Locomotion Gait Optimization For Modular Robots; Coevolving Morphology and Control

This study aims at providing a control-learning framework capable of generating optimal locomotion patterns for the modular robots. The key ideas are firstly to provide a generic control structure that can be well-adapted for the different morphologies and secondly to exploit and coevolve both morphology and control aspects. A generic framework combining robot morphology, control and environment and on the top of them optimization and evolutionary algorithms are presented. The details of the components and some of the preliminary results are discussed. (C) Selection and peer-review under responsibility of FET11 conference organizers and published by Elsevier B.V

Behavior finding: Morphogenetic Designs Shaped by Function

Evolution has shaped an incredible diversity of multicellular living organisms, whose complex forms are self-made through a robust developmental process. This fundamental combination of biological evolution and development has served as an inspiration for novel engineering design methodologies, with the goal to overcome the scalability problems suffered by classical top-down approaches. Top-down methodologies are based on the manual decomposition of the design into modular, independent subunits. In contrast, recent computational morphogenetic techniques have shown that they were able to automatically generate truly complex innovative designs. Algorithms based on evolutionary computation and artificial development have been proposed to automatically design both the structures, within certain constraints, and the controllers that optimize their function. However, the driving force of biological evolution does not resemble an enumeration of design requirements, but much rather relies on the interaction of organisms within the environment. Similarly, controllers do not evolve nor develop separately, but are woven into the organism’s morphology. In this chapter, we discuss evolutionary morphogenetic algorithms inspired by these important aspects of biological evolution. The proposed methodologies could contribute to the automation of processes that design “organic” structures, whose morphologies and controllers are intended to solve a functional problem. The performance of the algorithms is tested on a class of optimization problems that we call behavior-finding. These challenges are not explicitly based on morphology or controller constraints, but only on the solving abilities and efficacy of the design. Our results show that morphogenetic algorithms are well suited to behavior-finding

Challenges in the Locomotion of Self-Reconfigurable Modular Robots

Self-Reconfigurable Modular Robots (SRMRs) are assemblies of autonomous robotic units, referred to as modules, joined together using active connection mechanisms. By changing the connectivity of these modules, SRMRs are able to deliberately change their own shape in order to adapt to new environmental circumstances. One of the main motivations for the development of SRMRs is that conventional robots are limited in their capabilities by their morphology. The promise of the field of self-reconfigurable modular robotics is to design robots that are robust, self-healing, versatile, multi-purpose, and inexpensive. Despite significant efforts by numerous research groups worldwide, the potential advantages of SRMRs have yet to be realized. A high number of degrees of freedom and connectors make SRMRs more versatile, but also more complex both in terms of mechanical design and control algorithms. Scalability issues affect these robots in terms of hardware, low-level control, and high-level planning. In this thesis we identify and target three major challenges: (i) Hardware design; (ii) Planning and control; and, (iii) Application challenges. To tackle the hardware challenges we redesigned and manufactured the Self-Reconfigurable Modular Robot Roombots to meet desired requirements and characteristics. We explored in detail and improved two major mechanical components of an SRMR: the actuation and the connection mechanisms. We also analyzed the use of compliant extensions to increase locomotion performance in terms of locomotion speed and power consumption. We contributed to the control challenge by developing new methods that allow an arbitrary SRMR structure to learn to locomote in an efficient way. We defined a novel bio-inspired locomotion-learning framework that allows the quick and reliable optimization of new gaits after a morphological change due to self-reconfiguration or human construction. In order to find new suitable application scenarios for SRMRs we envision the use of Roombots modules to create Self-Reconfigurable Robotic Furniture. As a first step towards this vision, we explored the use and control of Plug-n-Play Robotic Elements that can augment existing pieces of furniture and create new functionalities in a household to improve quality of life

A Comparative Analysis of Darwinian Asexual and Sexual Reproduction in Evolutionary Robotics

Evolutionary Robotics systems draw inspiration from natural evolution to solve the problem of robot design. A key moment in the evolutionary process is reproduction, when the genotype of one or more parents is inherited by their offspring. Existent approaches have used both sexual and asexual reproduction but a comparison between the two is still missing. In this work, we study the effects of sexual and asexual reproduction on the controllers of an Evolutionary Robotics system. In our system, both morphologies and controllers are jointly evolved to solve two separate tasks. We adopt the Triangle of Life framework, in which the controllers go through a phase of learning before reproduction. Using extensive simulations we show that sexual reproduction of the robots' brains is preferable over asexual reproduction as it obtains better robots in terms of fitness. Moreover, we show that sexually reproducing robots present different morphologies and behaviors than the asexually reproducing ones, even though the reproduction mechanism only affects their brains. Finally, we study the effects of the reproduction mechanism on the robots' learning capabilities. By measuring the difference between the inherited and the learned brain we find that robots that evolved using sexual reproduction have better inherited brains and are also better learners

Improving Scalability of Evolutionary Robotics with Reformulation

Creating systems that can operate autonomously in complex environments is a challenge for contemporary engineering techniques. Automatic design methods offer a promising alternative, but so far they have not been able to produce agents that outperform manual designs. One such method is evolutionary robotics. It has been shown to be a robust and versatile tool for designing robots to perform simple tasks, but more challenging tasks at present remain out of reach of the method. In this thesis I discuss and attack some problems underlying the scalability issues associated with the method. I present a new technique for evolving modular networks. I show that the performance of modularity-biased evolution depends heavily on the morphology of the robot’s body and present a new method for co-evolving morphology and modular control. To be able to reason about the new technique I develop reformulation framework: a general way to describe and reason about metaoptimization approaches. Within this framework I describe a new heuristic for developing metaoptimization approaches that is based on the technique for co-evolving morphology and modularity. I validate the framework by applying it to a practical task of zero-g autonomous assembly of structures with a fleet of small robots. Although this work focuses on the evolutionary robotics, methods and approaches developed within it can be applied to optimization problems in any domain

The Environment and Body-Brain Complexity

An open question for both natural and artificial evolutionary systems is how, and under what environmental and evolutionary conditions complexity evolves. This study investigates the impact of increasingly complex task environments on the evolution of robot complexity. Specifically, the impact of evolving body-brain couplings on locomotive task performance, where robot evolution was directed by either body-brain exploration (novelty search) or objective-based (fitness function) evolutionary search. Results indicated that novelty search enabled the evolution of increased robot body-brain complexity and efficacy given specific environment conditions. The key contribution is thus the demonstration that body-brain exploration is suitable for evolving robot complexity that enables high fitness robots in specific environments

Improving Soft Snake Robot Locomotion Through Targeted Environmental Interactions Using Artificial Snake Skin

This dissertation outlines the design and development of the �first fully-soft, snake robot and its snake-inspired skin. Soft robotics takes advantage of soft materials to, among other things, improve robot interactions with complex, unstructured environments. Due to the interplay between the soft material and the environment, minor tweaks to the morphological design of the robot can produce major changes in behavior when using the same control input. The research goal of this dissertation was to determine how the locomotion a soft snake robot, using a lateral undulation gait, can be improved by targeting a specific environmental interaction through the confluence of body design, gait design, and interfacial mechanism design. Understanding how these three areas of design can affect one another is key in developing robots that are adaptable in a range of environments. Each design area is addressed in a chapter of this dissertation to illustrate how changes to one area propagate to others, and how that can be an advantage to improving the locomotion of a soft robot. Chapter 3 examines how the body design of the robot changes its locomotion capabilities in granular media, focusing on interactions between the body and the ridges formed in the media. Chapter 4 illustrates how improvements to the gait can also be driven by interactions between the robot's body and the granular media. The design and implementation of an interfacial mechanism to further improve locomotion is described in Chapter 5. Kirigami, a Japanese art form involving the patterning of cuts in thin materials, is used to create a snake-inspired skin. The skin design targets directional friction, a morphological characteristic vital to snake locomotion in two axes. Most skins implemented for snake robots focus only on the longitudinal axis for creating directional friction. However, lateral undulation, the gait employed throughout this work, requires a significant lateral resistance to successfully create locomotion. This interfacial mechanism is designed speci�cally for the kinematics of the soft actuators as well as the production of directional friction in two axes, which required the creation of a new set of radial kirigami lattices. Each chapter demonstrates how improvements to locomotion can come from designing the morphological characteristics of the robot alongside the development of a gait and interfacial mechanisms by targeting specific, bioinspired interactions between the robot and the environment. The �final iteration of system resulted in a soft robot and it's snake-inspired skin with a 530% improvement in velocity over the original robot with no skin. The main contributions of this dissertation are: 1. The development of the �first fully-soft snake robot. 2. A skin for lateral undulation with two axes of directional friction 3. A set of new kirigami lattice structures that can be used for bending actuators 4. A framework in which to investigate bioinspired design of robots in three areas of design: morphology, gait, and interfacial mechanisms

Otimização de locomoção bípede

Dissertação de mestrado integrado em Engenharia BiomédicaAtualmente verifica-se um crescimento exponencial a nível de desenvolvimento de sistemas robóticos móveis havendo um esforço para criar sistemas com propriedades mais eficientes e adaptáveis às exigências do ambiente de trabalho. Neste contexto, têm havido uma preocupação acrescida em desenvolver melhores sistemas de locomoção quer seja locomoção por rodas quer seja por pernas (bípede, quadrúpede e hexapode). Esta dissertação foca-se na otimização da locomoção bípede a qual é uma área que tem sido alvo de grande atenção uma vez que esta é uma área da robótica que ainda necessita de progredir no sentido de conseguir finalmente uma locomoção tão eficiente como a marcha humana. Deste modo, a elaboração deste trabalho teve como objetivos principais a criação de uma estratégia de otimização que combinasse a geração de padrões de movimento através de geradores centrais de padrões (CPGs) com um algoritmo de otimização evolucionário (Non-Dominated Sorting Genetic Algorithm ll). Essa estratégia implicou a determinação de objetivos que correspondem a características da locomoção bípede e que foram otimizados, sendo eles o deslocamento frontal, a altura a que o pé levanta, a força de impacto entre os pés e o chão e a posição do centro de massa. Os resultados foram obtidos a partir de simulações na plataforma Webots para o robô bípede Darwin-OP. Neste contexto, os resultados foram muito satisfatórios uma vez que o algoritmo foi capaz de gerar locomoção estável e os objetivos propostos foram otimizados. Foi feito também um estudo de sensibilidade que determinou a existência de parâmetros de CPGs que apresentam uma forte correlação positiva com as funções objetivos. Assim, os parâmetros Acompasso, frequência ω e ORoll influenciam fortemente o deslocamento e a força de impacto e o parâmetro AhPitch influencia a altura a que o pé levanta. No futuro seria pertinente aplicar o algoritmo elaborado num robô bípede real e conferir se consegue gerar uma locomoção eficiente em condições reais.Presently there is an exponential increase on the level of development of mobile robotic systems and so there is an effort to create systems with properties more efficient and adaptable to the demands of the work environment. In this context, there has been a heightened concern in developing better systems of locomotion either by wheels either by legs (bipedal, 4-legged or 6-legged). This dissertation focuses on the optimization of bipedal locomotion which is an area that has been the subject of much attention since this is an area of robotics that still needs to make progress towards finally achieving locomotion as efficient as the human gait. Thus, this work aimed to create an optimization strategy that combines the generation of movement patterns through central pattern generators (CPGs) with an evolutionary optimization algorithm (Non-Dominated Sorting Genetic Algorithm II). This strategy involved the determination of objectives that correspond to characteristics of bipedal locomotion and that have been optimized, namely the frontal displacement, the ground clearance, the impact force between the foot and the ground and the position of the center of mass. The results were obtained from simulations in Webots platform for the bipedal robot Darwin-OP. The results were very satisfactory since the algorithm was able to generate stable locomotion and the proposed objectives were optimized. We also made a sensitivity analysis that determined the existence of CPGs parameters that exhibit a strong positive correlation with the objective functions. Thus, the parameters Acompasso, the frequency ω and ORoll strongly influence the impact force and displacement as well as AhPitch influences the height to which the foot rises. In the future it would be appropriate to apply the developed algorithm in a real biped robot and check if it can generate an efficient locomotion in real conditions

Simultaneous incremental neuroevolution of motor control, navigation and object manipulation in 3D virtual creatures

There have been numerous attempts to develop 3D virtual agents by applying evolutionary processes to populations that exist in a realistic physical simulation. Whilst often contributing useful knowledge, no previous work has demonstrated the capacity to evolve a sequence of increasingly complex behaviours in a single, unified system. This thesis has this demonstration as its primary aim. A rigorous exploration of one aspect of incremental artificial evolution was carried out to understand how subtask presentations affect the whole-task generalisation performance of evolved, fixed-morphology 3D agents. Results from this work led to the design of an environment–body–control architecture that can be used as a base for evolving multiple behaviours incrementally. A simulation based on this architecture with a more complex environment was then developed and explored. This system was then adapted to include elements of physical manipulation as a first step toward a fully physical virtual creature environment demonstrating advanced evolved behaviours. The thesis demonstrates that incremental evolutionary systems can be subject to problems of forgetting and loss of gradient, and that different complexification strategies have a strong bearing on the management of these issues. Presenting successive generations of the population to a full range of objective functions (covering and revisiting the range of complexity) outperforms straightforward linear or direct presentations, establishing a more robust approach to the evolution of naturalistic embodied agents. When combining this approach with a bespoke control architecture in a problem requiring reactive and deliberative behaviours, we see results that not only demonstrate success at the tasks, but also show a variety of intricate behaviours being used. This is the first ever example of the simultaneous incremental evolution in 3D of composite behaviours more complex than simple locomotion. Finally, the architecture demonstrably supports extension to manipulation in a feedback control task. Given the problem-agnostic controller architecture, these results indicate a system with potential for discovering yet more advanced behaviours in yet more complex environments

Adaptive networks for robotics and the emergence of reward anticipatory circuits

Currently the central challenge facing evolutionary robotics is to determine how best to extend the range and complexity of behaviour supported by evolved neural systems. Implicit in the work described in this thesis is the idea that this might best be achieved through devising neural circuits (tractable to evolutionary exploration) that exhibit complementary functional characteristics. We concentrate on two problem domains; locomotion and sequence learning. For locomotion we compare the use of GasNets and other adaptive networks. For sequence learning we introduce a novel connectionist model inspired by the role of dopamine in the basal ganglia (commonly interpreted as a form of reinforcement learning). This connectionist approach relies upon a new neuron model inspired by notions of energy efficient signalling. Two reward adaptive circuit variants were investigated. These were applied respectively to two learning problems; where action sequences are required to take place in a strict order, and secondly, where action sequences are robust to intermediate arbitrary states. We conclude the thesis by proposing a formal model of functional integration, encompassing locomotion and sequence learning, extending ideas proposed by W. Ross Ashby. A general model of the adaptive replicator is presented, incoporating subsystems that are tuned to continuous variation and discrete or conditional events. Comparisons are made with Ross W. Ashby's model of ultrastability and his ideas on adaptive behaviour. This model is intended to support our assertion that, GasNets (and similar networks) and reward adaptive circuits of the type presented here, are intrinsically complementary. In conclusion we present some ideas on how the co-evolution of GasNet and reward adaptive circuits might lead us to significant improvements in the synthesis of agents capable of exhibiting complex adaptive behaviour