Theory of Robot Communication: I. The Medium is the Communication Partner

When people use electronic media for their communication, Computer-Mediated Communication (CMC) theories describe the social and communicative aspects of people's interpersonal transactions. When people interact via a remote-controlled robot, many of the CMC theses hold. Yet, what if people communicate with a conversation robot that is (partly) autonomous? Do the same theories apply? This paper discusses CMC theories in confrontation with observations and research data gained from human-robot communication. As a result, I argue for an addition to CMC theorizing when the robot as a medium itself becomes the communication partner. In view of the rise of social robots in coming years, I define the theoretical precepts of a possible next step in CMC, which I elaborate in a second paper.Comment: Hoorn, J. F. (2018). Theory of robot communication: I. The medium is the communication partner. arXiv:cs, 2502565(v1), 1-2

ILLUSTRATING THE PERCEPTION OF STUDENTS TOWARDS AUTONOMOUS SERVICE ROBOTS IN THE TOURISM INDUSTRY: AN EXPLORATORY STUDY

Research purpose – The purpose of the study was to explore the students’ perceptions towards autonomous service robots, using interviews and, in a novel manner, also their drawings, to generate data. The paper contributes to current knowledge claims around the Techno-Economic Paradigm by providing a greater understanding of, and student response to, autonomous service robots. Additional insights were gained on transformational skills set for tourism practitioners. Design/Methodology/Approach – A qualitative approach followed a participatory research design which was set in a public higher education institution. Undergraduate tourism and hospitality students who had previous exposure and engagement with robots were the purposive sample. The paper draws on the Techno-Economic Paradigm to illuminate how, according to students’ experiences and perceptions, autonomous robots interact in, and disrupt, the tourism industry. Findings – Four main drawings essentially expressed tourism and hospitality students’ perceptions of autonomous service robots in the tourism industry. Centrally there are opportunities for the absorption of robots in certain sectors of the tourism industry. Despite greater use of robots in service-driven industries like tourism, it remains challenging to establish the right balance between humans and robots, and up- and re-skilling transformation would be required of those working in the tourism industry and those studying towards a tourism qualification. This study advances that additional research is still required, including longitudinal studies on the effects of autonomous services in the tourism industry, as well as students’ perception on the use of robots, re-skilling as well as ethical risks to customers, the greater value to the economy and those working in the tourism industry. Originality of the research – The article contributes to the use of visual methodology as part of data generation, specifically how students’ perceptions regarding autonomous robots in the tourism industry were graphically distilled using this methodology

Decentralized Autonomous Navigation Strategies for Multi-Robot Search and Rescue

In this report, we try to improve the performance of existing approaches for search operations in multi-robot context. We propose three novel algorithms that are using a triangular grid pattern, i.e., robots certainly go through the vertices of a triangular grid during the search procedure. The main advantage of using a triangular grid pattern is that it is asymptotically optimal in terms of the minimum number of robots required for the complete coverage of an arbitrary bounded area. We use a new topological map which is made and shared by robots during the search operation. We consider an area that is unknown to the robots a priori with an arbitrary shape, containing some obstacles. Unlike many current heuristic algorithms, we give mathematically proofs of convergence of the algorithms. The computer simulation results for the proposed algorithms are presented using a simulator of real robots and environment. We evaluate the performance of the algorithms via experiments with real robots. We compare the performance of our own algorithms with three existing algorithms from other researchers. The results demonstrate the merits of our proposed solution. A further study on formation building with obstacle avoidance for a team of mobile robots is presented in this report. We propose a decentralized formation building with obstacle avoidance algorithm for a group of mobile robots to move in a defined geometric configuration. Furthermore, we consider a more complicated formation problem with a group of anonymous robots; these robots are not aware of their position in the final configuration and need to reach a consensus during the formation process. We propose a randomized algorithm for the anonymous robots that achieves the convergence to a desired configuration with probability 1. We also propose a novel obstacle avoidance rule, used in the formation building algorithm.Comment: arXiv admin note: substantial text overlap with arXiv:1402.5188 by other author

Knowledge representation and exploitation for interactive and cognitive robots

L'arrivée des robots dans notre vie quotidienne fait émerger le besoin pour ces systèmes d'avoir accès à une représentation poussée des connaissances et des capacités de raisonnements associées. Ainsi, les robots doivent pouvoir comprendre les éléments qui composent l'environnement dans lequel ils évoluent. De plus, la présence d'humains dans ces environnements et donc la nécessité d'interagir avec eux amènent des exigences supplémentaires. Ainsi, les connaissances ne sont plus utilisées par le robot dans le seul but d'agir physiquement sur son environnement mais aussi dans un but de communication et de partage d'information avec les humains. La connaissance ne doit plus être uniquement compréhensible par le robot lui-même mais doit aussi pouvoir être exprimée. Dans la première partie de cette thèse, nous présentons Ontologenius. C'est un logiciel permettant de maintenir des bases de connaissances sous forme d'ontologie, de raisonner dessus et de les gérer dynamiquement. Nous commençons par expliquer en quoi ce logiciel est adapté aux applications d'interaction humain-robot (HRI), notamment avec la possibilité de représenter la base de connaissances du robot mais aussi une estimation des bases de connaissances des partenaires humains ce qui permet d'implémenter les mécanismes de théorie de l'esprit. Nous poursuivons avec une présentation de ses interfaces. Cette partie se termine par une analyse des performances du système ainsi développé. Dans une seconde partie, cette thèse présente notre contribution à deux problèmes d'exploration des connaissances: l'un ayant trait au référencement spatial et l'autre à l'utilisation de connaissances sémantiques. Nous commençons par une tâche de description d'itinéraires pour laquelle nous proposons une ontologie permettant de décrire la topologie d'environnements intérieurs et deux algorithmes de recherche d'itinéraires. Nous poursuivons avec une tâche de génération d'expression de référence. Cette tâche vise à sélectionner l'ensemble optimal d'informations à communiquer afin de permettre à un auditeur d'identifier l'entité référencée dans un contexte donné. Ce dernier algorithme est ensuite affiné pour y ajouter les informations sur les activités passées provenant d'une action conjointe entre un robot et un humain, afin de générer des expressions encore plus pertinentes. Il est également intégré à un planificateur de tâches symbolique pour estimer la faisabilité et le coût des futures communications. Cette thèse se termine par la présentation de deux architectures cognitives, la première utilisant notre contribution concernant la description d'itinéraire et la seconde utilisant nos contributions autour de la Génération d'Expression de Référence. Les deux utilisent Ontologenius pour gérer la base de connaissances sémantique. À travers ces deux architectures, nous présentons comment nos travaux ont amené la base de connaissances a progressivement prendre un rôle central, fournissant des connaissances à tous les composants du système.As robots begin to enter our daily lives, we need advanced knowledge representations and associated reasoning capabilities to enable them to understand and model their environments. Considering the presence of humans in such environments, and therefore the need to interact with them, this need comes with additional requirements. Indeed, knowledge is no longer used by the robot for the sole purpose of being able to act physically on the environment but also to communicate and share information with humans. Therefore knowledge should no longer be understandable only by the robot itself, but should also be able to be narrative-enabled. In the first part of this thesis, we present our first contribution with Ontologenius. This software allows to maintain knowledge bases in the form of ontology, to reason on them and to manage them dynamically. We start by explaining how this software is suitable for \acrfull{hri} applications. To that end, for example to implement theory of mind abilities, it is possible to represent the robot's knowledge base as well as an estimate of the knowledge bases of human partners. We continue with a presentation of its interfaces. This part ends with a performance analysis, demonstrating its online usability. In a second part, we present our contribution to two knowledge exploration problems around the general topic of spatial referring and the use of semantic knowledge. We start with the route description task which aims to propose a set of possible routes leading to a target destination, in the framework of a guiding task. To achieve this task, we propose an ontology allowing us to describe the topology of indoor environments and two algorithms to search for routes. The second knowledge exploration problem we tackle is the \acrfull{reg} problem. It aims at selecting the optimal set of piece of information to communicate in order to allow a hearer to identify the referred entity in a given context. This contribution is then refined to use past activities coming from joint action between a robot and a human, in order to generate new kinds of Referring Expressions. It is also linked with a symbolic task planner to estimate the feasibility and cost of future communications. We conclude this thesis by the presentation of two cognitive architectures. The first one uses the route description contribution and the second one takes advantage of our Referring Expression Generation contribution. Both of them use Ontologenius to manage the semantic Knowledge Base. Through these two architectures, we present how our contributions enable Knowledge Base to gradually take a central role, providing knowledge to all the components of the architectures

Social navigation of autonomous robots in populated environments

Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería InformáticaClave Programa: DBICódigo Línea: 19Today, more and more mobile robots are coexisting with us in our daily lives. As a result, the behavior of robots that share space with humans in dynamic environments is a subject of intense investigation in robotics. Robots must re- spect human social conventions, guarantee the comfort of surrounding people, and maintain the legibility so that humans can understand the robot¿s intentions. Robots that move in humans¿ vicinity should navigate in a socially compliant way; this is called human-aware navigation. These social behaviors are not easy to frame in mathematical expressions. Consequently, motion planners with pre- programmed constraints and hard-coded functions can fail in acquiring proper behaviors related to human-awareness. All in all, it is easier to demonstrate socially acceptable behaviors than mathematically defining them. Therefore, learning these social behaviors from data seems a more principled approach. This thesis aims at endowing mobile robots with new social skills for au- tonomous navigation in spaces populated with humans. This work makes use of learning from demonstration (LfD) approaches to solve the problem of human- aware navigation. Different techniques and algorithms are explored and devel- oped in order to transfer social navigation behaviors to a robot by using demon- strations of human experts performing the proposed tasks. The contributions of this thesis are in the field of Learning from Demonstra- tion applied to human-aware navigation tasks. First, a LfD technique based on Inverse Reinforcement Learning (IRL) is employed to learn a policy for ¿social¿ local motion planning. Then, a novel learning algorithm combining LfD concepts and sampling-based path planners is presented. Finally, other novel approaches combining different LfD techniques, like deep learning among others, and path planners are investigated. The methods proposed are compared against state- of-the-art approaches and tested in different experiments with the real robots employed in the European projects FROG and TERESA.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e InformáticaPostprin

Facilitating Reliable Autonomy with Human-Robot Interaction

Autonomous robots are increasingly deployed to complex environments in which we cannot predict all possible failure cases a priori. Robustness to failures can be provided by humans enacting the roles of: (1) developers who can iteratively incorporate robustness into the robot system, (2) collocated bystanders who can be approached for aid, and (3) remote teleoperators who can be contacted for guidance. However, assisting the robot in any of these roles can place demands on the time or effort of the human. This dissertation develops modules to reduce the frequency and duration of failure interventions in order to increase the reliability of autonomous robots, while also reducing the demand on humans. In pursuit of that goal, the dissertation makes the following contributions: (1) A development paradigm for autonomous robots that separates task specification from error recovery. The paradigm reduces burden on developers while making the robot robust to failures. (2) A model for gauging the interruptibility of collocated humans. A human-subjects study shows that using the model can reduce the time expended by the robot during failure recovery. (3) A human-subjects experiment on the effects of decision support provided to remote operators during failures. The results show that humans need both diagnosis and action recommendations as decision support during an intervention. (4) An evaluation of model features and unstructured Machine Learning (ML) techniques in pursuit of learning robust suggestions models from intervention data, in order to reduce developer effort. The results indicate that careful crafting of features can lead to improved performance, but that without such feature selection, current ML algorithms lack robustness in addressing a domain where the robot's observations are heavily influenced by the user's actions.Ph.D

飛行ロボットにおける人間・ロボットインタラクションの実現に向けて : ユーザー同伴モデルとセンシングインターフェース

学位の種別: 課程博士審査委員会委員 : （主査）東京大学准教授 矢入 健久, 東京大学教授 堀 浩一, 東京大学教授 岩崎 晃, 東京大学教授 土屋 武司, 東京理科大学教授 溝口 博University of Tokyo(東京大学