Efficiently learning metric and topological maps with autonomous service robots

Models of the environment are needed for a wide range of robotic applications, from search and rescue to automated vacuum cleaning. Learning maps has therefore been a major research focus in the robotics community over the last decades. In general, one distinguishes between metric and topological maps. Metric maps model the environment based on grids or geometric representations whereas topological maps model the structure of the environment using a graph. The contribution of this paper is an approach that learns a metric as well as a topological map based on laser range data obtained with a mobile robot. Our approach consists of two steps. First, the robot solves the simultaneous localization and mapping problem using an efficient probabilistic filtering technique. In a second step, it acquires semantic information about the environment using machine learning techniques. This semantic information allows the robot to distinguish between different types of places like, e. g., corridors or rooms. This enables the robot to construct annotated metric as well as topological maps of the environment. All techniques have been implemented and thoroughly tested using real mobile robot in a variety of environments

Miniature mobile sensor platforms for condition monitoring of structures

In this paper, a wireless, multisensor inspection system for nondestructive evaluation (NDE) of materials is described. The sensor configuration enables two inspection modes-magnetic (flux leakage and eddy current) and noncontact ultrasound. Each is designed to function in a complementary manner, maximizing the potential for detection of both surface and internal defects. Particular emphasis is placed on the generic architecture of a novel, intelligent sensor platform, and its positioning on the structure under test. The sensor units are capable of wireless communication with a remote host computer, which controls manipulation and data interpretation. Results are presented in the form of automatic scans with different NDE sensors in a series of experiments on thin plate structures. To highlight the advantage of utilizing multiple inspection modalities, data fusion approaches are employed to combine data collected by complementary sensor systems. Fusion of data is shown to demonstrate the potential for improved inspection reliability

Learning relational models with human interaction for planning in robotics

Automated planning has proven to be useful to solve problems where an agent has to maximize a reward function by executing actions. As planners have been improved to salve more expressive and difficult problems, there is an increasing interest in using planning to improve efficiency in robotic tasks. However, planners rely on a domain model, which has to be either handcrafted or learned. Although learning domain models can be very costly, recent approaches provide generalization capabilities and integrate human feedback to reduce the amount of experiences required to learn. In this thesis we propase new methods that allow an agent with no previous knowledge to solve certain problems more efficiently by using task planning. First, we show how to apply probabilistic planning to improve robot performance in manipulation tasks (such as cleaning the dirt or clearing the tableware on a table). Planners obtain sequences of actions that get the best result in the long term, beating reactive strategies. Second, we introduce new reinforcement learning algorithms where the agent can actively request demonstrations from a teacher to learn new actions and speed up the learning process. In particular, we propase an algorithm that allows the user to set the mínimum quality to be achieved, where a better quality also implies that a larger number of demonstrations will be requested . Moreover, the learned model is analyzed to extract the unlearned or problematic parts of the model. This information allow the agent to provide guidance to the teacher when a demonstration is requested, and to avoid irrecoverable errors. Finally, a new domain model learner is introduced that, in addition to relational probabilistic action models, can also learn exogenous effects. This learner can be integrated with existing planners and reinforcement learning algorithms to salve a wide range of problems. In summary, we improve the use of learning and task planning to salve unknown tasks. The improvements allow an agent to obtain a larger benefit from planners, learn faster, balance the number of action executions and teacher demonstrations, avoid irrecoverable errors, interact with a teacher to solve difficult problems, and adapt to the behavior of other agents by learning their dynamics. All the proposed methods were compared with state-of-the-art approaches, and were also demonstrated in different scenarios, including challenging robotic tasks.La planificación automática ha probado ser de gran utilidad para resolver problemas en los que un agente tiene que ejecutar acciones para maximizar una función de recompensa. A medida que los planificadores han sido capaces de resolver problemas cada vez más complejos, ha habido un creciente interés por utilizar dichos planificadores para mejorar la eficiencia de tareas robóticas. Sin embargo, los planificadores requieren un modelo del dominio, el cual puede ser creado a mano o aprendido. Aunque aprender modelos automáticamente puede ser costoso, recientemente han aparecido métodos que permiten la interacción persona-máquina y generalizan el conocimiento para reducir la cantidad de experiencias requeridas para aprender. En esta tesis proponemos nuevos métodos que permiten a un agente sin conocimiento previo de la tarea resolver problemas de forma más eficiente mediante el uso de planificación automática. Comenzaremos mostrando cómo aplicar planificación probabilística para mejorar la eficiencia de robots en tareas de manipulación (como limpiar suciedad o recoger una mesa). Los planificadores son capaces de obtener las secuencias de acciones que producen los mejores resultados a largo plazo, superando a las estrategias reactivas. Por otro lado, presentamos nuevos algoritmos de aprendizaje por refuerzo en los que el agente puede solicitar demostraciones a un profesor. Dichas demostraciones permiten al agente acelerar el aprendizaje o aprender nuevas acciones. En particular, proponemos un algoritmo que permite al usuario establecer la mínima suma de recompensas que es aceptable obtener, donde una recompensa más alta implica que se requerirán más demostraciones. Además, el modelo aprendido será analizado para identificar qué partes están incompletas o son problemáticas. Esta información permitirá al agente evitar errores irrecuperables y también guiar al profesor cuando se solicite una demostración. Finalmente, se ha introducido un nuevo método de aprendizaje para modelos de dominios que, además de obtener modelos relacionales de acciones probabilísticas, también puede aprender efectos exógenos. Mostraremos cómo integrar este método en algoritmos de aprendizaje por refuerzo para poder abordar una mayor cantidad de problemas. En resumen, hemos mejorado el uso de técnicas de aprendizaje y planificación para resolver tareas desconocidas a priori. Estas mejoras permiten a un agente aprovechar mejor los planificadores, aprender más rápido, elegir entre reducir el número de acciones ejecutadas o el número de demostraciones solicitadas, evitar errores irrecuperables, interactuar con un profesor para resolver problemas complejos, y adaptarse al comportamiento de otros agentes aprendiendo sus dinámicas. Todos los métodos propuestos han sido comparados con trabajos del estado del arte, y han sido evaluados en distintos escenarios, incluyendo tareas robóticas

Assistive technology design and development for acceptable robotics companions for ageing years

© 2013 Farshid Amirabdollahian et al., licensee Versita Sp. z o. o. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs license, which means that the text may be used for non-commercial purposes, provided credit is given to the author.A new stream of research and development responds to changes in life expectancy across the world. It includes technologies which enhance well-being of individuals, specifically for older people. The ACCOMPANY project focuses on home companion technologies and issues surrounding technology development for assistive purposes. The project responds to some overlooked aspects of technology design, divided into multiple areas such as empathic and social human-robot interaction, robot learning and memory visualisation, and monitoring persons’ activities at home. To bring these aspects together, a dedicated task is identified to ensure technological integration of these multiple approaches on an existing robotic platform, Care-O-Bot®3 in the context of a smart-home environment utilising a multitude of sensor arrays. Formative and summative evaluation cycles are then used to assess the emerging prototype towards identifying acceptable behaviours and roles for the robot, for example role as a butler or a trainer, while also comparing user requirements to achieved progress. In a novel approach, the project considers ethical concerns and by highlighting principles such as autonomy, independence, enablement, safety and privacy, it embarks on providing a discussion medium where user views on these principles and the existing tension between some of these principles, for example tension between privacy and autonomy over safety, can be captured and considered in design cycles and throughout project developmentsPeer reviewe

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work