On-line, On-board Evolution of Robot Controllers

International audienceThis paper reports on a feasibility study into the evolution of robot controllers during the actual operation of robots (on-line), using only the computational resources within the robots themselves (on-board). We identify the main challenges that these restrictions imply and propose mechanisms to handle them. The resulting algorithm is evaluated in a hybrid system, using the actual robots' processors interfaced with a simulator that represents the environment. The results show that the proposed algorithm is indeed feasible and the particular problems we encountered during this study give hints for further research

odNEAT: an algorithm for decentralised online evolution of robotic controllers

Online evolution gives robots the capacity to learn new tasks and to adapt to changing environmental conditions during task execution. Previous approaches to online evolution of neural controllers are typically limited to the optimisation of weights in networks with a prespecified, fixed topology. In this article, we propose a novel approach to online learning in groups of autonomous robots called odNEAT. odNEAT is a distributed and decentralised neuroevolution algorithm that evolves both weights and network topology. We demonstrate odNEAT in three multirobot tasks: aggregation, integrated navigation and obstacle avoidance, and phototaxis. Results show that odNEAT approximates the performance of rtNEAT, an efficient centralised method, and outperforms IM-( mu + 1), a decentralised neuroevolution algorithm. Compared with rtNEAT and IM( mu + 1), odNEAT's evolutionary dynamics lead to the synthesis of less complex neural controllers with superior generalisation capabilities. We show that robots executing odNEAT can display a high degree of fault tolerance as they are able to adapt and learn new behaviours in the presence of faults. We conclude with a series of ablation studies to analyse the impact of each algorithmic component on performance.info:eu-repo/semantics/submittedVersio

Online evolution of robot behaviour

Tese de mestrado em Engenharia Informática (Interação e Conhecimento), apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2012In this dissertation, we propose and evaluate two novel approaches to the online synthesis of neural controllers for autonomous robots. The first approach is odNEAT, an online, distributed, and decentralized version of NeuroEvolution of Augmenting Topologies (NEAT). odNEAT is an algorithm for online evolution in groups of embodied agents such as robots. In odNEAT, agents have to solve the same task, either individually or collectively. While previous approaches to online evolution of neural controllers have been limited to the optimization of weights, odNEAT evolves both weights and network topology. We demonstrate odNEAT through a series of simulation-based experiments in which a group of e-puck-like robots must perform an aggregation task. Our results show that robots are capable of evolving effective aggregation strategies and that sustainable behaviours evolve quickly. We show that odNEAT approximates the performance of rtNEAT, a similar but centralized method. We also analyze the contribution of each algorithmic component on the performance through a series of ablation studies. In the second approach, we extend our previous method and combine online evolution of weights and network topology (odNEAT) with neuromodulated learning. We demonstrate our method through a series of experiments in which a group of simulated robots must perform a dynamic concurrent foraging task. In this task, scattered food items periodically change their nutritive value or become poisonous. Our results show that when neuromodulated learning is employed, neural controllers are synthesized faster than by odNEAT alone. We demonstrate that the online evolutionary process is capable of generating controllers that adapt to the periodic task changes. We evaluate the performance both in a single robot setup and in a multirobot setup. An analysis of the evolved networks shows that they are characterized by specialized modulatory neurons that exclusively regulate online learning in the output neurons

Optimización en sistemas multi-robot mediante embodied evolution

Programa Oficial de Doutoramento en Computación. 5009V01[Resumen] En esta tesis se ha desarrollado una versión del algoritmo evolutivo Embodied Evolution (EE) que generaliza las existentes en el campo, con el objetivo de avanzar en la estandarización de este paradigma de forma que pueda ser estudiado de manera formal, y así conocer sus fortalezas y limitaciones. El algoritmo desarrollado en esta tesis se ha denominado “canónico”porque se ha diseñado tras el análisis detallado de los procesos básicos comunes a las diferentes variantes dentro de Embodied Evolution, de modo que únicamente contiene dichos procesos intrínsecos, y una parametrización de los mismos lo más general posible. El funcionamiento de este algoritmo can´onico de Embodied Evolution se ha analizado en un conjunto de funciones teóricas representativas de los espacios de búsqueda en optimización colectiva, sobre los que se ha llevado a cabo un análisis de sensibilidad exhaustivo. Finalmente, las conclusiones del análisis teórico se han validado en una tarea real en la cual se ha podido comprobar la validez de la aproximación a la hora de optimizar la coordinación emergente del sistema multi-robot, tanto a nivel de rendimiento como a nivel de organización automática en especies, una propiedad fundamental de este paradigma.[Resumo] Nesta tese doutoral desenvolveuse unha version do algoritmo evolutivo Embodied Evolution (EE) que xeneraliza as existentes no campo, co obxectivo de avanzar na estandarización deste paradigma, de forma que poida ser estudado de maneira formal, e así coñecer as súas fortalezas e limitacións. O algoritmo desenvolvido nesta tese denominouse algoritmo canónico porque foi deseñado tras analizar detalladamente os procesos básicos comúns ás diferentes variantes dentro de Embodied Evolution, de modo que únicamente contén estes procesos intrínsecos, e una parametrización dos mesmos o máis xeral posible. O funcionamiento do algoritmo canónico de Embodied Evolution analizouse nun conxunto de funcións teóricas representativas dos espazos de búsqueda en optimización colectiva, sobre os que se realizou unha análise de sensibilidade exhaustiva. Finalmente, as conclusions da análise teorica validouse nunha tarefa real, na que se puido comprobar a validez da aproximación á hora de optimizar a coordinación emerxente do sistema multi-robot, tanto a nivel de rendemento como a nivel de organización automática en especies, unha propiedade fundamental neste paradigma.[Abstract] In this PhD thesis, a version of the Embodied Evolution (EE) algorithm has been developed that generalizes the existing version on the field, with the goal of advancing in the standardization of this paradigm such that it could be studied in a formal way and discover its strengths and limitations. The developed algorithm has been named “canonical” because it was designed after analyzing in detail the basic common processes of the di↵erent variants in Embodied Evolution, in a way that only contains the intrinsic processes and a parametrization as general as possible. The operation of this Embodied Evolution canonical algorithm has been analyzed in a set of theoretic functions that represent the di↵erent search landscapes in collective optimization, in which a sensibility analysis has been performed. Finally, the conclusions of the theoretic analysis has been validated in a real task in which the validity of the approximation has been confirmed through the successful optimization of the emergent coordination of the multi-robot system, both in performance and in automatic organization in species, a fundamental property in this paradigm

Never Too Old To Learn: On-line Evolution of Controllers in Swarm- and Modular Robotics

Eiben, A.E. [Promotor