Traditional methods for Learning from Demonstration require users to train the robot through the entire process, or to provide feedback throughout a given task. These previous methods have proved to be successful in a selection of robotic domains; however, many are limited by the ability of the user to effectively demonstrate the task. In many cases, noisy demonstrations or a failure to understand the underlying model prevent these methods from working with a wider range of non-expert users. My insight is that in many mobile pick-and-place domains, teaching is done at a too fine grained level. In many such tasks, users are solely concerned with the end goal. This implies that the complexity and time associated with training and teaching robots through the entirety of the task is unnecessary. The robotic agent needs to know (1) a probable search location to retrieve the task's objects and (2) how to arrange the items to complete the task. This thesis work develops new techniques for obtaining such data from high-level spatial and temporal observations and demonstrations which can later be applied in new, unseen environments. This thesis makes the following contributions: (1) This work is built on a crowd robotics platform and, as such, we contribute the development of efficient data streaming techniques to further these capabilities. By doing so, users can more easily interact with robots on a number of platforms. (2) The presentation of new algorithms that can learn pick-and-place tasks from a large corpus of goal templates. My work contributes algorithms that produce a metric which ranks the appropriate frame of reference for each item based solely on spatial demonstrations. (3) An algorithm which can enhance the above templates with ordering constraints using coarse and noisy temporal information. Such a method eliminates the need for a user to explicitly specify such constraints and searches for an optimal ordering and placement of items. (4) A novel algorithm which is able to learn probable search locations of objects based solely on sparsely made temporal observations. For this, we introduce persistence models of objects customized to a user's environment.

The evolution of cloud computing has finally started to affect robotics. Indeed, there have been several real-time cloud applications making their way into robotics as of late. Inherent benefits of cloud robotics include providing virtually infinite computational power and enabling collaboration of a multitude of connected devices. However, its drawbacks include higher latency and overall higher energy consumption. Moreover, local devices in proximity incur higher latency when communicating among themselves via the cloud. At the same time, the cloud is a single point of failure in the network. Fog Computing is an extension of the cloud computing paradigm providing data, compute, storage and application services to end-users on a so-called edge layer. Distinguishing characteristics are its support for mobility and dense geographical distribution. We propose to study the implications of applying fog computing concepts in robotics by developing a middle-ware solution for Robotic Fog Computing Cluster solution for enabling adaptive distributed computation in heterogeneous multi-robot systems interacting with the Internet of Things (IoT). The developed middle-ware has a modular plug-in architecture based on micro-services and facilitates communication of IOT devices with the multi-robot systems. In addition, the developed middle-ware solutions support different load balancing or task allocation algorithms. In particular, we establish that we can enhance the performance of distributed system by decreasing overall system latency by using already established multi-criteria decision-making algorithms like TOPSIS and TODIM with naive Q-learning and with Neural Network based Q-learning. / Master of Science

Manufacturing, under the umbrella of the latest industrial revolution, has gone through enormous changes in the last decades to then later evolve in what we know now as smart manufacturing. Different companies and entities have developed their own versions of architectures for intelligentand digitalized manufacturing systems. Ideating a exible and safe architecture is one of the first steps towards a system that intends to be applicable in different environments, regardless of the vast variety of possibilities available. For this purpose, the following thesis presents an investigation on the state-of-the-art solutions of the most recent digitalized cloud-based system architectures in the domain of discreet manufacturing. Based on an initial system architecture conceived from the company ABB, an evaluation of this architecture was conducted, by taking in consideration the existing systematical approaches to the digitalization of this industry. In the following thesis work, we investigate, describe and evaluate the limitations and strengths of the most recent and known architectural approaches to cloud robotics. Finally, a few key remarks are made towards ABB's initial solution but also to the industry in general. / PADME

Le développement de logiciels pour les robots connectés est une difficulté majeure dans le domaine du génie logiciel. Les systèmes proposés sont souvent issus de la fusion de une ou plusieurs plates-formes provenant des robots, des ordinateurs autonomes, des appareils mobiles, des machines virtuelles, des caméras et des réseaux. Nous proposons ici une approche orientée agent permettant de représenter les robots et tous les systèmes auxiliaires comme des agents d’un système. Ce concept de l’agence préserve l’autonomie sur chacun des agents, ce qui est essentiel dans la mise en oeuvre logique d’un nuage d’éléments connectés. Afin de procurer une flexibilité de mise en oeuvre des échanges entre les différentes entités, nous avons mis en place un mécanisme d’hyperactivité ce qui permet de libérer sélectivement une certaine autonomie d’un agent par rapport à ces associés.Actuellement, il n’existe pas de solution orientée méta-modèle pour décrire les ensembles de robots interconnectés. Dans cette thèse, nous présentons un méta-modèle appelé HTM5 pour spécifier a structure, les relations, les échanges, le comportement du système et l’hyperactivité dans un système de nuages de robots. La thèse décrit l’anatomie du méta-modèle (HTM5) en spécifiant les différentes couches indépendantes et en intégrant une plate-forme indépendante de toute plateforme spécifique. Par ailleurs, la thèse décrit également un langage de domaine spécifique pour la modélisation indépendante dans HTM5. Des études de cas concernant la conception et la mise en oeuvre d’un système multi-robots basés sur le modèle développé sont également présentés dans la thèse. Ces études présentent des applications où les décisions commerciales dynamiques sont modélisées à l’aide du modèle HTM5 confirmant ainsi la faisabilité du méta-modèle proposé. / Software development for cloud connected robotic systems is a complex software engineeringendeavour. These systems are often an amalgamation of one or more robotic platforms, standalonecomputers, mobile devices, server banks, virtual machines, cameras, network elements and ambientintelligence. An agent oriented approach represents robots and other auxiliary systems as agents inthe system.Software development for distributed and diverse systems like cloud robotic systems require specialsoftware modelling processes and tools. Model driven software development for such complexsystems will increase flexibility, reusability, cost effectiveness and overall quality of the end product.The proposed 5-view meta-model has separate meta-models for specifying structure, relationships,trade, system behaviour and hyperactivity in a cloud robotic system. The thesis describes theanatomy of the 5-view Hyperactive Transaction Meta-Model (HTM5) in computation independent,platform independent and platform specific layers. The thesis also describes a domain specificlanguage for computation independent modelling in HTM5.The thesis has presented a complete meta-model for agent oriented cloud robotic systems and hasseveral simulated and real experiment-projects justifying HTM5 as a feasible meta-model.

Modern day mobile robots are constrained in the resources available to them. Only so much hardware can be fit onto the robotic frame and at the same time they are required to perform tasks that require lots of computational resources, access to massive amounts of data and the ability to share knowledge with other robots around it. This thesis explores the cloud robotics approach in which complex compu- tations can be offloaded to a cloud service which can have a huge amount of computational resources and access to massive data sets. The Robot Operat- ing System, ROS, is extended to allow the robot to communicate with a high powered cluster and this system is used to test our approach on such a complex task as semantic scene understanding. The benefits of the cloud approach is utilized to connect to a cloud based object detection system and to build a cat- egorization system relying on large scale datasets and a parallel computation model. Finally a method is proposed for building a consistent scene description by exploiting semantic relationships between objects. / Moderna mobila robotar har begränsade resurser. Det får inte plats hur mycket hårdvara som helst på roboten och ändå förväntas de utföra arbeten som kräver extremt mycket datorkraft, tillgång till enorm mängd data och samtidigt kommunicera med andra robotar runt omkring sig. Det här examensarbetet utforskar robotik i molnet där komplexa beräk- ningar kan läggas ut i en molntjänst som kan ha tillgång till denna stora mängd datakraft och ha plats för de stora datamängder som behövs. The Ro- bot Operating System, eller ROS, byggs ut för att stödja kommunikation med en molntjänst och det här systemet används sedan för att testa vår lösning på ett så komplext problem som att förstå en omgivning eller miljö på ett seman- tiskt plan. Fördelarna med att använda en molnbaserad lösning används genom att koppla upp sig mot ett objektigenkänningssytem i molnet och för att byg- ga ett objektkategoriseringssystem som förlitar sig på storskaliga datamängder och parallella beräkningsmodeller. Slutligen föreslås en metod för att bygga en tillförlitlig miljöbeskrivning genom att utnyttja semantiska relationer mellan föremål.

Det här mastersprojektet syftar till att ansluta industriroboten till moln plattformen och utvärdera anslutning och säkerhet. För att uppnå bättre anslutning, säkerhet och integration, föreslås en modifierad Moln Tillverkningssystem- (CRS) arkitektur, som kännetecknas av hög modularitet, standardisering och komposibilitet. Arkitekturens specifika applikationer iprivata, offentliga och hybridmoln diskuteras också. Sedan är en  systemarkitektur med detaljerad mjukvarukomposition designad för Molnrobotik. Enligt den föreslagna systemarkitekturen presenteras möjliga säkerhetshotskällor och motsvarande lösningar.Under projektet används Universell Robot 5 (UR5) som en praktisk robotinstans för att utveckla en kommunikationsrutin mellan KTH Moln och robotar. Ett applikationsprogramgränssnitt (API) skrivet i Python for Universell Robot och servern är etablerad. API: n består av två modulära delar, Gateway Agenten och Applikationsmjukvaran.Gateway Agenten realiserar kopplingen mellan Universell Robot 5 (UR5) och molnet, medan applikationsmjukvaran kan anpassas till specifika tillämpningar och krav. I detta projekt utvecklas tre huvudfunktioner i applikationsmjukvaran, inklusive datainsamling, datavisualisering och fjärrkontroll. Förutom att utvärdera anslutning och stabilitet simulerasdet privata robotik molnsystemet och det offentliga robotik molnsystemet med KTH Moln.Hybrid robotik moln systemet diskuteras också. Genom resultaten av fallstudier verifieras anslutningen och integrationen av Moln Tillverkningssystem. / This master thesis project aims to connect the industrial robot to the Cloud platform, and evaluate the connectivity and security. To realize better connectivity, security and integration, a modified Cloud Manufacturing System (CRS) architecture is proposed, which is characterized by high modularity, standardization and composability. The architecture’s specific applications in private, public and hybrid cloud are discussed as well. Then, one system architecture with detailed software composition is designed for Cloud Robotics.According to the proposed system architecture, possible security threat sources and corresponding solutions are presented.During the project, Universal Robot 5 (UR5) is utilized as a practical robot instance to develop a communication routine between KTH Cloud and robots. An Application Program Interface (API) written by Python for Universal Robots and the server is established. The API consists of two modularized part, Gateway Agent and Application Package. The Gateway Agent realizes the connection between the Universal Robot 5 (UR5) and the cloud, while theApplication Package can be customized according to specific application and requirements. In this project, three main functions are developed in the Application Package, including data acquisition, data visualization and remote control. Besides, to evaluate connectivity and stability, private robotics cloud system and public robotics cloud system are simulated with KTH Cloud. The hybrid robotics cloud system is discussed as well. Through the results of case studies, the connectivity and integration of Cloud Manufacturing System are verified.