Evolutionary Re-Adaptation of Neurocontrollers in Changing Environments

Evolutionary robotics is an interesting novel approach to shape the control system of autonomous robots. This explores issues related to re-adaptation in changed environments of a population of evolved individuals. Experimental studies are reported for genetic evolution of neurocontrollers that have developed the ability to perform homing navigation for battery recharge of miniature mobile robot. It is shown that re-adaptation to important changes in the environment is very rapid and does not disrupt previously acquired knowledge. The results are discussed in relation to the internal representation of the neurocontroller and to the variability within the population

Neuro-Controllers, scalability and adaptation

A Layered Evolution (LE) paradigm based method for the generation of a neuron-controller is developed and verified through simulations and experimentally. It is intended to solve scalability issues in systems with many behavioral modules. Each and every module is a genetically evolved neuro-controller specialized in performing a different task. The main goal is to reach a combination of different basic behavioral elements using different artificial neural-network paradigms concerning mobile robot navigation in an unknown environment. The obtained controller is evaluated over different scenarios in a structured environment, ranging from a detailed simulation model to a real experiment. Finally most important implies are shown through several focuses.Red de Universidades con Carreras en Informática (RedUNCI

Behavioural robustness and the distributed mechanisms hypothesis

A current challenge in neuroscience and systems biology is to better understand properties that allow organisms to exhibit and sustain appropriate behaviours despite the effects of perturbations (behavioural robustness). There are still significant theoretical difficulties in this endeavour, mainly due to the context-dependent nature of the problem. Biological robustness, in general, is considered in the literature as a property that emerges from the internal structure of organisms, rather than being a dynamical phenomenon involving agent-internal controls, the organism body, and the environment. Our hypothesis is that the capacity for behavioural robustness is rooted in dynamical processes that are distributed between agent ‘brain’, body, and environment, rather than warranted exclusively by organisms’ internal mechanisms. Distribution is operationally defined here based on perturbation analyses. Evolutionary Robotics (ER) techniques are used here to construct four computational models to study behavioural robustness from a systemic perspective. Dynamical systems theory provides the conceptual framework for these investigations. The first model evolves situated agents in a goalseeking scenario in the presence of neural noise perturbations. Results suggest that evolution implicitly selects neural systems that are noise-resistant during coupling behaviour by concentrating search in regions of the fitness landscape that retain functionality for goal approaching. The second model evolves situated, dynamically limited agents exhibiting minimalcognitive behaviour (categorization task). Results indicate a small but significant tendency toward better performance under most types of perturbations by agents showing further cognitivebehavioural dependency on their environments. The third model evolves experience-dependent robust behaviour in embodied, one-legged walking agents. Evidence suggests that robustness is rooted in both internal and external dynamics, but robust motion emerges always from the systemin-coupling. The fourth model implements a historically dependent, mobile-object tracking task under sensorimotor perturbations. Results indicate two different modes of distribution, one in which inner controls necessarily depend on a set of specific environmental factors to exhibit behaviour, then these controls will be more vulnerable to perturbations on that set, and another for which these factors are equally sufficient for behaviours. Vulnerability to perturbations depends on the particular distribution. In contrast to most existing approaches to the study of robustness, this thesis argues that behavioural robustness is better understood in the context of agent-environment dynamical couplings, not in terms of internal mechanisms. Such couplings, however, are not always the full determinants of robustness. Challenges and limitations of our approach are also identified for future studies

Intelligent Control Architecture For Motion Learning in Robotics Applications

Abstract: The investigation of this Thesis was focused on how motion abilities can be learned by a robot. The main goal was to design and test a control architecture capable of learning how to properly move different simulated robots, through the use of Arti�cial Intelligence (AI) methods. With this purpose, a simulation environment and a set of simulated robots were created in order to test the control architecture. The robots were constructed with a simple geometry using links and joints. A fuzzy controller was designed to control the motors position. The control architecture design was based on subsumption and some AI methods that allowed the simulated robot to find and learn a set of motions based on targets. These methods were a genetic algorithm (GA) and a set of artificial neural networks (ANN). The GA was used to find the adequate robot movements for an specific target, while the ANNs were used to learn and perform such movements eficiently. The advantage of this approach was that, no knowledge of the environment or robot model is needed. The robot learns how to move its own body in order to achieve a determined task. In addition, the learned motions can be used to achieve complex movement execution in a further research. A set of experiments were performed in the simulator in order to show the performance of the control architecture in every one of its stages. The results showed that the proposed architecture was able to learn and perform basic movements of a robot independently of the environment or the robot defined structure.En esta Tesis, se investiga cómo las habilidades de movimiento en un robot, pueden ser aprendidas de forma automática. El objetivo principal fue dise~nar y probar una arquitectura de control capaz de aprender a mover adecuadamente diferentes robots simulados, mediante el uso de métodos de Inteligencia Artificial (IA). Con este propósito, se dise~no un entorno de simulación y un conjunto de robots simulados con el fin de probar la arquitectura de control. Los robots fueron construidos con una geometría muy simple utilizando enlaces y uniones (actuadores), y un controlador difuso fue dise~nado para controlar la posición de los actuadores. El dise~no de la arquitectura de control se basa en el concepto de subsunción (subsumption) y algunos métodos de IA que permiten al robot simulado determinar y aprender una serie de movimientos basados en objetivos. Los métodos usados son un algoritmo genético (GA) y un conjunto de redes neuronales artificiales (ANN). El GA se utiliza para encontrar los movimientos adecuados que el robot debe realizar para alcanzar un objetico específico, mientras que las redes neuronales se utilizan para aprender y realizar estos movimientos de forma eficiente. La ventaja de este enfoque es que, no es necesario conocer el entorno o tener un modelo del robot, sino que el robot aprende cómo mover su propio cuerpo en un ambiente definido con el fin de lograr una tarea determinada. Además, en una posterior investigación, es posible utilizar los movimientos aprendidos para realizar movimientos o tareas más complejas con los robots. Un conjunto de experimentos se llevaron a cabo en el simulador para mostrar el desempe~no de la arquitectura de control en cada una de sus etapas. Los resultados muestran que la arquitectura propuesta es capaz de aprender y realizar los movimientos del robot independientemente del medio ambiente o la estructura definida del robot.Maestrí