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Résumé

Ancrer l'interaction: Gestion des connaissances pour la robotique interactive Avec le développement de la robotique cognitive, le besoin d'outils avancés pour représenter, manipuler, raisonner sur les connaissances acquises par un robot a clairement été mis en avant. Mais stocker et manipuler des connaissances requiert tout d'abord d'éclaircir ce que l'on nomme connaissance pour un robot, et comment celle-ci peut-elle être représentée de manière intelligible pour une machine. Ce travail s'efforce dans un premier temps d'identifier de manière systématique les besoins en terme de représentation de connaissance des applications robotiques modernes, dans le contexte spécifique de la robotique de service et des interactions homme-robot. Nous proposons une typologie originale des caractéristiques souhaitables des systèmes de représentation des connaissances, appuyée sur un état de l'art détaillé des outils existants dans notre communauté. Dans un second temps, nous présentons en profondeur ORO, une instanciation particulière d'un système de représentation et manipulation des connaissances, conçu et implémenté durant la préparation de cette thèse. Nous détaillons le fonctionnement interne du système, ainsi que son intégration dans plusieurs architectures robotiques complètes. Un éclairage particulier est donné sur la modélisation de la prise de perspective dans le contexte de l'interaction, et de son interprétation en terme de théorie de l'esprit. La troisième partie de l'étude porte sur une application importante des systèmes de représentation des connaissances dans ce contexte de l'interaction homme-robot : le traitement du dialogue situé. Notre approche et les algorithmes qui amènent à l'ancrage interactif de la communication verbale non contrainte sont présentés, suivis de plusieurs expériences menées au Laboratoire d'Analyse et d'Architecture des Systèmes au CNRS à Toulouse, et au groupe Intelligent Autonomous System de l'université technique de Munich. Nous concluons cette thèse sur un certain nombre de considérations sur la viabilité et l'importance d'une gestion explicite des connaissances des agents, ainsi que par une réflexion sur les éléments encore manquant pour réaliser le programme d'une robotique "de niveau humain".

Abstract

With the rise of the so-called cognitive robotics, the need of advanced tools to store, manipulate, reason about the knowledge acquired by the robot has been made clear. But storing and manipulating knowledge requires first to understand what the knowledge itself means to the robot and how to represent it in a machine-processable way. This work strives first at providing a systematic study of the knowledge requirements of modern robotic applications in the context of service robotics and human-robot interaction. What are the expressiveness requirement for a robot? What are its needs in term of reasoning techniques? What are the requirement on the robot's knowledge processing structure induced by other cognitive functions like perception or decision making? We propose a novel typology of desirable features for knowledge representation systems supported by an extensive review of existing tools in our community. In a second part, the thesis presents in depth a particular instantiation of a knowledge representation and manipulation system called ORO, that has been designed and implemented during the preparation of the thesis. We elaborate on the inner working of this system, as well as its integration into several complete robot control stacks. A particular focus is given to the modelling of agent-dependent symbolic perspectives and their relations to theories of mind. The third part of the study is focused on the presentation of one important application of knowledge representation systems in the humanrobot interaction context: situated dialogue. Our approach and associated algorithms leading to the interactive grounding of unconstrained verbal communication are presented, followed by several experiments that have taken place both at the Laboratoire d'Analyse et d'Architecture des Systèmes at CNRS, Toulouse and at the Intelligent Autonomous System group at Munich Technical University. The thesis concludes on considerations regarding the viability and importance of an explicit management of the agent's knowledge, along with a reflection on the missing bricks in our research community on the way towards "human level robots".

Mots-Clés / Keywords

Abstract

On the 14th of October 2011, we performed for a general public audience (over 300 persons) a 18 min long live theatre play, acted by professional actor Xavier Brossard and the LAAS/CNRS PR2 robot. The play was created and directed by Nicolas Darrot, a mixed-media artist from Paris. The PR2 has been programmed in a 2-months course, reusing several software components developed at LAAS/CNRS, including the 3D environment for situation assessment SPARK, the ontology-based knowledge base ORO and the natural-language processor Dialogs. This video abstract presents the storyline of the play, and underlines some of the significant outcome for the human-robot interaction community. The full-length version of the video, along with downloads of the open-source components, is available from www.laas.fr/roboscopie.

Abstract

Over the last two years, the Modular OpenRobots Simulation Engine (MORSE) project1 went from a simple extension plugged on the Blender's Game Engine to a full-fledged simulation environment for academic robotics. Driven by the requirements of several of its developers, tools dedicated to Human-Robot interaction simulation have taken a prominent place in the project. This late breaking report discusses some of the recent additions in this domain, including the immersive experience provided by the integration of the Kinect device as input controller. We also give an overview of the experiences we plan to complete in the coming months.

Abstract

This paper presents how extraction, representation and use of symbolic knowledge from real-world perception and human-robot verbal and non-verbal in-teraction can actually enable a grounded and shared model of the world that is suitable for later high-level tasks such as dialogue understanding. We show how the anchoring process itself relies on the situated nature of human-robot interactions. We present an integrated approach, including a specialized symbolic knowledge representation system based on Description Logics, and case studies on several robotic platforms that demonstrate these cognitive capabilities.

Abstract

While key for human-robot interaction, natural language interpretation is a notoriously difficult task, especially because the interaction context is at the same time essential for dialogue understanding, difficult to build for machines, and depends on each speaker point of view. However, robots as embodied artifacts, can perceive their environment and interactors, and hence compute symbolic models from various perspectives. This allows in turn to build symbolic contexts for dialogues. In this paper, we introduce DIALOGS, a component for natural language interpretation that relies on these structured symbolic models of the world to ground verbal interaction.

Abstract

Interacting with a robot in a shared space requires not only a shared model (i.e., environment entities must be described with the same symbols), but also a model of mutual knowledge: both the robot and the human need to figure out what the other knows, sees or can do in the environment to appropriately behave. We present here our efforts to let the robot grounds verbal interaction with a human in a physical, situated environment. We propose a knowledge-oriented architecture, where perceptions from different point of views (from the robot itself, from the human, etc.) are turned into symbolic facts that are stored in different cognitive models and reused in a newly designed module for dialogue grounding.

Abstract

This paper presents how extraction, representation and use of symbolic knowledge from real-world perception and human-robot verbal and non-verbal interaction can actually enable a grounded and shared model of the world that is suitable for later high-level tasks like dialogue understanding, symbolic task planning or reactive supervision. We show how the anchoring process itself fundamentally relies on both the situated and embodied nature of human-robot interactions. We present an implementation, including a specialized symbolic knowledge representation system based on Description Logics, and experiments on several robotic platforms that demonstrate these cognitive capabilities.