Evolving Soft Robots with Vibration Based Movement

Creating effective designs for soft robots is extremely difficult due to the large number of different possibilities for shape, material properties, and movement mechanisms. Due to the lack of methods to design soft robots, previous research has used evolutionary algorithms to tackle this problem of overwhelming options. A popular technique is to use generative encodings to create designs using evolutionary algorithms because of their modularity and ability to induce large scale coordinated change. The main drawback of generative encodings is that it is difficult to know where along the ontogenic trajectory resides the phenotype with the highest fitness. The two main approaches for addressing this issue are static and scaled developmental timings. In order to compare the effectiveness of each of these two approaches, I have implemented a framework capable of evolving soft robot designs that utilize vibration as a movement mechanism

Artificial Neurogenesis: An Introduction and Selective Review

International audienceIn this introduction and review—like in the book which follows—we explore the hypothesis that adaptive growth is a means of producing brain-like machines. The emulation of neural development can incorporate desirable characteristics of natural neural systems into engineered designs. The introduction begins with a review of neural development and neural models. Next, artificial development— the use of a developmentally-inspired stage in engineering design—is introduced. Several strategies for performing this " meta-design " for artificial neural systems are reviewed. This work is divided into three main categories: bio-inspired representations ; developmental systems; and epigenetic simulations. Several specific network biases and their benefits to neural network design are identified in these contexts. In particular, several recent studies show a strong synergy, sometimes interchange-ability, between developmental and epigenetic processes—a topic that has remained largely under-explored in the literature

Evolvability signatures of generative encodings: beyond standard performance benchmarks

Evolutionary robotics is a promising approach to autonomously synthesize machines with abilities that resemble those of animals, but the field suffers from a lack of strong foundations. In particular, evolutionary systems are currently assessed solely by the fitness score their evolved artifacts can achieve for a specific task, whereas such fitness-based comparisons provide limited insights about how the same system would evaluate on different tasks, and its adaptive capabilities to respond to changes in fitness (e.g., from damages to the machine, or in new situations). To counter these limitations, we introduce the concept of "evolvability signatures", which picture the post-mutation statistical distribution of both behavior diversity (how different are the robot behaviors after a mutation?) and fitness values (how different is the fitness after a mutation?). We tested the relevance of this concept by evolving controllers for hexapod robot locomotion using five different genotype-to-phenotype mappings (direct encoding, generative encoding of open-loop and closed-loop central pattern generators, generative encoding of neural networks, and single-unit pattern generators (SUPG)). We observed a predictive relationship between the evolvability signature of each encoding and the number of generations required by hexapods to adapt from incurred damages. Our study also reveals that, across the five investigated encodings, the SUPG scheme achieved the best evolvability signature, and was always foremost in recovering an effective gait following robot damages. Overall, our evolvability signatures neatly complement existing task-performance benchmarks, and pave the way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary figures. Accepted at Information Sciences journal (in press). Supplemental videos are available online at, see http://goo.gl/uyY1R

Evolutionary Development based on Genetic Regulatory Models for Behavior-Finding

This thesis investigates the role, implications, and applications of a genetic-regulated developmental process in evolution. A novel formalism of string-regulated graph grammar, as an abstraction of biological development, is presented and studied. Founded on this formalism, evolutionary developmental models are introduced and tested. The results demonstrate the benefits of the models, compared to traditional direct encoding approaches, for the problems of form-finding and behavior-finding

On the origins and evolution of morphological complexity : a developmental perspective

The complexity of organisms has astonished biologists for centuries. How complexity has evolved, has given rise to much debate. Many have claimed that natural selection is the main factor that made it possible to achieve high degrees of complexity. On the contrary, others argue that this is far from the truth, as complexity can increase passively, without the need of natural selection. Either way, the exact mechanisms by which complexity has increased in some groups of organisms remains largely unknown. For morphology, to understand which mechanisms have enabled an increase in complexity, requires to study development. As development is the process that establishes morphology, any evolutionary change in morphology is preceded by a change in its development. Additionally, to understand how morphological complexity evolves, it is necessary to comprehend the phenotypic variation that different developmental mechanisms can produce. The two main questions that I am interested to answer in this dissertation are:  1. Are there some logical requirements that developmental mechanisms should fulfill in order to lead to complex morphologies? 2. How does morphological complexity affect evolution?   To tackle these questions, I used a general computational model of development, EmbryoMaker. EmbryoMaker is a general model that allows to simulate the development of 3D morphologies of any type. It is precisely this generality of EmbryoMaker which was vital for this dissertation, since it allowed an unconstrained exploration of developmental mechanisms, without being limited to certain organisms or systems. This allowed me to tackle the questions I posed in a general way. The general results obtained are: 1. The development of complex morphologies does not require cell signaling or complex gene networks. 2. Extracellular signaling enhances robustness through the compartmentalization of the embryo into different regions of gene expression 3. Complex morphologies are rare 4. The more complex a morphology is, the more finely tuned its developmental parameters need to be 5. The more complex the morphology, the larger the mutational asymmetry towards simplicity 6. The more complex morphology, the more complex the GPM These results indicate that there are qualitative differences in the way complex and simpler morphologies evolve. Complex morphologies evolve under a complex GPM and higher developmental instability. Additionally, complex morphologies produce a higher morphological diversity than simpler morphologies for the same amount of genetic variation, therefore offspring of complex individuals spread across large regions of the morphospace. Finally, these results also indicate that the evolution of morphological complexity becomes progressively slower as complexity increases, until possibly arriving at a complexity trap, where it cannot effectively increase.Luonnosta löytyvien muotojen moninaisuus on ällistyttänyt tutkijoita vuosisatoja ja muotojen kompleksisuuden synnystä on väitelty paljon. Yhtäältä on väitetty luonnonvalinnan olevan ensisijainen eliöiden muodon kompleksisuutta ajava tekijä ja toisaalta on ehdotettu kompleksisuuden voivan kehittyä passiivisesti luonnonvalinnasta riippumatta. Muodon monimutkaistumisen taustalla vaikuttavat mekanismit ovat pitkälti tuntemattomia. Kompleksisuuden lisääntymisen ymmärtäminen edellyttää ymmärrystä yksilönkehityksestä; muodot syntyvät yksilönkehityksen aikana, joten evolutiivinen muutos muodossa edellyttää muutoksia yksilönkehityksessä. Ymmärtääksemme, miten kompleksisuus lisääntyy evoluution myötä, on ymmärrettävä millaista ilmiasujen muuntelua yksilönkehitys voi tuottaa. Kaksi väitöskirjassani käsiteltävää pääkysymystä ovat: Vaaditaanko kehitysmekanismeilta tiettyjä ominaisuuksia, jotta ne voivat tuottaa kompleksisia muotoja? Miten muotojen kompleksisuus vaikuttaa evoluutioon? Käytän työssäni tietokonemallia, EmbryoMakeria, joka mahdollistaa minkä tahansa muodon kehityksen mallinnuksen kolmiuloitteisesti. EmbryoMakerin simulaatiot eivät rajoitu tiettyihin mallieliöihin tai järjestelmiin, mikä on olennaista tutkimukselleni. Tutkimukseni päätulokset ovat: Kompleksisten muotojen kehitys ei edellytä soluviestintää tai monimutkaisia geeniverkostoja. Solujen välinen viestintä lisää kehitysmekanismin vakautta jakamalla alkion rajattuihin geenien ilmentymisalueisiin. Kompleksiset muodot ovat harvinaisia. Mitä kompleksisempi muoto on, sitä tarkemmin sen kehitystä on säädeltävä. Mitä kompleksisempi muoto on, sitä todennäköisemmin mutaatiot johtavat muodon yksinkertaistumiseen. Mitä kompleksisempi muoto on, sitä kompleksisempi on sen taustalla toimiva geeni-ilmiasu-kartta. Tulokseni viittaavat laadullisiin eroihin kompleksisten ja yksinkertaisten muotojen evoluution välillä. Kompleksisten muotojen evoluution taustalla vaikuttava geeni-ilmiasukartta on monimutkaisempi kuin yksinkertaisilla muodoilla. Lisäksi kompleksisten muotojen kehitys on epävakaampaa kuin yksinkertaisten muotojen kehitys. Yksinkertaisiin muotoihin verrattuna kompleksiset muodot johtavat suurempaan monimuotoisuuteen vaikka geneettinen muuntelu taustalla olisi yhtä suurta; tämän vuoksi kompleksisten yksilöiden jälkeläiset levittäytyvät laajoille alueille muotoavaruudessa. Tulokseni osoittavat myös, että kompleksisuuden lisääntyessä kompleksisuuden evoluutio hidastuu, kunnes saavutetaan ’kompleksisuusansa' jossa kompleksisuus ei voi enää lisääntyä

A Practical Investigation into Achieving Bio-Plausibility in Evo-Devo Neural Microcircuits Feasible in an FPGA

Many researchers has conjectured, argued, or in some cases demonstrated, that bio-plausibility can bring about emergent properties such as adaptability, scalability, fault-tolerance, self-repair, reliability, and autonomy to bio-inspired intelligent systems. Evolutionary-developmental (evo-devo) spiking neural networks are a very bio-plausible mixture of such bio-inspired intelligent systems that have been proposed and studied by a few researchers. However, the general trend is that the complexity and thus the computational cost grow with the bio-plausibility of the system. FPGAs (Field- Programmable Gate Arrays) have been used and proved to be one of the flexible and cost efficient hardware platforms for research' and development of such evo-devo systems. However, mapping a bio-plausible evo-devo spiking neural network to an FPGA is a daunting task full of different constraints and trade-offs that makes it, if not infeasible, very challenging. This thesis explores the challenges, trade-offs, constraints, practical issues, and some possible approaches in achieving bio-plausibility in creating evolutionary developmental spiking neural microcircuits in an FPGA through a practical investigation along with a series of case studies. In this study, the system performance, cost, reliability, scalability, availability, and design and testing time and complexity are defined as measures for feasibility of a system and structural accuracy and consistency with the current knowledge in biology as measures for bio-plausibility. Investigation of the challenges starts with the hardware platform selection and then neuron, cortex, and evo-devo models and integration of these models into a whole bio-inspired intelligent system are examined one by one. For further practical investigation, a new PLAQIF Digital Neuron model, a novel Cortex model, and a new multicellular LGRN evo-devo model are designed, implemented and tested as case studies. Results and their implications for the researchers, designers of such systems, and FPGA manufacturers are discussed and concluded in form of general trends, trade-offs, suggestions, and recommendations

RNA, the Epicenter of Genetic Information

The origin story and emergence of molecular biology is muddled. The early triumphs in bacterial genetics and the complexity of animal and plant genomes complicate an intricate history. This book documents the many advances, as well as the prejudices and founder fallacies. It highlights the premature relegation of RNA to simply an intermediate between gene and protein, the underestimation of the amount of information required to program the development of multicellular organisms, and the dawning realization that RNA is the cornerstone of cell biology, development, brain function and probably evolution itself. Key personalities, their hubris as well as prescient predictions are richly illustrated with quotes, archival material, photographs, diagrams and references to bring the people, ideas and discoveries to life, from the conceptual cradles of molecular biology to the current revolution in the understanding of genetic information. Key Features Documents the confused early history of DNA, RNA and proteins - a transformative history of molecular biology like no other. Integrates the influences of biochemistry and genetics on the landscape of molecular biology. Chronicles the important discoveries, preconceptions and misconceptions that retarded or misdirected progress. Highlights major pioneers and contributors to molecular biology, with a focus on RNA and noncoding DNA. Summarizes the mounting evidence for the central roles of non-protein-coding RNA in cell and developmental biology. Provides a thought-provoking retrospective and forward-looking perspective for advanced students and professional researchers

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Modelling Early Transitions Toward Autonomous Protocells

This thesis broadly concerns the origins of life problem, pursuing a joint approach that combines general philosophical/conceptual reflection on the problem along with more detailed and formal scientific modelling work oriented in the conceptual perspective developed. The central subject matter addressed is the emergence and maintenance of compartmentalised chemistries as precursors of more complex systems with a proper cellular organization. Whereas an evolutionary conception of life dominates prebiotic chemistry research and overflows into the protocells field, this thesis defends that the 'autonomous systems perspective' of living phenomena is a suitable - arguably the most suitable - conceptual framework to serve as a backdrop for protocell research. The autonomy approach allows a careful and thorough reformulation of the origins of cellular life problem as the problem of how integrated autopoietic chemical organisation, present in all full-fledged cells, originated and developed from more simple far-from-equilibrium chemical aggregate systems.Comment: 205 Pages, 27 Figures, PhD Thesis Defended Feb 201