255 research outputs found

    Robotic Ironing with 3D Perception and Force/Torque Feedback in Household Environments

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    As robotic systems become more popular in household environments, the complexity of required tasks also increases. In this work we focus on a domestic chore deemed dull by a majority of the population, the task of ironing. The presented algorithm improves on the limited number of previous works by joining 3D perception with force/torque sensing, with emphasis on finding a practical solution with a feasible implementation in a domestic setting. Our algorithm obtains a point cloud representation of the working environment. From this point cloud, the garment is segmented and a custom Wrinkleness Local Descriptor (WiLD) is computed to determine the location of the present wrinkles. Using this descriptor, the most suitable ironing path is computed and, based on it, the manipulation algorithm performs the force-controlled ironing operation. Experiments have been performed with a humanoid robot platform, proving that our algorithm is able to detect successfully wrinkles present in garments and iteratively reduce the wrinkleness using an unmodified iron.Comment: Accepted and to be published on the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017) that will be held in Vancouver, Canada, September 24-28, 201

    Enabling garment-agnostic laundry tasks for a Robot Household Companion

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    Domestic chores, such as laundry tasks, are dull and repetitive. These tasks consume a significant amount of daily time, and are however unavoidable. Additionally, a great portion of elder and disabled people require help to perform them due to lack of mobility. In this work we present advances towards a Robot Household Companion (RHC), focusing on the performance of two particular laundry tasks: unfolding and ironing garments. Unfolding is required to recognize the garment prior to any later folding operation. For unfolding, we apply an interactive algorithm based on the analysis of a colored 3D reconstruction of the garment. Regions are clustered based on height, and a bumpiness value is computed to determine the most suitable pick and place points to unfold the overlapping region. For ironing, a custom Wrinkleness Local Descriptor (WiLD) descriptor is applied to a 3D reconstruction to find the most significant wrinkles in the garment. These wrinkles are then ironed using an iterative path-following control algorithm that regulates the amount of pressure exerted on the garment. Both algorithms focus on the feasibility of a physical implementation in real unmodified environments. A set of experiments to validate the algorithms have been performed using a full-sized humanoid robot.This work was supported by RoboCity2030-III-CM project (S2013/MIT-2748), funded by Programas de Actividades I+D in Comunidad de Madrid, Spain and EU and by a FPU grant funded by Ministerio de Educación, Cultura y Deporte, Spain. It was also supported by the anonymous donor of a red hoodie used in our initial trials. We gratefully acknowledge the support of NVIDIA, United States Corporation with the donation of the NVIDIA Titan X GPU used for this research

    Visual Closed-Loop Control for Pouring Liquids

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    Pouring a specific amount of liquid is a challenging task. In this paper we develop methods for robots to use visual feedback to perform closed-loop control for pouring liquids. We propose both a model-based and a model-free method utilizing deep learning for estimating the volume of liquid in a container. Our results show that the model-free method is better able to estimate the volume. We combine this with a simple PID controller to pour specific amounts of liquid, and show that the robot is able to achieve an average 38ml deviation from the target amount. To our knowledge, this is the first use of raw visual feedback to pour liquids in robotics.Comment: To appear at ICRA 201

    Robotic system for garment perception and manipulation

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    Mención Internacional en el título de doctorGarments are a key element of people’s daily lives, as many domestic tasks -such as laundry-, revolve around them. Performing such tasks, generally dull and repetitive, implies devoting many hours of unpaid labor to them, that could be freed through automation. But automation of such tasks has been traditionally hard due to the deformable nature of garments, that creates additional challenges to the already existing when performing object perception and manipulation. This thesis presents a Robotic System for Garment Perception and Manipulation that intends to address these challenges. The laundry pipeline as defined in this work is composed by four independent -but sequential- tasks: hanging, unfolding, ironing and folding. The aim of this work is the automation of this pipeline through a robotic system able to work on domestic environments as a robot household companion. Laundry starts by washing the garments, that then need to be dried, frequently by hanging them. As hanging is a complex task requiring bimanipulation skills and dexterity, a simplified approach is followed in this work as a starting point, by using a deep convolutional neural network and a custom synthetic dataset to study if a robot can predict whether a garment will hang or not when dropped over a hanger, as a first step towards a more complex controller. After the garment is dry, it has to be unfolded to ease recognition of its garment category for the next steps. The presented model-less unfolding method uses only color and depth information from the garment to determine the grasp and release points of an unfolding action, that is repeated iteratively until the garment is fully spread. Before storage, wrinkles have to be removed from the garment. For that purpose, a novel ironing method is proposed, that uses a custom wrinkle descriptor to locate the most prominent wrinkles and generate a suitable ironing plan. The method does not require a precise control of the light conditions of the scene, and is able to iron using unmodified ironing tools through a force-feedback-based controller. Finally, the last step is to fold the garment to store it. One key aspect when folding is to perform the folding operation in a precise manner, as errors will accumulate when several folds are required. A neural folding controller is proposed that uses visual feedback of the current garment shape, extracted through a deep neural network trained with synthetic data, to accurately perform a fold. All the methods presented to solve each of the laundry pipeline tasks have been validated experimentally on different robotic platforms, including a full-body humanoid robot.La ropa es un elemento clave en la vida diaria de las personas, no sólo a la hora de vestir, sino debido también a que muchas de las tareas domésticas que una persona debe realizar diariamente, como hacer la colada, requieren interactuar con ellas. Estas tareas, a menudo tediosas y repetitivas, obligan a invertir una gran cantidad de horas de trabajo no remunerado en su realización, las cuales podrían reducirse a través de su automatización. Sin embargo, automatizar dichas tareas ha sido tradicionalmente un reto, debido a la naturaleza deformable de las prendas, que supone una dificultad añadida a las ya existentes al llevar a cabo percepción y manipulación de objetos a través de robots. Esta tesis presenta un sistema robótico orientado a la percepción y manipulación de prendas, que pretende resolver dichos retos. La colada es una tarea doméstica compuesta de varias subtareas que se llevan a cabo de manera secuencial. En este trabajo, se definen dichas subtareas como: tender, desdoblar, planchar y doblar. El objetivo de este trabajo es automatizar estas tareas a través de un sistema robótico capaz de trabajar en entornos domésticos, convirtiéndose en un asistente robótico doméstico. La colada comienza lavando las prendas, las cuales han de ser posteriormente secadas, generalmente tendiéndolas al aire libre, para poder realizar el resto de subtareas con ellas. Tender la ropa es una tarea compleja, que requiere de bimanipulación y una gran destreza al manipular la prenda. Por ello, en este trabajo se ha optado por abordar una versión simplicada de la tarea de tendido, como punto de partida para llevar a cabo investigaciones más avanzadas en el futuro. A través de una red neuronal convolucional profunda y un conjunto de datos de entrenamiento sintéticos, se ha llevado a cabo un estudio sobre la capacidad de predecir el resultado de dejar caer una prenda sobre un tendedero por parte de un robot. Este estudio, que sirve como primer paso hacia un controlador más avanzado, ha resultado en un modelo capaz de predecir si la prenda se quedará tendida o no a partir de una imagen de profundidad de la misma en la posición en la que se dejará caer. Una vez las prendas están secas, y para facilitar su reconocimiento por parte del robot de cara a realizar las siguientes tareas, la prenda debe ser desdoblada. El método propuesto en este trabajo para realizar el desdoble no requiere de un modelo previo de la prenda, y utiliza únicamente información de profundidad y color, obtenida mediante un sensor RGB-D, para calcular los puntos de agarre y soltado de una acción de desdoble. Este proceso es iterativo, y se repite hasta que la prenda se encuentra totalmente desdoblada. Antes de almacenar la prenda, se deben eliminar las posibles arrugas que hayan surgido en el proceso de lavado y secado. Para ello, se propone un nuevo algoritmo de planchado, que utiliza un descriptor de arrugas desarrollado en este trabajo para localizar las arrugas más prominentes y generar un plan de planchado acorde a las condiciones de la prenda. A diferencia de otros métodos existentes, este método puede aplicarse en un entorno doméstico, ya que no requiere de un contol preciso de las condiciones de iluminación. Además, es capaz de usar las mismas herramientas de planchado que usaría una persona sin necesidad de realizar modificaciones a las mismas, a través de un controlador que usa realimentación de fuerza para aplicar una presión constante durante el planchado. El último paso al hacer la colada es doblar la prenda para almacenarla. Un aspecto importante al doblar prendas es ejecutar cada uno de los dobleces necesarios con precisión, ya que cada error o desfase cometido en un doblez se acumula cuando la secuencia de doblado está formada por varios dobleces consecutivos. Para llevar a cabo estos dobleces con la precisión requerida, se propone un controlador basado en una red neuronal, que utiliza realimentación visual de la forma de la prenda durante cada operación de doblado. Esta realimentación es obtenida a través de una red neuronal profunda entrenada con un conjunto de entrenamiento sintético, que permite estimar la forma en 3D de la parte a doblar a través de una imagen monocular de la misma. Todos los métodos descritos en esta tesis han sido validados experimentalmente con éxito en diversas plataformas robóticas, incluyendo un robot humanoide.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Abderrahmane Kheddar.- Secretario: Ramón Ignacio Barber Castaño.- Vocal: Karinne Ramírez-Amar

    Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary

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    The complex physical properties of highly deformable materials such as clothes pose significant challenges fanipulation systems. We present a novel visual feedback dictionary-based method for manipulating defoor autonomous robotic mrmable objects towards a desired configuration. Our approach is based on visual servoing and we use an efficient technique to extract key features from the RGB sensor stream in the form of a histogram of deformable model features. These histogram features serve as high-level representations of the state of the deformable material. Next, we collect manipulation data and use a visual feedback dictionary that maps the velocity in the high-dimensional feature space to the velocity of the robotic end-effectors for manipulation. We have evaluated our approach on a set of complex manipulation tasks and human-robot manipulation tasks on different cloth pieces with varying material characteristics.Comment: The video is available at goo.gl/mDSC4

    Dataset of Tactile Signatures of the Human Right Hand in Twenty-One Activities of Daily Living Using a High Spatial Resolution Pressure Sensor

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    Successful grasping with multi-fingered prosthetic or robotic hands remains a challenge to be solved for the effective use of these hands in unstructured environments. To this end, currently available tactile sensors need to improve their sensitivity, robustness, and spatial resolution, but a better knowledge of the distribution of contact forces in the human hand in grasping tasks is also necessary. The human tactile signatures can inform models for an efficient control of the artificial hands. In this study we present and analyze a dataset of tactile signatures of the human hand in twenty-one representative activities of daily living, obtained using a commercial high spatial resolution pressure sensor. The experiments were repeated for twenty-two subjects. The whole dataset includes more than one hundred million pressure data. The effect of the task and the subject on the grip force and the contribution to this grip force made by the different hand regions were analyzed. We also propose a method to effectively synchronize the measurements from different subjects and a method to represent the tactile signature of each task, highlighting the hand regions mainly involved in the task. The correlations between hand regions and between different tasks were also analyzed
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