Adaptive Shared Autonomy between Human and Robot to Assist Mobile Robot Teleoperation

Die Teleoperation vom mobilen Roboter wird in großem Umfang eingesetzt, wenn es für Mensch unpraktisch oder undurchführbar ist, anwesend zu sein, aber die Entscheidung von Mensch wird dennoch verlangt. Es ist für Mensch stressig und fehleranfällig wegen Zeitverzögerung und Abwesenheit des Situationsbewusstseins, ohne Unterstützung den Roboter zu steuern einerseits, andererseits kann der völlig autonome Roboter, trotz jüngsten Errungenschaften, noch keine Aufgabe basiert auf die aktuellen Modelle der Wahrnehmung und Steuerung unabhängig ausführen. Deswegen müssen beide der Mensch und der Roboter in der Regelschleife bleiben, um gleichzeitig Intelligenz zur Durchführung von Aufgaben beizutragen. Das bedeut, dass der Mensch die Autonomie mit dem Roboter während des Betriebes zusammenhaben sollte. Allerdings besteht die Herausforderung darin, die beiden Quellen der Intelligenz vom Mensch und dem Roboter am besten zu koordinieren, um eine sichere und effiziente Aufgabenausführung in der Fernbedienung zu gewährleisten. Daher wird in dieser Arbeit eine neuartige Strategie vorgeschlagen. Sie modelliert die Benutzerabsicht als eine kontextuelle Aufgabe, um eine Aktionsprimitive zu vervollständigen, und stellt dem Bediener eine angemessene Bewegungshilfe bei der Erkennung der Aufgabe zur Verfügung. Auf diese Weise bewältigt der Roboter intelligent mit den laufenden Aufgaben auf der Grundlage der kontextuellen Informationen, entlastet die Arbeitsbelastung des Bedieners und verbessert die Aufgabenleistung. Um diese Strategie umzusetzen und die Unsicherheiten bei der Erfassung und Verarbeitung von Umgebungsinformationen und Benutzereingaben (i.e. der Kontextinformationen) zu berücksichtigen, wird ein probabilistischer Rahmen von Shared Autonomy eingeführt, um die kontextuelle Aufgabe mit Unsicherheitsmessungen zu erkennen, die der Bediener mit dem Roboter durchführt, und dem Bediener die angemesse Unterstützung der Aufgabenausführung nach diesen Messungen anzubieten. Da die Weise, wie der Bediener eine Aufgabe ausführt, implizit ist, ist es nicht trivial, das Bewegungsmuster der Aufgabenausführung manuell zu modellieren, so dass eine Reihe von der datengesteuerten Ansätzen verwendet wird, um das Muster der verschiedenen Aufgabenausführungen von menschlichen Demonstrationen abzuleiten, sich an die Bedürfnisse des Bedieners in einer intuitiven Weise über lange Zeit anzupassen. Die Praxistauglichkeit und Skalierbarkeit der vorgeschlagenen Ansätze wird durch umfangreiche Experimente sowohl in der Simulation als auch auf dem realen Roboter demonstriert. Mit den vorgeschlagenen Ansätzen kann der Bediener aktiv und angemessen unterstützt werden, indem die Kognitionsfähigkeit und Autonomieflexibilität des Roboters zu erhöhen

Trust in Robots

Robots are increasingly becoming prevalent in our daily lives within our living or working spaces. We hope that robots will take up tedious, mundane or dirty chores and make our lives more comfortable, easy and enjoyable by providing companionship and care. However, robots may pose a threat to human privacy, safety and autonomy; therefore, it is necessary to have constant control over the developing technology to ensure the benevolent intentions and safety of autonomous systems. Building trust in (autonomous) robotic systems is thus necessary. The title of this book highlights this challenge: “Trust in robots—Trusting robots”. Herein, various notions and research areas associated with robots are unified. The theme “Trust in robots” addresses the development of technology that is trustworthy for users; “Trusting robots” focuses on building a trusting relationship with robots, furthering previous research. These themes and topics are at the core of the PhD program “Trust Robots” at TU Wien, Austria

Human-robot interaction and computer-vision-based services for autonomous robots

L'Aprenentatge per Imitació (IL), o Programació de robots per Demostració (PbD), abasta mètodes pels quals un robot aprèn noves habilitats a través de l'orientació humana i la imitació. La PbD s'inspira en la forma en què els éssers humans aprenen noves habilitats per imitació amb la finalitat de desenvolupar mètodes pels quals les noves tasques es poden transferir als robots. Aquesta tesi està motivada per la pregunta genèrica de "què imitar?", Que es refereix al problema de com extreure les característiques essencials d'una tasca. Amb aquesta finalitat, aquí adoptem la perspectiva del Reconeixement d'Accions (AR) per tal de permetre que el robot decideixi el què cal imitar o inferir en interactuar amb un ésser humà. L'enfoc proposat es basa en un mètode ben conegut que prové del processament del llenguatge natural: és a dir, la bossa de paraules (BoW). Aquest mètode s'aplica a grans bases de dades per tal d'obtenir un model entrenat. Encara que BoW és una tècnica d'aprenentatge de màquines que s'utilitza en diversos camps de la investigació, en la classificació d'accions per a l'aprenentatge en robots està lluny de ser acurada. D'altra banda, se centra en la classificació d'objectes i gestos en lloc d'accions. Per tant, en aquesta tesi es demostra que el mètode és adequat, en escenaris de classificació d'accions, per a la fusió d'informació de diferents fonts o de diferents assajos. Aquesta tesi fa tres contribucions: (1) es proposa un mètode general per fer front al reconeixement d'accions i per tant contribuir a l'aprenentatge per imitació; (2) la metodologia pot aplicar-se a grans bases de dades, que inclouen diferents modes de captura de les accions; i (3) el mètode s'aplica específicament en un projecte internacional d'innovació real anomenat Vinbot.El Aprendizaje por Imitación (IL), o Programación de robots por Demostración (PbD), abarca métodos por los cuales un robot aprende nuevas habilidades a través de la orientación humana y la imitación. La PbD se inspira en la forma en que los seres humanos aprenden nuevas habilidades por imitación con el fin de desarrollar métodos por los cuales las nuevas tareas se pueden transferir a los robots. Esta tesis está motivada por la pregunta genérica de "qué imitar?", que se refiere al problema de cómo extraer las características esenciales de una tarea. Con este fin, aquí adoptamos la perspectiva del Reconocimiento de Acciones (AR) con el fin de permitir que el robot decida lo que hay que imitar o inferir al interactuar con un ser humano. El enfoque propuesto se basa en un método bien conocido que proviene del procesamiento del lenguaje natural: es decir, la bolsa de palabras (BoW). Este método se aplica a grandes bases de datos con el fin de obtener un modelo entrenado. Aunque BoW es una técnica de aprendizaje de máquinas que se utiliza en diversos campos de la investigación, en la clasificación de acciones para el aprendizaje en robots está lejos de ser acurada. Además, se centra en la clasificación de objetos y gestos en lugar de acciones. Por lo tanto, en esta tesis se demuestra que el método es adecuado, en escenarios de clasificación de acciones, para la fusión de información de diferentes fuentes o de diferentes ensayos. Esta tesis hace tres contribuciones: (1) se propone un método general para hacer frente al reconocimiento de acciones y por lo tanto contribuir al aprendizaje por imitación; (2) la metodología puede aplicarse a grandes bases de datos, que incluyen diferentes modos de captura de las acciones; y (3) el método se aplica específicamente en un proyecto internacional de innovación real llamado Vinbot.Imitation Learning (IL), or robot Programming by Demonstration (PbD), covers methods by which a robot learns new skills through human guidance and imitation. PbD takes its inspiration from the way humans learn new skills by imitation in order to develop methods by which new tasks can be transmitted to robots. This thesis is motivated by the generic question of “what to imitate?” which concerns the problem of how to extract the essential features of a task. To this end, here we adopt Action Recognition (AR) perspective in order to allow the robot to decide what has to be imitated or inferred when interacting with a human kind. The proposed approach is based on a well-known method from natural language processing: namely, Bag of Words (BoW). This method is applied to large databases in order to obtain a trained model. Although BoW is a machine learning technique that is used in various fields of research, in action classification for robot learning it is far from accurate. Moreover, it focuses on the classification of objects and gestures rather than actions. Thus, in this thesis we show that the method is suitable in action classification scenarios for merging information from different sources or different trials. This thesis makes three contributions: (1) it proposes a general method for dealing with action recognition and thus to contribute to imitation learning; (2) the methodology can be applied to large databases which include different modes of action captures; and (3) the method is applied specifically in a real international innovation project called Vinbot

Towards a framework for socially interactive robots

250 p.En las últimas décadas, la investigación en el campo de la robótica social ha crecido considerablemente. El desarrollo de diferentes tipos de robots y sus roles dentro de la sociedad se están expandiendo poco a poco. Los robots dotados de habilidades sociales pretenden ser utilizados para diferentes aplicaciones; por ejemplo, como profesores interactivos y asistentes educativos, para apoyar el manejo de la diabetes en niños, para ayudar a personas mayores con necesidades especiales, como actores interactivos en el teatro o incluso como asistentes en hoteles y centros comerciales.El equipo de investigación RSAIT ha estado trabajando en varias áreas de la robótica, en particular,en arquitecturas de control, exploración y navegación de robots, aprendizaje automático y visión por computador. El trabajo presentado en este trabajo de investigación tiene como objetivo añadir una nueva capa al desarrollo anterior, la capa de interacción humano-robot que se centra en las capacidades sociales que un robot debe mostrar al interactuar con personas, como expresar y percibir emociones, mostrar un alto nivel de diálogo, aprender modelos de otros agentes, establecer y mantener relaciones sociales, usar medios naturales de comunicación (mirada, gestos, etc.),mostrar personalidad y carácter distintivos y aprender competencias sociales.En esta tesis doctoral, tratamos de aportar nuestro grano de arena a las preguntas básicas que surgen cuando pensamos en robots sociales: (1) ¿Cómo nos comunicamos (u operamos) los humanos con los robots sociales?; y (2) ¿Cómo actúan los robots sociales con nosotros? En esa línea, el trabajo se ha desarrollado en dos fases: en la primera, nos hemos centrado en explorar desde un punto de vista práctico varias formas que los humanos utilizan para comunicarse con los robots de una maneranatural. En la segunda además, hemos investigado cómo los robots sociales deben actuar con el usuario.Con respecto a la primera fase, hemos desarrollado tres interfaces de usuario naturales que pretenden hacer que la interacción con los robots sociales sea más natural. Para probar tales interfaces se han desarrollado dos aplicaciones de diferente uso: robots guía y un sistema de controlde robot humanoides con fines de entretenimiento. Trabajar en esas aplicaciones nos ha permitido dotar a nuestros robots con algunas habilidades básicas, como la navegación, la comunicación entre robots y el reconocimiento de voz y las capacidades de comprensión.Por otro lado, en la segunda fase nos hemos centrado en la identificación y el desarrollo de los módulos básicos de comportamiento que este tipo de robots necesitan para ser socialmente creíbles y confiables mientras actúan como agentes sociales. Se ha desarrollado una arquitectura(framework) para robots socialmente interactivos que permite a los robots expresar diferentes tipos de emociones y mostrar un lenguaje corporal natural similar al humano según la tarea a realizar y lascondiciones ambientales.La validación de los diferentes estados de desarrollo de nuestros robots sociales se ha realizado mediante representaciones públicas. La exposición de nuestros robots al público en esas actuaciones se ha convertido en una herramienta esencial para medir cualitativamente la aceptación social de los prototipos que estamos desarrollando. De la misma manera que los robots necesitan un cuerpo físico para interactuar con el entorno y convertirse en inteligentes, los robots sociales necesitan participar socialmente en tareas reales para las que han sido desarrollados, para así poder mejorar su sociabilida

Progress and Prospects of the Human-Robot Collaboration

International audienceRecent technological advances in hardware designof the robotic platforms enabled the implementationof various control modalities for improved interactions withhumans and unstructured environments. An important applicationarea for the integration of robots with such advancedinteraction capabilities is human-robot collaboration. Thisaspect represents high socio-economic impacts and maintainsthe sense of purpose of the involved people, as the robotsdo not completely replace the humans from the workprocess. The research community’s recent surge of interestin this area has been devoted to the implementation of variousmethodologies to achieve intuitive and seamless humanrobot-environment interactions by incorporating the collaborativepartners’ superior capabilities, e.g. human’s cognitiveand robot’s physical power generation capacity. In fact,the main purpose of this paper is to review the state-of-thearton intermediate human-robot interfaces (bi-directional),robot control modalities, system stability, benchmarking andrelevant use cases, and to extend views on the required futuredevelopments in the realm of human-robot collaboration

Generative Models for Learning Robot Manipulation Skills from Humans

A long standing goal in artificial intelligence is to make robots seamlessly interact with humans in performing everyday manipulation skills. Learning from demonstrations or imitation learning provides a promising route to bridge this gap. In contrast to direct trajectory learning from demonstrations, many problems arise in interactive robotic applications that require higher contextual level understanding of the environment. This requires learning invariant mappings in the demonstrations that can generalize across different environmental situations such as size, position, orientation of objects, viewpoint of the observer, etc. In this thesis, we address this challenge by encapsulating invariant patterns in the demonstrations using probabilistic learning models for acquiring dexterous manipulation skills. We learn the joint probability density function of the demonstrations with a hidden semi-Markov model, and smoothly follow the generated sequence of states with a linear quadratic tracking controller. The model exploits the invariant segments (also termed as sub-goals, options or actions) in the demonstrations and adapts the movement in accordance with the external environmental situations such as size, position and orientation of the objects in the environment using a task-parameterized formulation. We incorporate high-dimensional sensory data for skill acquisition by parsimoniously representing the demonstrations using statistical subspace clustering methods and exploit the coordination patterns in latent space. To adapt the models on the fly and/or teach new manipulation skills online with the streaming data, we formulate a non-parametric scalable online sequence clustering algorithm with Bayesian non-parametric mixture models to avoid the model selection problem while ensuring tractability under small variance asymptotics. We exploit the developed generative models to perform manipulation skills with remotely operated vehicles over satellite communication in the presence of communication delays and limited bandwidth. A set of task-parameterized generative models are learned from the demonstrations of different manipulation skills provided by the teleoperator. The model captures the intention of teleoperator on one hand and provides assistance in performing remote manipulation tasks on the other hand under varying environmental situations. The assistance is formulated under time-independent shared control, where the model continuously corrects the remote arm movement based on the current state of the teleoperator; and/or time-dependent autonomous control, where the model synthesizes the movement of the remote arm for autonomous skill execution. Using the proposed methodology with the two-armed Baxter robot as a mock-up for semi-autonomous teleoperation, we are able to learn manipulation skills such as opening a valve, pick-and-place an object by obstacle avoidance, hot-stabbing (a specialized underwater task akin to peg-in-a-hole task), screw-driver target snapping, and tracking a carabiner in as few as 4 - 8 demonstrations. Our study shows that the proposed manipulation assistance formulations improve the performance of the teleoperator by reducing the task errors and the execution time, while catering for the environmental differences in performing remote manipulation tasks with limited bandwidth and communication delays

Towards edge robotics: the progress from cloud-based robotic systems to intelligent and context-aware robotic services

Current robotic systems handle a different range of applications such as video surveillance, delivery of goods, cleaning, material handling, assembly, painting, or pick and place services. These systems have been embraced not only by the general population but also by the vertical industries to help them in performing daily activities. Traditionally, the robotic systems have been deployed in standalone robots that were exclusively dedicated to performing a specific task such as cleaning the floor in indoor environments. In recent years, cloud providers started to offer their infrastructures to robotic systems for offloading some of the robot’s functions. This ultimate form of the distributed robotic system was first introduced 10 years ago as cloud robotics and nowadays a lot of robotic solutions are appearing in this form. As a result, standalone robots became software-enhanced objects with increased reconfigurability as well as decreased complexity and cost. Moreover, by offloading the heavy processing from the robot to the cloud, it is easier to share services and information from various robots or agents to achieve better cooperation and coordination. Cloud robotics is suitable for human-scale responsive and delay-tolerant robotic functionalities (e.g., monitoring, predictive maintenance). However, there is a whole set of real-time robotic applications (e.g., remote control, motion planning, autonomous navigation) that can not be executed with cloud robotics solutions, mainly because cloud facilities traditionally reside far away from the robots. While the cloud providers can ensure certain performance in their infrastructure, very little can be ensured in the network between the robots and the cloud, especially in the last hop where wireless radio access networks are involved. Over the last years advances in edge computing, fog computing, 5G NR, network slicing, Network Function Virtualization (NFV), and network orchestration are stimulating the interest of the industrial sector to satisfy the stringent and real-time requirements of their applications. Robotic systems are a key piece in the industrial digital transformation and their benefits are very well studied in the literature. However, designing and implementing a robotic system that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency, reliability) is an ambitious task. This thesis studies the integration of modern Information andCommunication Technologies (ICTs) in robotic systems and proposes some robotic enhancements that tackle the real-time constraints of robotic services. To evaluate the performance of the proposed enhancements, this thesis departs from the design and prototype implementation of an edge native robotic system that embodies the concepts of edge computing, fog computing, orchestration, and virtualization. The proposed edge robotics system serves to represent two exemplary robotic applications. In particular, autonomous navigation of mobile robots and remote-control of robot manipulator where the end-to-end robotic system is distributed between the robots and the edge server. The open-source prototype implementation of the designed edge native robotic system resulted in the creation of two real-world testbeds that are used in this thesis as a baseline scenario for the evaluation of new innovative solutions in robotic systems. After detailing the design and prototype implementation of the end-to-end edge native robotic system, this thesis proposes several enhancements that can be offered to robotic systems by adapting the concept of edge computing via the Multi-Access Edge Computing (MEC) framework. First, it proposes exemplary network context-aware enhancements in which the real-time information about robot connectivity and location can be used to dynamically adapt the end-to-end system behavior to the actual status of the communication (e.g., radio channel). Three different exemplary context-aware enhancements are proposed that aim to optimize the end-to-end edge native robotic system. Later, the thesis studies the capability of the edge native robotic system to offer potential savings by means of computation offloading for robot manipulators in different deployment configurations. Further, the impact of different wireless channels (e.g., 5G, 4G andWi-Fi) to support the data exchange between a robot manipulator and its remote controller are assessed. In the following part of the thesis, the focus is set on how orchestration solutions can support mobile robot systems to make high quality decisions. The application of OKpi as an orchestration algorithm and DLT-based federation are studied to meet the KPIs that autonomously controlledmobile robots have in order to provide uninterrupted connectivity over the radio access network. The elaborated solutions present high compatibility with the designed edge robotics system where the robot driving range is extended without any interruption of the end-to-end edge robotics service. While the DLT-based federation extends the robot driving range by deploying access point extension on top of external domain infrastructure, OKpi selects the most suitable access point and computing resource in the cloud-to-thing continuum in order to fulfill the latency requirements of autonomously controlled mobile robots. To conclude the thesis the focus is set on how robotic systems can improve their performance by leveraging Artificial Intelligence (AI) and Machine Learning (ML) algorithms to generate smart decisions. To do so, the edge native robotic system is presented as a true embodiment of a Cyber-Physical System (CPS) in Industry 4.0, showing the mission of AI in such concept. It presents the key enabling technologies of the edge robotic system such as edge, fog, and 5G, where the physical processes are integrated with computing and network domains. The role of AI in each technology domain is identified by analyzing a set of AI agents at the application and infrastructure level. In the last part of the thesis, the movement prediction is selected to study the feasibility of applying a forecast-based recovery mechanism for real-time remote control of robotic manipulators (FoReCo) that uses ML to infer lost commands caused by interference in the wireless channel. The obtained results are showcasing the its potential in simulation and real-world experimentation.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Karl Holger.- Secretario: Joerg Widmer.- Vocal: Claudio Cicconett

Flexible Supervised Autonomy for Exploration in Subterranean Environments

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.Comment: Field Robotics special issue: DARPA Subterranean Challenge, Advancement and Lessons Learned from the Final