Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Efficient Evolution of Neural Networks

This thesis addresses the study of evolutionary methods for the synthesis of neural network controllers. Chapter 1 introduces the research area, reviews the state of the art, discusses promising research directions, and presents the two major scientific objectives of the thesis. The first objective, which is covered in Chapter 2, is to verify the efficacy of some of the most promising neuro-evolutionary methods proposed in the literature, including two new methods that I elaborated. This has been made by designing extended version of the double-pole balancing problem, which can be used to more properly benchmark alternative algorithms, by studying the effect of critical parameters, and by conducting several series of comparative experiments. The obtained results indicate that some methods perform better with respect to all the considered criteria, i.e. performance, robustness to environmental variations and capability to scale-up to more complex problems. The second objective, which is targeted in Chapter 3, consists in the design of a new hybrid algorithm that combines evolution and learning by demonstration. The combination of these two processes is appealing since it potentially allows the adaptive agent to exploit a richer training feedback constituted by both a scalar performance objective (reinforcement signal or fitness measure) and a detailed description of a suitable behaviour (demonstration). The proposed method has been successfully evaluated on two qualitatively different robotic problems. Chapter 4 summarizes the results obtained and describes the major contributions of the thesis

Novel approaches to cooperative coevolution of heterogeneous multiagent systems

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2017Heterogeneous multirobot systems are characterised by the morphological and/or behavioural heterogeneity of their constituent robots. These systems have a number of advantages over the more common homogeneous multirobot systems: they can leverage specialisation for increased efficiency, and they can solve tasks that are beyond the reach of any single type of robot, by combining the capabilities of different robots. Manually designing control for heterogeneous systems is a challenging endeavour, since the desired system behaviour has to be decomposed into behavioural rules for the individual robots, in such a way that the team as a whole cooperates and takes advantage of specialisation. Evolutionary robotics is a promising alternative that can be used to automate the synthesis of controllers for multirobot systems, but so far, research in the field has been mostly focused on homogeneous systems, such as swarm robotics systems. Cooperative coevolutionary algorithms (CCEAs) are a type of evolutionary algorithm that facilitate the evolution of control for heterogeneous systems, by working over a decomposition of the problem. In a typical CCEA application, each agent evolves in a separate population, with the evaluation of each agent depending on the cooperation with agents from the other coevolving populations. A CCEA is thus capable of projecting the large search space into multiple smaller, and more manageable, search spaces. Unfortunately, the use of cooperative coevolutionary algorithms is associated with a number of challenges. Previous works have shown that CCEAs are not necessarily attracted to the global optimum, but often converge to mediocre stable states; they can be inefficient when applied to large teams; and they have not yet been demonstrated in real robotic systems, nor in morphologically heterogeneous multirobot systems. In this thesis, we propose novel methods for overcoming the fundamental challenges in cooperative coevolutionary algorithms mentioned above, and study them in multirobot domains: we propose novelty-driven cooperative coevolution, in which premature convergence is avoided by encouraging behavioural novelty; and we propose Hyb-CCEA, an extension of CCEAs that places the team heterogeneity under evolutionary control, significantly improving its scalability with respect to the team size. These two approaches have in common that they take into account the exploration of the behaviour space by the evolutionary process. Besides relying on the fitness function for the evaluation of the candidate solutions, the evolutionary process analyses the behaviour of the evolving agents to improve the effectiveness of the evolutionary search. The ultimate goal of our research is to achieve general methods that can effectively synthesise controllers for heterogeneous multirobot systems, and therefore help to realise the full potential of this type of systems. To this end, we demonstrate the proposed approaches in a variety of multirobot domains used in previous works, and we study the application of CCEAs to new robotics domains, including a morphological heterogeneous system and a real robotic system.Fundação para a Ciência e a Tecnologia (FCT, PEst-OE/EEI/LA0008/2011

Open-ended Search through Minimal Criterion Coevolution

Search processes guided by objectives are ubiquitous in machine learning. They iteratively reward artifacts based on their proximity to an optimization target, and terminate upon solution space convergence. Some recent studies take a different approach, capitalizing on the disconnect between mainstream methods in artificial intelligence and the field\u27s biological inspirations. Natural evolution has an unparalleled propensity for generating well-adapted artifacts, but these artifacts are decidedly non-convergent. This new class of non-objective algorithms induce a divergent search by rewarding solutions according to their novelty with respect to prior discoveries. While the diversity of resulting innovations exhibit marked parallels to natural evolution, the methods by which search is driven remain unnatural. In particular, nature has no need to characterize and enforce novelty; rather, it is guided by a single, simple constraint: survive long enough to reproduce. The key insight is that such a constraint, called the minimal criterion, can be harnessed in a coevolutionary context where two populations interact, finding novel ways to satisfy their reproductive constraint with respect to each other. Among the contributions of this dissertation, this approach, called minimal criterion coevolution (MCC), is the primary (1). MCC is initially demonstrated in a maze domain (2) where it evolves increasingly complex mazes and solutions. An enhancement to the initial domain (3) is then introduced, allowing mazes to expand unboundedly and validating MCC\u27s propensity for open-ended discovery. A more natural method of diversity preservation through resource limitation (4) is introduced and shown to maintain population diversity without comparing genetic distance. Finally, MCC is demonstrated in an evolutionary robotics domain (5) where it coevolves increasingly complex bodies with brain controllers to achieve principled locomotion. The overall benefit of these contributions is a novel, general, algorithmic framework for the continual production of open-ended dynamics without the need for a characterization of behavioral novelty

Engineering evolutionary control for real-world robotic systems

Evolutionary Robotics (ER) is the field of study concerned with the application of evolutionary computation to the design of robotic systems. Two main issues have prevented ER from being applied to real-world tasks, namely scaling to complex tasks and the transfer of control to real-robot systems. Finding solutions to complex tasks is challenging for evolutionary approaches due to the bootstrap problem and deception. When the task goal is too difficult, the evolutionary process will drift in regions of the search space with equally low levels of performance and therefore fail to bootstrap. Furthermore, the search space tends to get rugged (deceptive) as task complexity increases, which can lead to premature convergence. Another prominent issue in ER is the reality gap. Behavioral control is typically evolved in simulation and then only transferred to the real robotic hardware when a good solution has been found. Since simulation is an abstraction of the real world, the accuracy of the robot model and its interactions with the environment is limited. As a result, control evolved in a simulator tends to display a lower performance in reality than in simulation. In this thesis, we present a hierarchical control synthesis approach that enables the use of ER techniques for complex tasks in real robotic hardware by mitigating the bootstrap problem, deception, and the reality gap. We recursively decompose a task into sub-tasks, and synthesize control for each sub-task. The individual behaviors are then composed hierarchically. The possibility of incrementally transferring control as the controller is composed allows transferability issues to be addressed locally in the controller hierarchy. Our approach features hybridity, allowing different control synthesis techniques to be combined. We demonstrate our approach in a series of tasks that go beyond the complexity of tasks where ER has been successfully applied. We further show that hierarchical control can be applied in single-robot systems and in multirobot systems. Given our long-term goal of enabling the application of ER techniques to real-world tasks, we systematically validate our approach in real robotic hardware. For one of the demonstrations in this thesis, we have designed and built a swarm robotic platform, and we show the first successful transfer of evolved and hierarchical control to a swarm of robots outside of controlled laboratory conditions.A Robótica Evolutiva (RE) é a área de investigação que estuda a aplicação de computação evolutiva na conceção de sistemas robóticos. Dois principais desafios têm impedido a aplicação da RE em tarefas do mundo real: a dificuldade em solucionar tarefas complexas e a transferência de controladores evoluídos para sistemas robóticos reais. Encontrar soluções para tarefas complexas é desafiante para as técnicas evolutivas devido ao bootstrap problem e à deception. Quando o objetivo é demasiado difícil, o processo evolutivo tende a permanecer em regiões do espaço de procura com níveis de desempenho igualmente baixos, e consequentemente não consegue inicializar. Por outro lado, o espaço de procura tende a enrugar à medida que a complexidade da tarefa aumenta, o que pode resultar numa convergência prematura. Outro desafio na RE é a reality gap. O controlo robótico é tipicamente evoluído em simulação, e só é transferido para o sistema robótico real quando uma boa solução tiver sido encontrada. Como a simulação é uma abstração da realidade, a precisão do modelo do robô e das suas interações com o ambiente é limitada, podendo resultar em controladores com um menor desempenho no mundo real. Nesta tese, apresentamos uma abordagem de síntese de controlo hierárquica que permite o uso de técnicas de RE em tarefas complexas com hardware robótico real, mitigando o bootstrap problem, a deception e a reality gap. Decompomos recursivamente uma tarefa em sub-tarefas, e sintetizamos controlo para cada subtarefa. Os comportamentos individuais são então compostos hierarquicamente. A possibilidade de transferir o controlo incrementalmente à medida que o controlador é composto permite que problemas de transferibilidade possam ser endereçados localmente na hierarquia do controlador. A nossa abordagem permite o uso de diferentes técnicas de síntese de controlo, resultando em controladores híbridos. Demonstramos a nossa abordagem em várias tarefas que vão para além da complexidade das tarefas onde a RE foi aplicada. Também mostramos que o controlo hierárquico pode ser aplicado em sistemas de um robô ou sistemas multirobô. Dado o nosso objetivo de longo prazo de permitir o uso de técnicas de RE em tarefas no mundo real, concebemos e desenvolvemos uma plataforma de robótica de enxame, e mostramos a primeira transferência de controlo evoluído e hierárquico para um exame de robôs fora de condições controladas de laboratório.This work has been supported by the Portuguese Foundation for Science and Technology (Fundação para a Ciência e Tecnologia) under the grants SFRH/BD/76438/2011, EXPL/EEI-AUT/0329/2013, and by Instituto de Telecomunicações under the grant UID/EEA/50008/2013

Objective versus Non-Objective Search in Evolving Morphologically Robust Robot Controllers

This study evaluates objective versus non-objective based evolutionary search methods for behavior evolution in robot teams. The goal is to evaluate the morphological robustness of evolved controllers, where controllers are evolved for specific robot sensory-motor configurations (morphologies) but must continue to function as these morphologies degrade. Robots use artificial neural network controllers where behavior evolution is directed by developmental neuro-evolution. Guiding evolutionary controller design we use objective (fitness function) versus nonobjective (novelty) search. The former optimizes for behavioral fitness and the latter for behavioral novelty. These methods are evaluated across varying robot morphologies and increasing task complexity. Results indicate that novelty search yields no benefits over objective search, in terms of evolving morphologically robust controllers. That is, both novelty and objective search evolve team controllers that are morphologically robust given varying robot morphologies and increasing task complexity. Results thus suggest behavioral diversity methods such as novelty search may not be suitable for generating robot behaviors that can continue functioning given changing robot morphologies, for example, due to damaged or disabled sensors and actuators