"When humans interact, there are many avenues of physical communication available ranging from vocal to physical gestures. In our past observations, when humans collaborate on manipulation tasks in shared workspaces there is often minimal to no verbal or physical communication, yet the collaboration is still fluid with minimal interferences between partners. However, when humans perform similar tasks in the presence of a robot collaborator, manipulation can be clumsy, disconnected, or simply not human-like. The focus of this work is to leverage our observations of human-human interaction in a robot's motion planner in order to facilitate more safe, efficient, and human-like collaborative manipulation in shared workspaces. We first present an approach to formulating the cost function for a motion planner intended for human-robot collaboration such that robot motions are both safe and efficient. To achieve this, we propose two factors to consider in the cost function for the robot's motion planner: (1) Avoidance of the workspace previously-occupied by the human, so robot motion is safe as possible, and (2) Consistency of the robot's motion, so that the motion is predictable as possible for the human and they can perform their task without focusing undue attention on the robot. Our experiments in simulation and a human-robot workspace sharing study compare a cost function that uses only the first factor and a combined cost that uses both factors vs. a baseline method that is perfectly consistent but does not account for the human's previous motion. We find using either cost function we outperform the baseline method in terms of task success rate without degrading the task completion time. The best task success rate is achieved with the cost function that includes both the avoidance and consistency terms. Next, we present an approach to human-attention aware robot motion generation which attempts to convey intent of the robot's task to its collaborator. We capture human attention through the combined use of a wearable eye-tracker and motion capture system. Since human attention isn't static, we present a method of generating a motion policy that can be queried online. Finally, we show preliminary tests of this method."

Cette thèse traite du problème de planification de mouvement pour objets documentés. La difficulté du problème réside dans le couplage d’un problème symbolique et d’un problème géométrique. Les approches habituelles combinent la planification de tâche et la planification de mouvement. Elles sont complexes à implémenter et coûteuse en temps de calcul. Notre approche se différencie sur trois aspects. Le premier aspect est un cadre théorique modélisant les mouvements admissibles du robot et des objets. Ce modèle théorique utilise des contraintes pour lier tâche symbolique et chemins géométriques accomplissant cette tâche. Un graphe de contrainte permet de modéliser les règles de manipulation. Un algorithme de planification utilisant ce graphe est proposé. Le deuxième aspect est la gestion de chemin contraint. Dans le cadre de la manipulation, un définition abstraite sous forme de contrainte numérique est nécessaire. Un critère de continuité pour les méthodes de type Newton-Raphson est proposé pour assurer la continuité de trajectoire dans des sous-variétés. Le dernier aspect est la documentation des objets. Certaines informations, facile à définir pour l’être humain, accélère grandement la recherche d’une solution. Cette documentation, spécifique à chaque objet et préhenseur, est utilisée pour générer un graphe de contrainte, facilitant ainsi la spécification et la résolution du problème. / This thesis tackles the manipulation planning for documented objects. The difficulty of the problem is the coupling of a symbolic and a geometrical problem. Classical approaches combine task and motion planning. They are hard to implement and time consuming. This approach is different on three aspects. The first aspect is a theoretical framework to model admissible motions of the robot and objects. This model uses constraints to link symbolic task and motions achieving such task. A graph of constraint models the manipulation rules. A planning algorithm using this graph is proposed. The second aspect is the handling of constrained motion. In manipulation planning, an abstract definition of numerical constraint is necessary. A continuity criterion for Newton-Raphson methods is proposed to ensure the continuity of trajectories in sub-manifolds. The last aspect is object documentation. Some information, easy to define for human beings, greatly speeds up the search. This documentation, specific to each object and end-effector, is used to generate a graph of constraint, easing the problem specification and resolution.