Artificial Cognition for Social Human-Robot Interaction: An Implementation

© 2017 The Authors Human–Robot Interaction challenges Artificial Intelligence in many regards: dynamic, partially unknown environments that were not originally designed for robots; a broad variety of situations with rich semantics to understand and interpret; physical interactions with humans that requires fine, low-latency yet socially acceptable control strategies; natural and multi-modal communication which mandates common-sense knowledge and the representation of possibly divergent mental models. This article is an attempt to characterise these challenges and to exhibit a set of key decisional issues that need to be addressed for a cognitive robot to successfully share space and tasks with a human. We identify first the needed individual and collaborative cognitive skills: geometric reasoning and situation assessment based on perspective-taking and affordance analysis; acquisition and representation of knowledge models for multiple agents (humans and robots, with their specificities); situated, natural and multi-modal dialogue; human-aware task planning; human–robot joint task achievement. The article discusses each of these abilities, presents working implementations, and shows how they combine in a coherent and original deliberative architecture for human–robot interaction. Supported by experimental results, we eventually show how explicit knowledge management, both symbolic and geometric, proves to be instrumental to richer and more natural human–robot interactions by pushing for pervasive, human-level semantics within the robot's deliberative system

Supervision and Motion Planning for a Mobile Manipulator Interacting with Humans

Human Robot collaborative task achievement requires adapted tools and algorithms for both decision making and motion computation. The human presence as well as its behavior must be considered and actively monitored at the desicional level for the robot to produce a legible set of actions. Additionally , having a human within the robot range of action introduces security constraints as well as confort consideration which must be taken into account at the motion planning level. This paper presents a robotic architecture adapted to human robot interaction and focuses on two tools: a human aware manipulation planner and a supervision system dedicated to collaborative task achievement

ロボットナビゲーションにおける人間への意図伝達に関する研究

早大学位記番号:新7327早稲田大

Interaction décisionnelle homme-robot : la planification de tâches au service de la sociabilité du robot

Cette thèse aborde la problématique du robot assistant et plus particulièrement les aspects décisionnels qui y sont liés. Un robot assistant est amené à interargir avec des hommes ce qui impose qu'il doit intègrer dans son processus décisionnel de haut-niveau les contraintes sociales inhérentes à un comportement acceptable par son(ses) partenaire(s) humain(s). Cette thèse propose une approche permettant de décrire de manière générique diverses règles sociales qui sont introduites dans le processus de planification du robot afin d'évaluer la qualité sociale des plans solutions et de ne retenir que le(s) plus approprié(s). Cette thèse décrit également l'implémentation de cette approche sous la forme d'un planificateur de tâches appelé HATP (Human Aware Task Planner en anglais). Enfin, cette thèse propose une validation de l'approche développée grâce à un scénario de simulation et à une mise en oeuvre sur un robot réel.This thesis is about assistive robot challenge et more especially about decisional issues linked to it. An assistive robot has to interact with humans which implies that it must integrate in its high-level decisional process some social constraints inherent in a behaviour acceptable by its human partner(s). This thesis proposes an approach allowing to describe, in a generic way, a set of social rules introduced in the robot planning process in order to evaluate social quality of solution plans and, thus, keep the most appropriate. This thesis also describes implementation of this approach in the form of a task planner called HATP (Human Aware Task Planner). Finally, this thesis proposes a validation of the developed approach with a simulation scenario and an implementation on a real robot

Safe and Efficient Robot Action Choice Using Human Intent Prediction in Physically-Shared Space Environments.

Emerging robotic systems are capable of autonomously planning and executing well-defined tasks, particularly when the environment can be accurately modeled. Robots supporting human space exploration must be able to safely interact with human astronaut companions during intravehicular and extravehicular activities. Given a shared workspace, efficiency can be gained by leveraging robotic awareness of its human companion. This dissertation presents a modular architecture that allows a human and robotic manipulator to efficiently complete independent sets of tasks in a shared physical workspace without the robot requiring oversight or situational awareness from its human companion. We propose that a robot requires four capabilities to act safely and optimally with awareness of its companion: sense the environment and the human within it; translate sensor data into a form useful for decision-making; use this data to predict the human’s future intent; and then use this information to inform its action-choice based also on the robot’s goals and safety constraints. We first present a series of human subject experiments demonstrating that human intent can help a robot predict and avoid conflict, and that sharing the workspace need not degrade human performance so long as the manipulator does not distract or introduce conflict. We describe an architecture that relies on Markov Decision Processes (MDPs) to support robot decision-making. A key contribution of our architecture is its decomposition of the decision problem into two parts: human intent prediction (HIP) and robot action choice (RAC). This decomposition is made possible by an assumption that the robot’s actions will not influence human intent. Presuming an observer that can feedback human actions in real-time, we leverage the well-known space environment and task scripts astronauts rehearse in advance to devise models for human intent prediction and robot action choice. We describe a series of case studies for HIP and RAC using a minimal set of state attributes, including an abbreviated action-history. MDP policies are evaluated in terms of model fitness and safety/efficiency performance tradeoffs. Simulation results indicate that incorporation of both observed and predicted human actions improves robot action choice. Future work could extend to more general human-robot interaction.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107160/1/cmcghan_1.pd

Grounding the Interaction : Knowledge Management for Interactive Robots

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. \ud \ud 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. \ud \ud 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. \ud \ud 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”.-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------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. \ud \ud 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. \ud \ud 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. \ud \ud The third part of the study is focused on the presentation of one important application of knowledge representation systems in the human-robot 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. \ud \ud 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". \ud \u