Evolving Modularity in Soft Robots Through an Embodied and Self-Organizing Neural Controller

Modularity is a desirable property for embodied agents, as it could foster their suitability to different domains by disassembling them into transferable modules that can be reassembled differently. We focus on a class of embodied agents known as voxel-based soft robots (VSRs). They are aggregations of elastic blocks of soft material; as such, their morphologies are intrinsically modular. Nevertheless, controllers used until now for VSRs act as abstract, disembodied processing units: Disassembling such controllers for the purpose of module transferability is a challenging problem. Thus, the full potential of modularity for VSRs still remains untapped. In this work, we propose a novel self-organizing, embodied neural controller for VSRs. We optimize it for a given task and morphology by means of evolutionary computation: While evolving, the controller spreads across the VSR morphology in a way that permits emergence of modularity. We experimentally investigate whether such a controller (i) is effective and (ii) allows tuning of its degree of modularity, and with what kind of impact. To this end, we consider the task of locomotion on rugged terrains and evolve controllers for two morphologies. Our experiments confirm that our self-organizing, embodied controller is indeed effective. Moreover, by mimicking the structural modularity observed in biological neural networks, different levels of modularity can be achieved. Our findings suggest that the self-organization of modularity could be the basis for an automatic pipeline for assembling, disassembling, and reassembling embodied agents

Harnessing the Power of Collective Intelligence: the Case Study of Voxel-based Soft Robots

The field of Evolutionary Robotics (ER) is concerned with the evolution of artificial agents---robots. Albeit groundbreaking, progress in the field has recently stagnated. In the research community, there is a strong feeling that a paradigm change has become necessary to disentangle ER. In particular, a solution has emerged from ideas from Collective Intelligence (CI). In CI---which has many relevant examples in nature---behavior emerges from the interaction between several components. In the absence of central intelligence, collective systems are usually more adaptable. In this thesis, we set out to harness the power of CI, focusing on the case study of simulated Voxel-based Soft Robots (VSRs): they are aggregations of homogeneous and soft cubic blocks that actuate by altering their volume. We investigate two axes. First, the morphologies of VSRs are intrinsically modular and an ideal substrate for CI; nevertheless, controllers employed until now do not take advantage of such modularity. Our results prove that VSRs can truly be controlled by the CI of their modules. Second, we investigate the spatial and time scales of CI. In particular, we evolve a robot to detect its global body properties given only local information processing, and, in a different study, generalize better to unseen environmental conditions through Hebbian learning. We also consider how evolution and learning interact in VSRs. Looking beyond VSRs, we propose a novel soft robot formalism that more closely resembles natural tissues and blends local with global actuation.The field of Evolutionary Robotics (ER) is concerned with the evolution of artificial agents---robots. Albeit groundbreaking, progress in the field has recently stagnated. In the research community, there is a strong feeling that a paradigm change has become necessary to disentangle ER. In particular, a solution has emerged from ideas from Collective Intelligence (CI). In CI---which has many relevant examples in nature---behavior emerges from the interaction between several components. In the absence of central intelligence, collective systems are usually more adaptable. In this thesis, we set out to harness the power of CI, focusing on the case study of simulated Voxel-based Soft Robots (VSRs): they are aggregations of homogeneous and soft cubic blocks that actuate by altering their volume. We investigate two axes. First, the morphologies of VSRs are intrinsically modular and an ideal substrate for CI; nevertheless, controllers employed until now do not take advantage of such modularity. Our results prove that VSRs can truly be controlled by the CI of their modules. Second, we investigate the spatial and time scales of CI. In particular, we evolve a robot to detect its global body properties given only local information processing, and, in a different study, generalize better to unseen environmental conditions through Hebbian learning. We also consider how evolution and learning interact in VSRs. Looking beyond VSRs, we propose a novel soft robot formalism that more closely resembles natural tissues and blends local with global actuation

Generating walking behaviours in legged robots

Many legged robots have boon built with a variety of different abilities, from running to liopping to climbing stairs. Despite this however, there has been no consistency of approach to the problem of getting them to walk. Approaches have included breaking down the walking step into discrete parts and then controlling them separately, using springs and linkages to achieve a passive walking cycle, and even working out the necessary movements in simulation and then imposing them on the real robot. All of these have limitations, although most were successful at the task for which they were designed. However, all of them fall into one of two categories: either they alter the dynamics of the robots physically so that the robot, whilst very good at walking, is not as general purpose as it once was (as with the passive robots), or they control the physical mechanism of the robot directly to achieve their goals, and this is a difficult task.In this thesis a design methodology is described for building controllers for 3D dynam¬ ically stable walking, inspired by the best walkers and runners around — ourselves — so the controllers produced are based 011 the vertebrate Central Nervous System. This means that there is a low-level controller which adapts itself to the robot so that, when switched on, it can be considered to simulate the springs and linkages of the passive robots to produce a walking robot, and this now active mechanism is then controlled by a relatively simple higher level controller. This is the best of both worlds — we have a robot which is inherently capable of walking, and thus is easy to control like the passive walkers, but also retains the general purpose abilities which makes it so potentially useful.This design methodology uses an evolutionary algorithm to generate low-level control¬ lers for a selection of simulated legged robots. The thesis also looks in detail at previous walking robots and their controllers and shows that some approaches, including staged evolution and hand-coding designs, may be unnecessary, and indeed inappropriate, at least for a general purpose controller. The specific algorithm used is evolutionary, using a simple genetic algorithm to allow adaptation to different robot configurations, and the controllers evolved are continuous time neural networks. These are chosen because of their ability to entrain to the movement of the robot, allowing the whole robot and network to be considered as a single dynamical system, which can then be controlled by a higher level system.An extensive program of experiments investigates the types of neural models and net¬ work structures which are best suited to this task, and it is shown that stateless and simple dynamic neural models are significantly outperformed as controllers by more complex, biologically plausible ones but that other ideas taken from biological systems, including network connectivities, are not generally as useful and reasons for this are examined.The thesis then shows that this system, although only developed 011 a single robot, is capable of automatically generating controllers for a wide selection of different test designs. Finally it shows that high level controllers, at least to control steering and speed, can be easily built 011 top of this now active walking mechanism

Using evolutionary artificial neural networks to design hierarchical animat nervous systems.

The research presented in this thesis examines the area of control systems for robots or animats (animal-like robots). Existing systems have problems in that they require a great deal of manual design or are limited to performing jobs of a single type. For these reasons, a better solution is desired. The system studied here is an Artificial Nervous System (ANS) which is biologically inspired; it is arranged as a hierarchy of layers containing modules operating in parallel. The ANS model has been developed to be flexible, scalable, extensible and modular. The ANS can be implemented using any suitable technology, for many different environments. The implementation focused on the two lowest layers (the reflex and action layers) of the ANS, which are concerned with control and rhythmic movement. Both layers were realised as Artificial Neural Networks (ANN) which were created using Evolutionary Algorithms (EAs). The task of the reflex layer was to control the position of an actuator (such as linear actuators or D.C. motors). The action layer performed the task of Central Pattern Generators (CPG), which produce rhythmic patterns of activity. In particular, different biped and quadruped gait patterns were created. An original neural model was specifically developed for assisting in the creation of these time-based patterns. It is shown in the thesis that Artificial Reflexes and CPGs can be configured successfully using this technique. The Artificial Reflexes were better at generalising across different actuators, without changes, than traditional controllers. Gaits such as pace, trot, gallop and pronk were successfully created using the CPGs. Experiments were conducted to determine whether modularity in the networks had an impact. It has been demonstrated that the degree of modularization in the network influences its evolvability, with more modular networks evolving more efficiently

Development of behaviors for a simulated humanoid robot

Mestrado em Engenharia de Computadores e TelemáticaControlar um robô bípede com vários graus de liberdade é um desafio que recebe a atenção de vários investigadores nas áreas da biologia, física, electrotecnia, ciências de computadores e mecânica. Para que um humanóide possa agir em ambientes complexos, são necessários comportamentos rápidos, estáveis e adaptáveis. Esta dissertação está centrada no desenvolvimento de comportamentos robustos para um robô humanóide simulado, no contexto das competições de futebol robótico simulado 3D do RoboCup, para a equipa FCPortugal3D. Desenvolver tais comportamentos exige o desenvolvimento de métodos de planeamento de trajectórias de juntas e controlo de baixo nível. Controladores PID foram implementados para o controlo de baixo nível. Para o planeamento de trajectórias, quatro métodos foram estudados. O primeiro método apresentado foi implementado antes desta dissertação e consiste numa sequência de funções degrau que definem o ângulo desejado para cada junta durante o movimento. Um novo método baseado na interpolação de um seno foi desenvolvido e consiste em gerar uma trajectória sinusoidal durante um determinado tempo, o que resulta em transições suaves entre o ângulo efectivo e o ângulo desejado para cada junta. Um outro método que foi desenvolvido, baseado em séries parciais de Fourier, gera um padrão cíclico para cada junta, podendo ter múltiplas frequências. Com base no trabalho desenvolvido por Sven Behnke, um CPG para locomoção omnidireccional foi estudado em detalhe e implementado. Uma linguagem de definição de comportamentos é também parte deste estudo e tem como objectivo simplificar a definição de comportamentos utilizando os vários métodos propostos. Integrando o controlo de baixo nível e os métodos de planeamento de trajectórias, vários comportamentos foram criados para permitir a uma versão simulada do humanóide NAO andar em diferentes direcções, rodar, chutar a bola, apanhar a bola (guarda-redes) e levantar do chão. Adicionalmente, a optimização e geração automática de comportamentos foi também estudada, utilizado algoritmos de optimização como o Hill Climbing e Algoritmos Genéticos. No final, os resultados são comparados com as equipas de simulação 3D que reflectem o estado da arte. Os resultados obtidos são bons e foram capazes de ultrapassar uma das três melhores equipas simuladas do RoboCup em diversos aspectos como a velocidade a andar, a velocidade de rotação, a distância da bola depois de chutada, o tempo para apanhar a bola e o tempo para levantar do chão. ABSTRACT: Controlling a biped robot with several degrees of freedom is a challenging task that takes the attention of several researchers in the fields of biology, physics, electronics, computer science and mechanics. For a humanoid robot to perform in complex environments, fast, stable and adaptable behaviors are required. This thesis is concerned with the development of robust behaviors for a simulated humanoid robot, in the scope of the RoboCup 3D Simulated Soccer Competitions, for FCPortugal3D team. Developing such robust behaviors requires the development of methods for joint trajectory planning and low-level control. PID control were implemented to achieve low-level joint control. For trajectory planning, four methods were studied. The first presented method was implemented before this thesis and consists of a sequence of step functions that define the target angle of each joint during the movement. A new method based on the interpolation of a sine function was developed and consists of generating a sinusoidal shape during some amount of time, leading to smooth transitions between the current angle and the target angle of each joint. Another method developed, based on partial Fourier Series, generates a multi-frequency cyclic pattern for each joint. This method is very flexible and allows to completely control the angular positions and velocities of the joints. Based on the work of developed by Sven Behnke, a CPG for omnidirectional locomotion was studied in detail and implemented. A behavior definition language is also part of this study and aims at simplifying the definition of behaviors using the several proposed methods. By integrating the low-level control and the trajectory planning methods, several behaviors were created to allow a simulated version of the humanoid NAO to walk in different directions, turn, kick the ball, catch the ball (goal keeper) and get up from the ground. Furthermore, the automatic generation of gaits, through the use of optimization algorithms such as hill climbing and genetic algorithms, was also studied and tested. In the end, the results are compared with the state of the art teams of the RoboCup 3D simulation league. The achieved results are good and were able to overcome one of the state of the art simulated teams of RoboCup in several aspects such as walking velocity, turning velocity, distance of the ball when kicked, time to catch the ball and the time to get up from the ground

Impact of Morphology Variations on Evolved Neural Controllers for Modular Robots

Modular robots, in particular those in which the modules are physically interchangeable, are suitable to be evolved because they allow for many different designs. Moreover, they can constitute ecosystems where “old” robots are disassembled and the resulting modules are composed together, either within an external assembling facility or by self-assembly procedures, to form new robots. However, in practical settings, self-assembly may result in morphologies that are slightly different from the expected ones: this may cause a detrimental misalignment between controller and morphology. Here, we characterize experimentally the robustness of neural controllers for Voxel-based Soft Robots, a kind of modular robots, with respect to small variations in the morphology. We employ evolutionary computation for optimizing the controllers and assess the impact of morphology variations along two axes: kind of morphology and size of the robot. Moreover, we quantify the advantage of performing a re-optimization of the controller for the varied morphology. Our results show that small variations in the morphology are in general detrimental for the performance of the evolved neural controller. Yet, a short re-optimization is often sufficient for aligning back the performance of the modified robot to the original one