In daily human interactions, spatial reasoning occupies an important place. In this paper we present a situation assessment reasoner that generates relevant symbolic information from the geometry of the environment with respect to relations between objects and human capabilities. The role of SPARK (SPAtial Reasoning and Knowledge) component is to permanently maintain a state of the world in order to provide a basis for the robot to plan, to act, to react and to interact. More precisely, we describe here the way the system manages the hypotheses to be able to handle such knowledge in a flexible manner. Equipped with such capabilities, a robot that will interact with humans should be able to extract, compute or infer these relations and capabilities in order to communicate and interact efficiently in a natural way. To illustrate our work, we will explain how the robot is able to manage and update agents beliefs and pass Sally-Anne test. This work is part of a broader effort to develop a complete decisional framework for human-robot interactive task achievement.

Ce travail de these a eu pour objectif de definir et mettre en oeuvre l'architecture decisionnelle d'un robot realisant une tâche en collaboration avec un homme pour atteindre un but commun. Un certain nombre de fonctionnalites existaient deja ou ont ete developpees conjointement avec ce travail au sein de l'equipe. Ce travail a d'abord consiste en l'etude puis a la formalisation des differentes capacites necessaires. Il s'est traduit concretement par l'approfondissement de certains des modules fonctionnels existants par l'auteur ou par d'autres membres de l'equipe en lien etroit avec l'auteur. La premiere contribution principale de l'auteur a consiste a developper une couche de controle de haut niveau qui a permis l'integration et la mise en oeuvre des differentes capacites du robot decoupee en 3 activites : la construction et la mise a jour de l'etat du monde ; la gestion des buts et des plans de haut niveau ; l'execution et le suivi des mouvements de manipulation. La deuxieme contribution principale a consiste a ameliorer les raisonnement geometriques et temporelles pour d'abord permettre au robot de mieux suivre l'evolution de l'etat du monde puis lui donner la capacite a inferer quand l'homme a des croyances distinctes de celle du robot.

Robots should be capable of interacting in a cooperative and adaptive manner with their human counterparts in open-ended tasks that can change in real-time. An important aspect of the robot behavior will be the ability to acquire new knowledge of the cooperative tasks by observing and interacting with humans. The current research addresses this challenge. We present results from a cooperative human-robot interaction system that has been specifically developed for portability between different humanoid platforms, by abstraction layers at the perceptual and motor interfaces. In the perceptual domain, the resulting system is demonstrated to learn to recognize objects and to recognize actions as sequences of perceptual primitives, and to transfer this learning, and recognition, between different robotic platforms. For execution, composite actions and plans are shown to be learnt on one robot and executed successfully on a different one. Most importantly, the system provides the ability to link actions into shared plans, that form the basis of human-robot cooperation, applying principles from human cognitive development to the domain of robot cognitive systems.

We have designed and implemented new spatio-temporal reasoning skills for a cognitive robot, which explicitly reasons about human beliefs on object positions. It enables the robot to build symbolic models reflecting each agent's perspective on the world. Using these models, the robot has a better understanding of what humans say and do, and is able to reason on what human should know to achieve a given goal. These new capabilities are also demonstrated experimentally.

If robots are to cooperate with humans in an increasingly human-like manner, then significant progress must be made in their abilities to observe and learn to perform novel goal directed actions in a flexible and adaptive manner. The current research addresses this challenge. In CHRIS.I [1], we developed a platform-independent perceptual system that learns from observation to recognize human actions in a way which abstracted from the specifics of the robotic platform, learning actions including “put X on Y” and “take X”. In the current research, we extend this system from action perception to execution, consistent with current developmental research in human understanding of goal directed action and teleological reasoning. We demonstrate the platform independence with experiments on three different robots. In Experiments 1 and 2 we complete our previous study of perception of actions “put” and “take” demonstrating how the system learns to execute these same actions, along with new related actions “cover” and “uncover” based on the composition of action primitives “grasp X” and “release X at Y”. Significantly, these compositional action execution specifications learned on one iCub robot are then executed on another, based on the abstraction layer of motor primitives. Experiment 3 further validates the platform-independence of the system, as a new action that is learned on the iCub in Lyon is then executed on the Jido robot in Toulouse. In Experiment 4 we extended the definition of action perception to include the notion of agency, again inspired by developmental studies of agency attribution, exploiting the Kinect motion capture system for tracking human motion. Finally in Experiment 5 we demonstrate how the combined representation of action in terms of perception and execution provides the basis for imitation. This provides the basis for an open ended cooperation capability where new actions can be learned and integrated into shared plans for cooperation. Part of the novelty of this research is the robots' use of spoken language understanding and visual perception to generate action representations in a platform independent manner based on physical state changes. This provides a flexible capability for goal-directed action imitation.

Human Robot cooperation brings several challenges to autonomous robotics such as adoption of a pro-active behavior, situation analysis and goal generation, intention explanation and adaptation and choice of a correct behavior toward the human. In this paper, we describe a decisional architecture for human robot interaction which addresses some of these challenges. The description will be centered on a planner called HATP (Human Aware Task Planner) and its capacity to synthesize plans for human robot teamwork that respect social conventions and that favour acceptable collaborative behaviors. We provide an overall description of HATP and its integration in a complete implemented architecture. We also illustrate the performance of our system on a daily life scenario achieved by a robot in interaction with a human partner in a realistic setup.

. An essential aspect of human robot interaction is proactive robot behavior particularly in situations where the robot is able to determine by itself if, how and when it can intervene and help. This is certainly valuable since it permits the user to be freed from the burden of permanently monitoring the robot and choosing the command that should be issued to the robot. In this work we present an architecture for proactive robot behavior. Its main features involve the ability to select high level goals based on scenario recognition. The goals are then refined by a specific planner that is able to determine if the robot can contribute to the goal achievement and finally a human aware supervision system that allows the robot to share the human activity thanks to its ability to achieve task cooperatively. The paper describes the overall system and its implementation on a realistic testbed.