Sensing Highly Non-Rigid Objects with RGBD Sensors for Robotic Systems

The goal of this research is to enable a robotic system to manipulate clothing and other highly non-rigid objects using an RGBD sensor. The focus of this thesis is to define and test various algorithms / models that are used to solve parts of the laundry process (i.e. handling, classifying, sorting, unfolding, and folding). First, a system is presented for automatically extracting and classifying items in a pile of laundry. Using only visual sensors, the robot identifies and extracts items sequentially from the pile. When an item is removed and isolated, a model is captured of the shape and appearance of the object, which is then compared against a dataset of known items. The contributions of this part of the laundry process are a novel method for extracting articles of clothing from a pile of laundry, a novel method of classifying clothing using interactive perception, and a multi-layer approach termed L-M-H, more specifically L-C-S-H for clothing classification. This thesis describes two different approaches to classify clothing into categories. The first approach relies upon silhouettes, edges, and other low-level image measurements of the articles of clothing. Experiments from the first approach demonstrate the ability of the system to efficiently classify and label into one of six categories (pants, shorts, short-sleeve shirt, long-sleeve shirt, socks, or underwear). These results show that, on average, classification rates using robot interaction are 59% higher than those that do not use interaction. The second approach relies upon color, texture, shape, and edge information from 2D and 3D data within a local and global perspective. The multi-layer approach compartmentalizes the problem into a high (H) layer, multiple mid-level (characteristics(C), selection masks(S)) layers, and a low (L) layer. This approach produces \u27local\u27 solutions to solve the global classification problem. Experiments demonstrate the ability of the system to efficiently classify each article of clothing into one of seven categories (pants, shorts, shirts, socks, dresses, cloths, or jackets). The results presented in this paper show that, on average, the classification rates improve by +27.47% for three categories, +17.90% for four categories, and +10.35% for seven categories over the baseline system, using support vector machines. Second, an algorithm is presented for automatically unfolding a piece of clothing. A piece of cloth is pulled in different directions at various points of the cloth in order to flatten the cloth. The features of the cloth are extracted and calculated to determine a valid location and orientation in which to interact with it. The features include the peak region, corner locations, and continuity / discontinuity of the cloth. In this thesis, a two-stage algorithm is presented, introducing a novel solution to the unfolding / flattening problem using interactive perception. Simulations using 3D simulation software, and experiments with robot hardware demonstrate the ability of the algorithm to flatten pieces of laundry using different starting configurations. These results show that, at most, the algorithm flattens out a piece of cloth from 11.1% to 95.6% of the canonical configuration. Third, an energy minimization algorithm is presented that is designed to estimate the configuration of a deformable object. This approach utilizes an RGBD image to calculate feature correspondence (using SURF features), depth values, and boundary locations. Input from a Kinect sensor is used to segment the deformable surface from the background using an alpha-beta swap algorithm. Using this segmentation, the system creates an initial mesh model without prior information of the surface geometry, and it reinitializes the configuration of the mesh model after a loss of input data. This approach is able to handle in-plane rotation, out-of-plane rotation, and varying changes in translation and scale. Results display the proposed algorithm over a dataset consisting of seven shirts, two pairs of shorts, two posters, and a pair of pants. The current approach is compared using a simulated shirt model in order to calculate the mean square error of the distance from the vertices on the mesh model to the ground truth, provided by the simulation model

Enabling garment-agnostic laundry tasks for a Robot Household Companion

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

Robotic system for garment perception and manipulation

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

Active recognition and pose estimation of rigid and deformable objects in 3D space

Object recognition and pose estimation is a fundamental problem in computer vision and of utmost importance in robotic applications. Object recognition refers to the problem of recognizing certain object instances, or categorizing objects into specific classes. Pose estimation deals with estimating the exact position of the object in 3D space, usually expressed in Euler angles. There are generally two types of objects that require special care when designing solutions to the aforementioned problems: rigid and deformable. Dealing with deformable objects has been a much harder problem, and usually solutions that apply to rigid objects, fail when used for deformable objects due to the inherent assumptions made during the design. In this thesis we deal with object categorization, instance recognition and pose estimation of both rigid and deformable objects. In particular, we are interested in a special type of deformable objects, clothes. We tackle the problem of autonomously recognizing and unfolding articles of clothing using a dual manipulator. This problem consists of grasping an article from a random point, recognizing it and then bringing it into an unfolded state by a dual arm robot. We propose a data-driven method for clothes recognition from depth images using Random Decision Forests. We also propose a method for unfolding an article of clothing after estimating and grasping two key-points, using Hough Forests. Both methods are implemented into a POMDP framework allowing the robot to interact optimally with the garments, taking into account uncertainty in the recognition and point estimation process. This active recognition and unfolding makes our system very robust to noisy observations. Our methods were tested on regular-sized clothes using a dual-arm manipulator. Our systems perform better in both accuracy and speed compared to state-of-the-art approaches. In order to take advantage of the robotic manipulator and increase the accuracy of our system, we developed a novel approach to address generic active vision problems, called Active Random Forests. While state of the art focuses on best viewing parameters selection based on single view classifiers, we propose a multi-view classifier where the decision mechanism of optimally changing viewing parameters is inherent to the classification process. This has many advantages: a) the classifier exploits the entire set of captured images and does not simply aggregate probabilistically per view hypotheses; b) actions are based on learnt disambiguating features from all views and are optimally selected using the powerful voting scheme of Random Forests and c) the classifier can take into account the costs of actions. The proposed framework was applied to the same task of autonomously unfolding clothes by a robot, addressing the problem of best viewpoint selection in classification, grasp point and pose estimation of garments. We show great performance improvement compared to state of the art methods and our previous POMDP formulation. Moving from deformable to rigid objects while keeping our interest to domestic robotic applications, we focus on object instance recognition and 3D pose estimation of household objects. We are particularly interested in realistic scenes that are very crowded and objects can be perceived under severe occlusions. Single shot-based 6D pose estimators with manually designed features are still unable to tackle such difficult scenarios for a variety of objects, motivating the research towards unsupervised feature learning and next-best-view estimation. We present a complete framework for both single shot-based 6D object pose estimation and next-best-view prediction based on Hough Forests, the state of the art object pose estimator that performs classification and regression jointly. Rather than using manually designed features we propose an unsupervised feature learnt from depth-invariant patches using a Sparse Autoencoder. Furthermore, taking advantage of the clustering performed in the leaf nodes of Hough Forests, we learn to estimate the reduction of uncertainty in other views, formulating the problem of selecting the next-best-view. To further improve 6D object pose estimation, we propose an improved joint registration and hypotheses verification module as a final refinement step to reject false detections. We provide two additional challenging datasets inspired from realistic scenarios to extensively evaluate the state of the art and our framework. One is related to domestic environments and the other depicts a bin-picking scenario mostly found in industrial settings. We show that our framework significantly outperforms state of the art both on public and on our datasets. Unsupervised feature learning, although efficient, might produce sub-optimal features for our particular tast. Therefore in our last work, we leverage the power of Convolutional Neural Networks to tackled the problem of estimating the pose of rigid objects by an end-to-end deep regression network. To improve the moderate performance of the standard regression objective function, we introduce the Siamese Regression Network. For a given image pair, we enforce a similarity measure between the representation of the sample images in the feature and pose space respectively, that is shown to boost regression performance. Furthermore, we argue that our pose-guided feature learning using our Siamese Regression Network generates more discriminative features that outperform the state of the art. Last, our feature learning formulation provides the ability of learning features that can perform under severe occlusions, demonstrating high performance on our novel hand-object dataset. Concluding, this work is a research on the area of object detection and pose estimation in 3D space, on a variety of object types. Furthermore we investigate how accuracy can be further improved by applying active vision techniques to optimally move the camera view to minimize the detection error.Open Acces

A Grasping-centered Analysis for Cloth Manipulation

Compliant and soft hands have gained a lot of attention in the past decade because of their ability to adapt to the shape of the objects, increasing their effectiveness for grasping. However, when it comes to grasping highly flexible objects such as textiles, we face the dual problem: it is the object that will adapt to the shape of the hand or gripper. In this context, the classic grasp analysis or grasping taxonomies are not suitable for describing textile objects grasps. This work proposes a novel definition of textile object grasps that abstracts from the robotic embodiment or hand shape and recovers concepts from the early neuroscience literature on hand prehension skills. This framework enables us to identify what grasps have been used in literature until now to perform robotic cloth manipulation, and allows for a precise definition of all the tasks that have been tackled in terms of manipulation primitives based on regrasps. In addition, we also review what grippers have been used. Our analysis shows how the vast majority of cloth manipulations have relied only on one type of grasp, and at the same time we identify several tasks that need more variety of grasp types to be executed successfully. Our framework is generic, provides a classification of cloth manipulation primitives and can inspire gripper design and benchmark construction for cloth manipulation.Comment: 13 pages, 4 figures, 4 tables. Accepted for publication at IEEE Transactions on Robotic

Visual grasp point localization, classification and state recognition in robotic manipulation of cloth: an overview

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Cloth manipulation by robots is gaining popularity among researchers because of its relevance, mainly (but not only) in domestic and assistive robotics. The required science and technologies begin to be ripe for the challenges posed by the manipulation of soft materials, and many contributions have appeared in the last years. This survey provides a systematic review of existing techniques for the basic perceptual tasks of grasp point localization, state estimation and classification of cloth items, from the perspective of their manipulation by robots. This choice is grounded on the fact that any manipulative action requires to instruct the robot where to grasp, and most garment handling activities depend on the correct recognition of the type to which the particular cloth item belongs and its state. The high inter- and intraclass variability of garments, the continuous nature of the possible deformations of cloth and the evident difficulties in predicting their localization and extension on the garment piece are challenges that have encouraged the researchers to provide a plethora of methods to confront such problems, with some promising results. The present review constitutes for the first time an effort in furnishing a structured framework of these works, with the aim of helping future contributors to gain both insight and perspective on the subjectPeer ReviewedPostprint (author's final draft