Shared control is a technique to provide interactive autonomy in a telerobotic task, replacing the requirement for pure teleoperation where the operator's intervention is unnecessary or even undesirable. In this thesis, a geometrically correct theory of shared control for teleoperation is developed using differential geometry. The autonomous function proposed is force control. In shared control, the workspace is commonly partitioned into a "position domain" and a "force domain". This computational process requires the use of a metric. In the context of manifolds, these are known as Riemannian metrics. The switching matrix is shown to be equivalent to a filter which embodies a Riemannian metric form. However, since the metric form is non-invariant, it is shown that the metric form must undergo a transformation if the measurement reference frame is moved. If the transformation is not made, then the switching matrix fails to produce correct results in the new measurement frame. Alternatively, the switching matrix can be viewed as a misinterpretation of a projection operator. Again, the projection operator needs to be transformed correctly if the measurement reference frame is moved. Many robot control architectures preclude the implementation of robust force control. However, a compliant device mounted between the robot wrist and the workpiece can be a good alternative in lieu of explicit force control. In this form of shared control, force and displacement are regulated by control of displacement only. The geometry of compliant devices is examined in the context of shared control and a geometrically correct scheme for shared control is derived. This scheme follows naturally from a theoretical analysis of stiffness and potential energy. This thesis unifies some recent results formulated for robotic hybrid position / force control under the modern framework of differential geometry and Lie groups.

Dans cette thèse, nous proposons d'intégrer la notion d'habitude comportementale au sein d'une architecture de contrôle robotique, et d'étudier son interaction avec les mécanismes générant le comportement planifié. Les architectures de contrôle robotiques permettent à ce dernier d'être utilisé efficacement dans le monde réel et au robot de rester réactif aux changements dans son environnement, tout en étant capable de prendre des décisions pour accomplir des buts à long terme (Kortenkamp et Simmons, 2008). Or, ces architectures sont rarement dotées de capacités d'apprentissage leur permettant d'intégrer les expériences précédentes du robot. En neurosciences et en psychologie, l'étude des différents types d'apprentissage montre pour que ces derniers sont une capacité essentielle pour adapter le comportement des mammifères à des contextes changeants, mais également pour exploiter au mieux les contextes stables (Dickinson, 1985). Ces apprentissages sont modélisés par des algorithmes d'apprentissage par renforcement direct et indirect (Sutton et Barto, 1998), combinés pour exploiter leurs propriétés au mieux en fonction du contexte (Daw et al., 2005). Nous montrons que l'architecture proposée, qui s'inspire de ces modèles du comportement, améliore la robustesse de la performance lors d'un changement de contexte dans une tâche simulée. Si aucune des méthodes de combinaison évaluées ne se démarque des autres, elles permettent d'identifier les contraintes sur le processus de planification. Enfin, l'extension de l'étude de notre architecture à deux tâches (dont l'une sur robot réel) confirme que la combinaison permet l'amélioration de l'apprentissage du robot. / In this work, we study how the notion of behavioral habit, inspired from the study of biology, can benefit to robots. Robot control architectures allow the robot to be able to plan to reach long term goals while staying reactive to events happening in the environment (Kortenkamp et Simmons, 2008). However, these architectures are rarely provided with learning capabilities that would allow them to acquire knowledge from experience. On the other hand, learning has been shown as an essential abiilty for behavioral adaptation in mammals. It permits flexible adaptation to new contexts but also efficient behavior in known contexts (Dickinson, 1985). The learning mechanisms are modeled as model-based (planning) and model-free (habitual) reinforcement learning algorithms (Sutton et Barto, 1998) which are combined into a global model of behavior (Daw et al., 2005). We proposed a robotic control architecture that take inspiration from this model of behavior and embed the two kinds of algorithms, and studied its performance in a robotic simulated task. None of the several methods for combining the algorithm we studied gave satisfying results, however, it allowed to identify some properties required for the planning process in a robotic task. We extended our study to two other tasks (one being on a real robot) and confirmed that combining the algorithms improves learning of the robot's behavior.