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Theses

Visuomotor learning, implications for sensorimotor development and emergence of social interactions

Abstract : This thesis try to bring answers to the question of the sensorimotor learning and development in the context of human-robot interactions in a real non-constrained environment. To achieve this goal we defend in this thesis the fact that human being interacts through intentional and conscious strategy but also depends of the property of their low level motor system, their body, and of their sensorimotor learning loops, allowing these to facilitate implicitly this interaction. We try to answer these questions through the study of the sensorimotor loops in humans, and through the study of the development of these properties in infants. First, we study here the properties of our robot « Tino », which is a prototype of an humanoid hydraulic robot, unique in France and which is the main experimental platform used in this thesis. We analyses in this thesis the property of this robot and made analogies with the human motor system properties that are implied in the interaction between human, the environment and other humans. We show how certain of these properties could be used to simplify tasks for the control system. We study finally the limit of this analogy and of the exploitation of these properties. After this part we study in this thesis the modeling of low level motor loop and of the properties of the human muscular system in order to capture the main interesting properties for interactions. We propose an implementation on robot and analyses the properties of this control system in simulation and on the robotic platform Tino. Then, we propose a bio-inspired and developmental neural architecture that is able to learn visuomotor association with babbling exploration of the environment. We show with this model implemented on the robot Tino that we can observe the emergence of implicit social interaction through the sensorimotor loops, such are imitation and pointing gesture. But this model use a simple associative learning which is able to construct an “actions repertoire” but is unable to react to the environment and humans finely. To solve this problem we have developed, through two simulations, a learning model based on reinforcement learning to allow our system to produce coherent trajectory in order to act in an environment. We applied this in a simulated task of grasping and moving an object on a table. We show then the analogies between this model and historic experiments about the impact of intention on the motor actions and trajectories in humans Finally we study in this thesis the dynamic of interactions and the interest of bringing oscillatory neural network in these sensorimotor architectures. To this end we propose in this thesis several oscillatory models able to learn and to adapt in the context of bio-inspired architecture that learn in interaction with a real environment.
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Nils Beaussé. Visuomotor learning, implications for sensorimotor development and emergence of social interactions. Computer Vision and Pattern Recognition [cs.CV]. Université de Cergy Pontoise, 2019. English. ⟨NNT : 2019CERG1051⟩. ⟨tel-02945304⟩

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