOIMA concept for studying cognition in artificial agents.Peer Reviewe

The Analysis of Synonymy and Antonymy in Discourse Relations: An Interpretable Modeling Approach

The idea that discourse relations are interpreted both by explicit content and by shared knowledge between producer and interpreter is pervasive in discourse and linguistic studies. How much weight should be ascribed in this process to the lexical semantics of the arguments is, however, uncertain. We propose a computational approach to analyze contrast and concession relations in the PDTB corpus. Our work sheds light on the question of how much lexical relations contribute to the signaling of such explicit and implicit relations, as well as on the contribution of different parts of speech to these semantic relations. This study contributes to bridging the gap between corpus and computational linguistics by proposing transparent and explainable computational models of discourse relations based on the synonymy and antonymy of their arguments