Learning locomotion gait through hormone-based controller in modular robots

Modular robots are robots composed of multiple units, called 'modules'. Each module is an independent robot, with its own control electronics, actuators, sensors, communications and power. These modules can change their position and configuration in order to adapt to the requirements of the situation, making modular robot suitable for tasks that involve unknown or unstructured terrains, in which a robot cannot be designed speci cally for them. Some examples of those applications are space exploration, battlefield reconnaissance, finding victims among the debris in natural catastrophes and other similar tasks involving complicated terrains, which require a high versability. But this versability comes with several drawbacks. As modular robots are composed of several independent robots, the nature of their controller is distributed, which difficults their design and programming, requiring additionally a robust communication protocol to share information among modules. The high number of modules also results in a robot with a with number of degrees of freedom, for which achieving the coordination required for locomotion becomes increasingly difficult. Finally, as the modules are fully independent robots, the cost of researching modular robotics is usually very high, since the price of building a single robot has to be multiplied by the high number of modules. This thesis addresses those three mentioned problems: obtaining optimal locomotion gaits from a biologically inspired approach, using sinusoidal oscillators whose parameters are found through evolutionary optimization algorithms; developing a homogenous, distributed controller based on digital hormones that can recognize the current robot configuration and select the proper gait; and the development of a low-cost modular robotic platform to reseach locomotion gaits for different configurations.Ingeniería Electrónica Industrial y Automátic

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

Situación actual del juego con dinero en jóvenes y adolescentes

El juego con apuesta es un fenómeno social muy extendido en nuestra cultura que apenas ha sido estudiado en jóvenes y adolescentes a pesar de que los datos muestran prevalencias importantes de juego. Por ello, el objetivo de este estudio ha sido conocer en profundidad a qué juega la juventud, cuáles son sus juegos de azar preferidos, así como profundizar en sus motivaciones, opiniones y percepciones referentes al juego con dinero. Se ha utilizado una metodología cualitativa, organizando y desarrollando 6 grupos de discusión en los que han participado un total de 79 jóvenes y adolescentes procedentes de centros escolares y universitarios del País Vasco. Los resultados han mostrado que la irrupción de las nuevas tecnologías, la normalización del juego en nuestra sociedad y la facilidad de acceso al mismo constituirían algunos de los pilares principales en los que se sustenta el incremento de jóvenes que participan en juegos con dinero. Estos resultados alertan sobre la importancia de la prevención y el tratamiento del juego patológico así como la necesidad de atender a un colectivo más joven del habitual.Although the data indicates a prevalence of gambling among younger teenagers, there are very few studies on this widespread social phenomenon. The aim of this research was to gain in-depth knowledge of gambling among the young with a focus on finding out their favorite games, motivations, opinions and perceptions regarding gambling for money. A qualitative methodology was used, organizing and holding six discussion groups which involved a total of 79 young people and teenagers from schools and universities in the Basque Country. The results have shown that the emergence of new technologies, the fact that gambling has become a normal activity in our society and easy access to it are some of the main reasons for the increase in young people's participation in cash games. These findings alert us to the need for prevention and treatment of pathological gambling and the importance of targeting younger groups

Perfiles de jugadores patológicos en la adolescencia.

El juego es un trastorno del control de los impulsos que causa muchos problemas a las personas que lo padecen. En el caso de los jóvenes y adolescentes, esta circunstancia es especialmente grave debido a que existe un número elevado de jugadores patológicos en esa franja de edad, y a que el inicio del juego patológico se sitúa en la adolescencia. A pesar de ello, existen pocos estudios realizados. Por ello, el objetivo de esta investigación ha sido analizar el perfi l de jugadores patológicos entre jóvenes y adolescentes de la Federación Española de Jugadores de Azar Rehabilitados (FEJAR). Para ello se ha utilizado una metodología cualitativa a través de grupos de discusión. En el estudio han participado 31 jugadores patológicos de entre 16 y 26 años obtenidos gracias a la FEJAR. Los resultados han mostrado la edad de inicio en el juego, el modo de inicio, el tipo de juego, el uso del dinero así como de las consecuencias del juego en su vida. Este estudio es útil para conocer en profundidad el juego patológico en la adolescencia.Gambling is an impulse control disorder that causes many problems for people who suffer from this. In the case of young people and teenagers this circumstance is especially serious because there is a large number of pathological gamblers in this age group, and since the onset of pathological gambling lies in adolescence. There are, however, few studies on this. The objective of this research was therefore to analyse the profi le of pathological gamblers among young people and teenagers from the Spanish Federation of Rehabilitated Gamblers (FEJAR). To do this we used a qualitative methodology through discussion groups. The study involved 31 young people and teenagers from 16 to 26 years of age and adolescents at risk of problem gambling. The results showed the age of starting to gamble, the game type, the use of money as well as the consequences of gambling in their lives. This study is useful to gain in- depth knowledge on pathological gambling in adolescence

Propiedades psicométricas de la validación Española del Cuestionario de Experiencias Depresivas

Introduction: This study assessed the psychometric prop­erties of the Spanish translation of the Depressive Expe­riences Questionnaire (DEQ). This questionnaire measures two different personality dimensions vulnera­ble to two different subtypes of depression, anaclitic de­pression and introjective depression, respectively. Ob­jectives: The aims of this study are to assess the psy­chometric properties of Spanish translation of Depres­sive Experiences Questionnaire and its relationship with at­tachment styles. Method: The sample (N = 416) con­sisted of undergraduate students with a mean of 27.63 (ST = 10.98) years old. The administration was collec­tive and taken under the supervision of the researcher. Results: The results showed good internal consistency, similar to that of other studies. The findings showed sig­nificant re­lationships with other instruments measuring depressive symptomatology and confirmed the hypothe­sis of a rela­tionship between the DEQ and attachment style. Conclu­sion: The Spanish version of the DEQ could be an in­strument for distinguishing the types of personality vul­nerability to different expressions of de­pression in the Spanish population.Introducción: Este estudio evaluó las propiedades psicométricas del Cuestionario de Experiencias Depresi­vas en español, que mide dos dimensiones de la perso­nalidad relacionadas con dos subtipos diferentes de de­presión, depresión anaclítica y depresión introyectiva, respectivamente. Objetivo: Los objetivos de este estudio son comprobar las propiedades psicométricas del instru­mento en población española y su relación con los estilos de apego. Método: La muestra ha sido de 416 personas y está formada por universitarios con una media de 27.63 (DT = 10.98) años de edad. La evaluación fue colectiva y llevada bajo la supervisión del investigador. Resultados: Los resultados han mostrado una buena consistencia in­terna, similar a la de otros estudios, así como relaciones significativas con otros instrumentos de evaluación de sintomatología depresiva y del apego. Conclusiones: La versión española del DEQ pueden ser un instrumento para distinguir los tipos de personalidad vulnerables a las diferentes expresiones de la depresión en la población española.Introduction: This study assessed the psychometric prop­erties of the Spanish translation of the Depressive Expe­riences Questionnaire (DEQ). This questionnaire measures two different personality dimensions vulnera­ble to two different subtypes of depression, anaclitic de­pression and introjective depression, respectively. Ob­jectives: The aims of this study are to assess the psy­chometric properties of Spanish translation of Depres­sive Experiences Questionnaire and its relationship with at­tachment styles. Method: The sample (N = 416) con­sisted of undergraduate students with a mean of 27.63 (ST = 10.98) years old. The administration was collec­tive and taken under the supervision of the researcher. Results: The results showed good internal consistency, similar to that of other studies. The findings showed sig­nificant re­lationships with other instruments measuring depressive symptomatology and confirmed the hypothe­sis of a rela­tionship between the DEQ and attachment style. Conclu­sion: The Spanish version of the DEQ could be an in­strument for distinguishing the types of personality vul­nerability to different expressions of de­pression in the Spanish population

Improving CGDA execution through genetic algorithms incorporating spatial and velocity constraints

Proceedings of: 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 26-28 April 2017, Coimbra, Portugal.In the Continuous Goal Directed Actions (CGDA) framework, actions are modelled as time series which contain the variations of object and environment features. As robot joint trajectories are not explicitly encoded in CGDA, Evolutionary Algorithms (EA) are used for the execution of these actions. These computations usually require a large number of evaluations. As a consequence of this, these evaluations are performed in a simulated environment, and the computed trajectory is then transferred to the physical robot. In this paper, constraints are introduced in the CGDA framework, as a way to reduce the number of evaluations needed by the system to converge to the optimal robot joint trajectory. Specifically, spatial and velocity constraints are introduced in the framework. Their effects in two different CGDA commonly studied use cases (the “wax” and “paint” actions) are analyzed and compared. The experimental results obtained using these constraints are compared with those obtained with the Steady State Tournament (SST) algorithm used in the original proposal of CGDA. Conclusions extracted from this study depict a high reduction in the required number of evaluations when incorporating spatial constraints. Velocity constraints provide however less promising results, which will be discussed within the context of previous CGDA works.The research leading to these results has received funding from the RoboCity2030-III-CM project (Robtica aplicada a la mejora de la calidad de vida de los ciudadanos. fase Ill; S2013IMIT-2748), funded by Program as de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU, and by a FPU grant funded by Miniesterio de Educacion, Cultura y deporte

Real Evaluations Tractability using Continuous Goal-Directed Actions in Smart City Applications

One of the most important challenges of Smart City Applications is to adapt the system to interact with non-expert users. Robot imitation frameworks aim to simplify and reduce times of robot programming by allowing users to program directly through action demonstrations. In classical robot imitation frameworks, actions are modelled using joint or Cartesian space trajectories. They accurately describe actions where geometrical characteristics are relevant, such as fixed trajectories from one pose to another. Other features, such as visual ones, are not always well represented with these pure geometrical approaches. Continuous Goal-Directed Actions (CGDA) is an alternative to these conventional methods, as it encodes actions as changes of any selected feature that can be extracted from the environment. As a consequence of this, the robot joint trajectories for execution must be fully computed to comply with this feature-agnostic encoding. This is achieved using Evolutionary Algorithms (EA), which usually requires too many evaluations to perform this evolution step in the actual robot. The current strategies involve performing evaluations in a simulated environment, transferring only the final joint trajectory to the actual robot. Smart City applications involve working in highly dynamic and complex environments, where having a precise model is not always achievable. Our goal is to study the tractability of performing these evaluations directly in a real-world scenario. Two different approaches to reduce the number of evaluations using EA, are proposed and compared. In the first approach, Particle Swarm Optimization (PSO)-based methods have been studied and compared within the CGDA framework: naïve PSO, Fitness Inheritance PSO (FI-PSO), and Adaptive Fuzzy Fitness Granulation with PSO (AFFG-PSO).The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Robot imitation through vision, kinesthetic and force features with online adaptation to changing environments

Proceedings of: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1-5 October 2018, Madrid, Spain.Continuous Goal-Directed Actions (CGDA)is a robot imitation framework that encodes actions as the changes they produce on the environment. While it presents numerous advantages with respect to other robot imitation frameworks in terms of generalization and portability, final robot joint trajectories for the execution of actions are not necessarily encoded within the model. This is studied as an optimization problem, and the solution is computed through evolutionary algorithms in simulated environments. Evolutionary algorithms require a large number of evaluations, which had made the use of these algorithms in real world applications very challenging. This paper presents online evolutionary strategies, as a change of paradigm within CGDA execution. Online evolutionary strategies shift and merge motor execution into the planning loop. A concrete online evolutionary strategy, Online Evolved Trajectories (OET), is presented. OET drastically reduces computational times between motor executions, and enables working in real world dynamic environments and/or with human collaboration. Its performance has been measured against Full Trajectory Evolution (FTE)and Incrementally Evolved Trajectories (IET), obtaining the best overall results. Experimental evaluations are performed on the TEO full-sized humanoid robot with “paint” and “iron” actions that together involve vision, kinesthetic and force features.The research leading to these results has received funding from the RoboCity2030-III-CM project (Robotica aplicada a la mejora de la calidad de vida de los ciudadanos. Fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

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

Quick, Stat!: a statistical analysis of the Quick, Draw! Dataset

Proceeding of: 10th EUROSIM Congress (2019), Logroño, La Rioja, Spain, July 1-5, 2019The Quick, Draw! Dataset is a Google dataset with a collection of 50 million drawings, divided in 345 categories, collected from the users of the game Quick, Draw!1. In contrast with most of the existing image datasets, in the Quick, Draw! Dataset, drawings are stored as time series of pencil positions instead of a bitmap matrix composed by pixels. This aspect makes this dataset the largest doodle dataset available at the time. The Quick, Draw! Dataset is presented as a great opportunity to researchers for developing and studying machine learning techniques. Due to the size of this dataset and the nature of its source, there is a scarce of information about the quality of the drawings contained. In this paper a statistical analysis of three of the classes contained in the Quick, Draw! Dataset is depicted: mountain, book and whale. The goal is to give to the reader a first impression of the data collected in this dataset. For the analysis of the quality of the drawings a ClassificationNeuralNetworkwas trained to obtain a classification score. Using this classification score and the parameters provided by the dataset, a statistical analysis of the quality and nature of the drawings contained in this dataset is provided.The research leading to these results has received funding from RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU