A scalable pipeline for designing reconfigurable organisms

Freeform generative design of complex functional structures

Generative machine learning is poised to revolutionise a range of domains where rational design has long been the de facto approach: where design is practically a time consuming and frustrating process guided by heuristics and intuition. In this article we focus on the domain of flow chemistry, which is an ideal candidate for generative design approaches. We demonstrate a generative machine learning framework that optimises diverse, bespoke reactor elements for flow chemistry applications, combining evolutionary algorithms and a scalable fluid dynamics solver for in silico performance assessment. Experimental verification confirms the discovery of never-before-seen bespoke mixers whose performance exceeds the state of the art by 45%. These findings highlight the power of autonomous generative design to improve the operational performance of complex functional structures, with potential wide-ranging industrial applications.

Phase-separated droplets swim to their dissolution

Biological macromolecules can condense into liquid domains. In cells, these condensates form membraneless organelles that can organize chemical reactions. However, little is known about the physical consequences of chemical activity in and around condensates. Working with model bovine serum albumin (BSA) condensates, we show that droplets swim along chemical gradients. Active BSA droplets loaded with urease swim toward each other. Passive BSA droplets show diverse responses to externally applied gradients of the enzyme's substrate and products. In all these cases, droplets swim toward solvent conditions that favor their dissolution. We call this behavior "dialytaxis", and expect it to be generic, as conditions which favor dissolution typically reduce interfacial tension, whose gradients are well-known to drive droplet motion through the Marangoni effect. These results could potentially suggest alternative physical mechanisms for active transport in living cells, and may enable the design of fluid micro-robots.

The Lithbea Domain

The tree of life is the evolutionary metaphor for the past and present connections of all cellular organisms. Today, to speak of biodiversity is not only to speak of archaea, bacteria, and eukaryotes, but they should also consider the "new biodiversity" that includes viruses and synthetic organisms, which represent the new forms of life created in laboratories. There is even a third group of artificial entities that, although not living systems, pretend to imitate the living. To embrace and organize all this new biodiversity, I propose the creation of a new domain, with the name Lithbea (from life-on-the-border entites) The criteria for inclusion as members of Lithbea are: i) the acellular nature of the living system, ii) its origin in laboratory manipulation, iii) showing new biological traits, iv) the presence of exogenous genetic elements, v) artificial or inorganic nature. Within Lithbea there are two subdomains: Virworld (from virus world) which includes all viruses, regarded as lifeless living systems, and classified according to the International Committee on Taxonomy of Viruses (ICTV), and ii) Humade (from human-made) which includes all synthetic organisms and artificial entities. The relationships of Lithbea members to the three classical woesian domains and their implications are briefly discussed.

Tests for consciousness in humans and beyond

Which systems/organisms are conscious? New tests for consciousness ('C-tests') are urgently needed. There is persisting uncertainty about when consciousness arises in human development, when it is lost due to neurological disorders and brain injury, and how it is distributed in nonhuman species. This need is amplified by recent and rapid developments in artificial intelligence (AI), neural organoids, and xenobot technology. Although a number of C-tests have been proposed in recent years, most are of limited use, and currently we have no C-tests for many of the populations for which they are most critical. Here, we identify challenges facing any attempt to develop C-tests, propose a multidimensional classification of such tests, and identify strategies that might be used to validate them.

An evaluation of the xenobotic cognitive project: Towards Stage 1 of xenobotic cognition

Xenobot, the world's first biological robot, puts numerous philosophical riddles before us. One among them pertains to the cognitive status of these entities. Are these biological robots cognitive? To evaluate the cognitive status of xenobots and to resolve the puzzle of a single mind emerging from smaller sub-units, in this article, I juxtapose the cognitive capacities of xenobots with that of two other minimal models of cognition, i.e., basal cognition and nonliving active matter cognition. Further, the article underlines the essential cognitive capabilities that xenobots need to achieve to enter what I call stage 1 of xenobotic cognition. Stage 1 is characterized by numerous cognitive mechanisms, which are integral for the survival and cognition of basal organisms. Finally, I suggest that developing xenobots that can reach Stage 1 can help us achieve sophistication in the areas of evolution of the human mind, robotics, biology and medicine, and artificial intelligence (AI).

Outline of an evolutionary morphology generator towards the modular design of a biohybrid catheter

Biohybrid machines (BHMs) are an amalgam of actuators composed of living cells with synthetic materials. They are engineered in order to improve autonomy, adaptability and energy efficiency beyond what conventional robots can offer. However, designing these machines is no trivial task for humans, provided the field's short history and, thus, the limited experience and expertise on designing and controlling similar entities, such as soft robots. To unveil the advantages of BHMs, we propose to overcome the hindrances of their design process by developing a modular modeling and simulation framework for the digital design of BHMs that incorporates Artificial Intelligence powered algorithms. Here, we present the initial workings of the first module in an exemplar framework, namely, an evolutionary morphology generator. As proof-of-principle for this project, we use the scenario of developing a biohybrid catheter as a medical device capable of arriving to hard-to-reach regions of the human body to release drugs. We study the automatically generated morphology of actuators that will enable the functionality of that catheter. The primary results presented here enforced the update of the methodology used, in order to better depict the problem under study, while also provided insights for the future versions of the software module.

Open-endedness in synthetic biology: A route to continual innovation for biological design

Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize existing biological functions, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design, with the novelty of designed biology being at least as important as how well it fulfils its goal. Rather than solely focusing on optimization toward a single best design, designing with novelty in mind may allow us to move beyond the diminishing returns we see in performance for most engineered biology. Research from the artificial life community has demonstrated that embracing novelty can automatically generate innovative and unexpected solutions to challenging problems beyond local optima. Synthetic biology offers the ideal playground to explore more creative approaches to biological design.

Glutamine addiction in tumor cell: oncogene regulation and clinical treatment

After undergoing metabolic reprogramming, tumor cells consume additional glutamine to produce amino acids, nucleotides, fatty acids, and other substances to facilitate their unlimited proliferation. As such, the metabolism of glutamine is intricately linked to the survival and progression of cancer cells. Consequently, targeting the glutamine metabolism presents a promising strategy to inhibit growth of tumor cell and cancer development. This review describes glutamine uptake, metabolism, and transport in tumor cells and its pivotal role in biosynthesis of amino acids, fatty acids, nucleotides, and more. Furthermore, we have also summarized the impact of oncogenes like C-MYC, KRAS, HIF, and p53 on the regulation of glutamine metabolism and the mechanisms through which glutamine triggers mTORC1 activation. In addition, role of different anti-cancer agents in targeting glutamine metabolism has been described and their prospective applications are assessed.

Bioelectricity in dental medicine: a narrative review

BACKGROUND

Bioelectric signals, whether exogenous or endogenous, play crucial roles in the life processes of organisms. Recently, the significance of bioelectricity in the field of dentistry is steadily gaining greater attention.

OBJECTIVE

This narrative review aims to comprehensively outline the theory, physiological effects, and practical applications of bioelectricity in dental medicine and to offer insights into its potential future direction. It attempts to provide dental clinicians and researchers with an electrophysiological perspective to enhance their clinical practice or fundamental research endeavors.

METHODS

An online computer search for relevant literature was performed in PubMed, Web of Science and Cochrane Library, with the keywords "bioelectricity, endogenous electric signal, electric stimulation, dental medicine."

RESULTS

Eventually, 288 documents were included for review. The variance in ion concentration between the interior and exterior of the cell membrane, referred to as transmembrane potential, forms the fundamental basis of bioelectricity. Transmembrane potential has been established as an essential regulator of intercellular communication, mechanotransduction, migration, proliferation, and immune responses. Thus, exogenous electric stimulation can significantly alter cellular action by affecting transmembrane potential. In the field of dental medicine, electric stimulation has proven useful for assessing pulp condition, locating root apices, improving the properties of dental biomaterials, expediting orthodontic tooth movement, facilitating implant osteointegration, addressing maxillofacial malignancies, and managing neuromuscular dysfunction. Furthermore, the reprogramming of bioelectric signals holds promise as a means to guide organism development and intervene in disease processes. Besides, the development of high-throughput electrophysiological tools will be imperative for identifying ion channel targets and precisely modulating bioelectricity in the future.

CONCLUSIONS

Bioelectricity has found application in various concepts of dental medicine but large-scale, standardized, randomized controlled clinical trials are still necessary in the future. In addition, the precise, repeatable and predictable measurement and modulation methods of bioelectric signal patterns are essential research direction.

Motile Living Biobots Self-Construct from Adult Human Somatic Progenitor Seed Cells

Fundamental knowledge gaps exist about the plasticity of cells from adult soma and the potential diversity of body shape and behavior in living constructs derived from genetically wild-type cells. Here anthrobots are introduced, a spheroid-shaped multicellular biological robot (biobot) platform with diameters ranging from 30 to 500 microns and cilia-powered locomotive abilities. Each Anthrobot begins as a single cell, derived from the adult human lung, and self-constructs into a multicellular motile biobot after being cultured in extra cellular matrix for 2 weeks and transferred into a minimally viscous habitat. Anthrobots exhibit diverse behaviors with motility patterns ranging from tight loops to straight lines and speeds ranging from 5-50 microns s-1 . The anatomical investigations reveal that this behavioral diversity is significantly correlated with their morphological diversity. Anthrobots can assume morphologies with fully polarized or wholly ciliated bodies and spherical or ellipsoidal shapes, each related to a distinct movement type. Anthrobots are found to be capable of traversing, and inducing rapid repair of scratches in, cultured human neural cell sheets in vitro. By controlling microenvironmental cues in bulk, novel structures, with new and unexpected behavior and biomedically-relevant capabilities, can be discovered in morphogenetic processes without direct genetic editing or manual sculpting.

From reinforcement learning to agency: Frameworks for understanding basal cognition

Organisms play, explore, and mimic those around them. Is there a purpose to this behavior? Are organisms just behaving, or are they trying to achieve goals? We believe this is a false dichotomy. To that end, to understand organisms, we attempt to unify two approaches for understanding complex agents, whether evolved or engineered. We argue that formalisms describing multiscale competencies and goal-directedness in biology (e.g., TAME), and reinforcement learning (RL), can be combined in a symbiotic framework. While RL has been largely focused on higher-level organisms and robots of high complexity, TAME is naturally capable of describing lower-level organisms and minimal agents as well. We propose several novel questions that come from using RL/TAME to understand biology as well as ones that come from using biology to formulate new theory in AI. We hope that the research programs proposed in this piece shape future efforts to understand biological organisms and also future efforts to build artificial agents.

Selection for Size in Molecular Self-Assembly Drives the De Novo Evolution of a Molecular Machine

The functioning of machines typically requires a concerted action of their parts. This requirement also holds for molecular motors that drive vital cellular processes and imposes constraints on their conformational changes as well as the rates at which they occur. It remains unclear whether, during evolution, features required for functional molecular machines can emerge simultaneously or require sequential adaptation to different selection pressures. We address this question by theoretically analyzing the evolution of filament treadmilling. This process refers to the self-assembly of linear polymers that grow and shrink at equal rates at their opposite ends. It constitutes a simple biological molecular machine that is involved in bacterial cell division and requires that several conditions are met. In our simulation framework, treadmilling emerges as a consequence of selecting for a target average polymer length. We discuss why other forms of assembly dynamics, which also reach the imposed target length, do not emerge in our simulations. Our work shows that complex molecular functions can evolve de novo under selection for a single physical feature.

Reflections upon a new definition of life

What is life? Multiple definitions have been proposed to answer this question, but unfortunately, none of them has reached the consensus of the scientific community. Here, the strategy used to define what life is was based on first establishing which characteristics are common to all living systems (organic nature, entropy-producing system, self-organizing, reworkable pre-program, capacity to interact and adapt, reproduction and evolution) and from them constructing the definition taking into account that reproduction and evolution are not essential for life. On this basis, life is defined as an interactive process occurring in entropy-producing, adaptive, and informative (organic) systems. An unforeseen consequence of the inseparable duality between the system (living being) and the process (life) is the interchangeability of the elements of the definition to obtain other equally valid alternatives. In addition, in the light of this definition, cases of temporarily lifeless living systems (viruses, dormant seeds, and ultracold cells) are analyzed, as well as the status of artificial life entities and the hypothetical nature of extraterrestrial life. All living systems are perishable because the passage of time leads to increasing entropy. Life must create order by continuously producing disorder and exporting it to the environment and so we move and stay in the phase transition between order and chaos, far from equilibrium, thanks to the input of energy from the outside. However, the passage of time eventually leads us to an end in which life disappears and entropy increases.

Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind

Each of us made the remarkable journey from mere matter to mind: starting life as a quiescent oocyte ("just chemistry and physics"), and slowly, gradually, becoming an adult human with complex metacognitive processes, hopes, and dreams. In addition, even though we feel ourselves to be a unified, single Self, distinct from the emergent dynamics of termite mounds and other swarms, the reality is that all intelligence is collective intelligence: each of us consists of a huge number of cells working together to generate a coherent cognitive being with goals, preferences, and memories that belong to the whole and not to its parts. Basal cognition is the quest to understand how Mind scales-how large numbers of competent subunits can work together to become intelligences that expand the scale of their possible goals. Crucially, the remarkable trick of turning homeostatic, cell-level physiological competencies into large-scale behavioral intelligences is not limited to the electrical dynamics of the brain. Evolution was using bioelectric signaling long before neurons and muscles appeared, to solve the problem of creating and repairing complex bodies. In this Perspective, I review the deep symmetry between the intelligence of developmental morphogenesis and that of classical behavior. I describe the highly conserved mechanisms that enable the collective intelligence of cells to implement regulative embryogenesis, regeneration, and cancer suppression. I sketch the story of an evolutionary pivot that repurposed the algorithms and cellular machinery that enable navigation of morphospace into the behavioral navigation of the 3D world which we so readily recognize as intelligence. Understanding the bioelectric dynamics that underlie construction of complex bodies and brains provides an essential path to understanding the natural evolution, and bioengineered design, of diverse intelligences within and beyond the phylogenetic history of Earth.

Humans and robots: Friends of the future? A bird's eye view of biomanufacturing industry 5.0

The evolution of industries have introduced versatile technologies, motivating limitless possibilities of tackling pivotal global predicaments in the arenas of medicine, environment, defence, and national security. In this direction, ardently emerges the new era of Industry 5.0 through the eyes of biomanufacturing, which integrates the most advanced systems 21st century has to offer by means of integrating artificial systems to mimic and nativize the natural milieu to substitute the deficits of nature, thence leading to a new meta world. Albeit, it questions the natural order of the living world, which necessitates certain paramount stipulations to be addressed for a successful expansion of biomanufacturing Industry 5.0. Can humans live in synergism with artificial beings? How can humans establish dominance of hierarchy with artificial counterparts? This perspective provides a bird's eye view on the plausible direction of a new meta world inquisitively. For this purpose, we propose the influence of internet of things (IoT) via new generation interfacial systems, such as, human-machine interface (HMI) and brain-computer interface (BCI) in the domain of tissue engineering and regenerative medicine, which can be extended to target modern warfare and smart healthcare.

Hybrid Life: Integrating biological, artificial, and cognitive systems

Artificial life is a research field studying what processes and properties define life, based on a multidisciplinary approach spanning the physical, natural, and computational sciences. Artificial life aims to foster a comprehensive study of life beyond "life as we know it" and toward "life as it could be," with theoretical, synthetic, and empirical models of the fundamental properties of living systems. While still a relatively young field, artificial life has flourished as an environment for researchers with different backgrounds, welcoming ideas, and contributions from a wide range of subjects. Hybrid Life brings our attention to some of the most recent developments within the artificial life community, rooted in more traditional artificial life studies but looking at new challenges emerging from interactions with other fields. Hybrid Life aims to cover studies that can lead to an understanding, from first principles, of what systems are and how biological and artificial systems can interact and integrate to form new kinds of hybrid (living) systems, individuals, and societies. To do so, it focuses on three complementary perspectives: theories of systems and agents, hybrid augmentation, and hybrid interaction. Theories of systems and agents are used to define systems, how they differ (e.g., biological or artificial, autonomous, or nonautonomous), and how multiple systems relate in order to form new hybrid systems. Hybrid augmentation focuses on implementations of systems so tightly connected that they act as a single, integrated one. Hybrid interaction is centered around interactions within a heterogeneous group of distinct living and nonliving systems. After discussing some of the major sources of inspiration for these themes, we will focus on an overview of the works that appeared in Hybrid Life special sessions, hosted by the annual Artificial Life Conference between 2018 and 2022. This article is categorized under: Neuroscience > Cognition Philosophy > Artificial Intelligence Computer Science and Robotics > Robotics.

A Light-Powered Liquid Crystal Elastomer Roller

Achieving and controlling the desired movements of active machines is generally accomplished through precise control of artificial muscles in a distributed and serialized manner, which is a significant challenge. The emerging motion control strategy based on self-oscillation in active machines has unique advantages, including directly harvesting energy from constant ambient light, and it has no need for complex controllers. Inspired by the roller, we have innovatively developed a self-rolling roller that consists of a roller and a liquid crystal elastomer (LCE) fiber. By utilizing a well-established dynamic LCE model and subjecting it to constant illumination, we have investigated the dynamic behavior of the self-rolling roller. Based on numerical calculations, it has been discovered that the roller, when subjected to steady illumination, exhibits two distinct motion regimes: the static regime and the self-rolling regime. The self-rolling regime, characterized by continuous periodic rolling, is sustained by the interaction between light energy and damping dissipation. The continuous periodic rolling observed in the self-rolling regime is maintained through the interplay between the dissipation of damping and the absorption of light energy. In the static state, the rolling angle of the roller begins to decrease rapidly and then converges to zero. Detailed investigations have been conducted to determine the critical conditions required to initiate self-rolling, as well as the essential system parameters that influence its frequency and amplitude. The proposed self-rolling roller has superiorities in its simple structure, light weight, alternative to manual labor, and speediness. This advancement is expected to inspire greater design diversity in micromachines, soft robotics, energy harvesters, and similar areas.

Efficient automatic design of robots

Robots are notoriously difficult to design because of complex interdependencies between their physical structure, sensory and motor layouts, and behavior. Despite this, almost every detail of every robot built to date has been manually determined by a human designer after several months or years of iterative ideation, prototyping, and testing. Inspired by evolutionary design in nature, the automated design of robots using evolutionary algorithms has been attempted for two decades, but it too remains inefficient: days of supercomputing are required to design robots in simulation that, when manufactured, exhibit desired behavior. Here we show de novo optimization of a robot's structure to exhibit a desired behavior, within seconds on a single consumer-grade computer, and the manufactured robot's retention of that behavior. Unlike other gradient-based robot design methods, this algorithm does not presuppose any particular anatomical form; starting instead from a randomly-generated apodous body plan, it consistently discovers legged locomotion, the most efficient known form of terrestrial movement. If combined with automated fabrication and scaled up to more challenging tasks, this advance promises near-instantaneous design, manufacture, and deployment of unique and useful machines for medical, environmental, vehicular, and space-based tasks.

Chemically fueling new microrobot abilities

A combustion-powered soft actuator takes microrobots to new heights and speeds.

Future medicine: from molecular pathways to the collective intelligence of the body

The remarkable anatomical homeostasis exhibited by complex living organisms suggests that they are inherently reprogrammable information-processing systems that offer numerous interfaces to their physiological and anatomical problem-solving capacities. We briefly review data suggesting that the multiscale competency of living forms affords a new path for biomedicine that exploits the innate collective intelligence of tissues and organs. The concept of tissue-level allostatic goal-directedness is already bearing fruit in clinical practice. We sketch a roadmap towards 'somatic psychiatry' by using advances in bioelectricity and behavioral neuroscience to design methods that induce self-repair of structure and function. Relaxing the assumption that cellular control mechanisms are static, exploiting powerful concepts from cybernetics, behavioral science, and developmental biology may spark definitive solutions to current biomedical challenges.

Colloidal robotics

Robots have components that work together to accomplish a task. Colloids are particles, usually less than 100 µm, that are small enough that they do not settle out of solution. Colloidal robots are particles capable of functions such as sensing, computation, communication, locomotion and energy management that are all controlled by the particle itself. Their design and synthesis is an emerging area of interdisciplinary research drawing from materials science, colloid science, self-assembly, robophysics and control theory. Many colloidal robot systems approach synthetic versions of biological cells in autonomy and may find ultimate utility in bringing these specialized functions to previously inaccessible locations. This Perspective examines the emerging literature and highlights certain design principles and strategies towards the realization of colloidal robots.

Practical hardware for evolvable robots

The evolutionary robotics field offers the possibility of autonomously generating robots that are adapted to desired tasks by iteratively optimising across successive generations of robots with varying configurations until a high-performing candidate is found. The prohibitive time and cost of actually building this many robots means that most evolutionary robotics work is conducted in simulation, but to apply evolved robots to real-world problems, they must be implemented in hardware, which brings new challenges. This paper explores in detail the design of an example system for realising diverse evolved robot bodies, and specifically how this interacts with the evolutionary process. We discover that every aspect of the hardware implementation introduces constraints that change the evolutionary space, and exploring this interplay between hardware constraints and evolution is the key contribution of this paper. In simulation, any robot that can be defined by a suitable genetic representation can be implemented and evaluated, but in hardware, real-world limitations like manufacturing/assembly constraints and electrical power delivery mean that many of these robots cannot be built, or will malfunction in operation. This presents the novel challenge of how to constrain an evolutionary process within the space of evolvable phenotypes to only those regions that are practically feasible: the viable phenotype space. Methods of phenotype filtering and repair were introduced to address this, and found to degrade the diversity of the robot population and impede traversal of the exploration space. Furthermore, the degrees of freedom permitted by the hardware constraints were found to be poorly matched to the types of morphological variation that would be the most useful in the target environment. Consequently, the ability of the evolutionary process to generate robots with effective adaptations was greatly reduced. The conclusions from this are twofold. 1) Designing a hardware platform for evolving robots requires different thinking, in which all design decisions should be made with reference to their impact on the viable phenotype space. 2) It is insufficient to just evolve robots in simulation without detailed consideration of how they will be implemented in hardware, because the hardware constraints have a profound impact on the evolutionary space.

Heat-Driven Synchronization in Coupled Liquid Crystal Elastomer Spring Self-Oscillators

Self-oscillating coupled machines are capable of absorbing energy from the external environment to maintain their own motion and have the advantages of autonomy and portability, which also contribute to the exploration of the field of synchronization and clustering. Based on a thermally responsive liquid crystal elastomer (LCE) spring self-oscillator in a linear temperature field, this paper constructs a coupling and synchronization model of two self-oscillators connected by springs. Based on the existing dynamic LCE model, this paper theoretically reveals the self-oscillation mechanism and synchronization mechanism of two self-oscillators. The results show that adjusting the initial conditions and system parameters causes the coupled system to exhibit two synchronization modes: in-phase mode and anti-phase mode. The work conducted by the driving force compensates for the damping dissipation of the system, thus maintaining self-oscillation. The phase diagrams of different system parameters are drawn to illuminate the self-oscillation and synchronization mechanism. For weak interaction, changing the initial conditions may obtain the modes of in-phase and anti-phase. Under conditions of strong interactions, the system consistently exhibits an in-phase mode. Furthermore, an investigation is conducted on the influence of system parameters, such as the LCE elastic coefficient and spring elastic coefficient, on the amplitudes and frequencies of the two synchronization modes. This study aims to enhance the understanding of self-oscillator synchronization and its potential applications in areas such as energy harvesting, power generation, detection, soft robotics, medical devices and micro/nanodevices.

A Geometric Approach towards Inverse Kinematics of Soft Extensible Pneumatic Actuators Intended for Trajectory Tracking

Soft robots are interesting examples of hyper-redundancy in robotics. However, the nonlinear continuous dynamics of these robots and the use of hyper-elastic and visco-elastic materials make modeling these robots more complicated. This study presents a geometric inverse kinematics (IK) model for trajectory tracking of multi-segment extensible soft robots, where each segment of the soft actuator is geometrically approximated with a rigid links model to reduce the complexity. In this model, the links are connected with rotary and prismatic joints, which enable both the extension and rotation of the robot. Using optimization methods, the desired configuration variables of the soft actuator for the desired end-effector positions were obtained. Furthermore, the redundancy of the robot is applied for second task applications, such as tip angle control. The model's performance was investigated through kinematics and dynamics simulations and numerical benchmarks on multi-segment soft robots. The results showed lower computational costs and higher accuracy compared to most existing models. The method is easy to apply to multi-segment soft robots in both 2D and 3D, and it was experimentally validated on 3D-printed soft robotic manipulators. The results demonstrated the high accuracy in path following using this technique.

Biological Robots: Perspectives on an Emerging Interdisciplinary Field

Advances in science and engineering often reveal the limitations of classical approaches initially used to understand, predict, and control phenomena. With progress, conceptual categories must often be re-evaluated to better track recently discovered invariants across disciplines. It is essential to refine frameworks and resolve conflicting boundaries between disciplines such that they better facilitate, not restrict, experimental approaches and capabilities. In this essay, we address specific questions and critiques which have arisen in response to our research program, which lies at the intersection of developmental biology, computer science, and robotics. In the context of biological machines and robots, we explore changes across concepts and previously distinct fields that are driven by recent advances in materials, information, and life sciences. Herein, each author provides their own perspective on the subject, framed by their own disciplinary training. We argue that as with computation, certain aspects of developmental biology and robotics are not tied to specific materials; rather, the consilience of these fields can help to shed light on issues of multiscale control, self-assembly, and relationships between form and function. We hope new fields can emerge as boundaries arising from technological limitations are overcome, furthering practical applications from regenerative medicine to useful synthetic living machines.

The machine-organism relation revisited

This article addresses some crucial assumptions that are rarely acknowledged when organisms and machines are compared. We begin by presenting a short historical reconstruction of the concept of "machine." We show that there has never been a unique and widely accepted definition of "machine" and that the extant definitions are based on specific technologies. Then we argue that, despite the concept's ambiguity, we can still defend a more robust, specific, and useful notion of machine analogy that accounts for successful strategies in connecting specific devices (or mechanisms) with particular living phenomena. For that purpose, we distinguish between what we call "generic identity" and proper "machine analogy." We suggest that "generic identity"-which, roughly stated, presumes that some sort of vague similarity might exist between organisms and machines-is a source of the confusion haunting many persistent disagreements and that, accordingly, it should be dismissed. Instead, we endorse a particular form of "machine analogy" where the relation between organic phenomena and mechanical devices is not generic but specific and grounded on the identification of shared "invariants." We propose that the machine analogy is a kind of analogy as proportion and we elucidate how this is used or might be used in scientific practices. We finally argue that while organisms are not machines in a generic sense, they might share many robust "invariants," which justify the scientists' use of machine analogies for grasping living phenomena.

Light-Fueled Synchronization of Two Coupled Liquid Crystal Elastomer Self-Oscillators

The synchronization and group behaviors of self-excited coupled oscillators are common in nature and deserve to be explored, for self-excited motions have the advantages of actively collecting energy from the environment, being autonomous, making equipment portable, and so on. Based on light-powered self-excited oscillators composed of liquid crystal elastomer (LCE) bars, the synchronization of two self-excited coupled oscillators is theoretically studied. Numerical calculations show that self-excited oscillations of the system have two synchronization modes, in-phase mode and anti-phase mode, which are mainly determined by their interaction. The time histories of various quantities are calculated to elucidate the mechanism of self-excited oscillation and synchronization. For strong interactions, the system always develops into in-phase synchronization mode, while for weak interaction, the system will evolve into anti-phase synchronization mode. Furthermore, the effects of initial conditions, contraction coefficient, light intensity, and damping coefficient on the two synchronization modes of the self-excited oscillation are investigated extensively. The initial condition generally does not affect the synchronization mode and its amplitude. The amplitude of self-oscillation always increases with increasing contraction coefficient, gravitational acceleration, and light intensity, while it decreases with the increasing damping coefficient. This work will deepen people's understanding of the synchronization behaviors of self-excited coupled oscillators, and the theoretical framework could be extended to scenarios involving large-scale synchronization of the systems with numerous interacting oscillators.

A Variational Synthesis of Evolutionary and Developmental Dynamics

This paper introduces a variational formulation of natural selection, paying special attention to the nature of 'things' and the way that different 'kinds' of 'things' are individuated from-and influence-each other. We use the Bayesian mechanics of particular partitions to understand how slow phylogenetic processes constrain-and are constrained by-fast, phenotypic processes. The main result is a formulation of adaptive fitness as a path integral of phenotypic fitness. Paths of least action, at the phenotypic and phylogenetic scales, can then be read as inference and learning processes, respectively. In this view, a phenotype actively infers the state of its econiche under a generative model, whose parameters are learned via natural (Bayesian model) selection. The ensuing variational synthesis features some unexpected aspects. Perhaps the most notable is that it is not possible to describe or model a population of conspecifics per se. Rather, it is necessary to consider populations of distinct natural kinds that influence each other. This paper is limited to a description of the mathematical apparatus and accompanying ideas. Subsequent work will use these methods for simulations and numerical analyses-and identify points of contact with related mathematical formulations of evolution.

Darwin's agential materials: evolutionary implications of multiscale competency in developmental biology

A critical aspect of evolution is the layer of developmental physiology that operates between the genotype and the anatomical phenotype. While much work has addressed the evolution of developmental mechanisms and the evolvability of specific genetic architectures with emergent complexity, one aspect has not been sufficiently explored: the implications of morphogenetic problem-solving competencies for the evolutionary process itself. The cells that evolution works with are not passive components: rather, they have numerous capabilities for behavior because they derive from ancestral unicellular organisms with rich repertoires. In multicellular organisms, these capabilities must be tamed, and can be exploited, by the evolutionary process. Specifically, biological structures have a multiscale competency architecture where cells, tissues, and organs exhibit regulative plasticity-the ability to adjust to perturbations such as external injury or internal modifications and still accomplish specific adaptive tasks across metabolic, transcriptional, physiological, and anatomical problem spaces. Here, I review examples illustrating how physiological circuits guiding cellular collective behavior impart computational properties to the agential material that serves as substrate for the evolutionary process. I then explore the ways in which the collective intelligence of cells during morphogenesis affect evolution, providing a new perspective on the evolutionary search process. This key feature of the physiological software of life helps explain the remarkable speed and robustness of biological evolution, and sheds new light on the relationship between genomes and functional anatomical phenotypes.

Emergence in Artificial Life

Even when concepts similar to emergence have been used since antiquity, we lack an agreed definition. However, emergence has been identified as one of the main features of complex systems. Most would agree on the statement "life is complex." Thus understanding emergence and complexity should benefit the study of living systems. It can be said that life emerges from the interactions of complex molecules. But how useful is this to understanding living systems? Artificial Life (ALife) has been developed in recent decades to study life using a synthetic approach: Build it to understand it. ALife systems are not so complex, be they soft (simulations), hard (robots), or wet(protocells). Thus, we can aim at first understanding emergence in ALife, to then use this knowledge in biology. I argue that to understand emergence and life, it becomes useful to use information as a framework. In a general sense, I define emergence as information that is not present at one scale but present at another. This perspective avoids problems of studying emergence from a materialist framework and can also be useful in the study of self-organization and complexity.

Small-Scale Robotics with Tailored Wettability

Small-scale robots (SSRs) have emerged as promising and versatile tools in various biomedical, sensing, decontamination, and manipulation applications, as they are uniquely capable of performing tasks at small length scales. With the miniaturization of robots from the macroscale to millimeter-, micrometer-, and nanometer-scales, the viscous and surface forces, namely adhesive forces and surface tension have become dominant. These forces significantly impact motion efficiency. Surface engineering of robots with both hydrophilic and hydrophobic functionalization presents a brand-new pathway to overcome motion resistance and enhance the ability to target and regulate robots for various tasks. This review focuses on the current progress and future perspectives of SSRs with hydrophilic and hydrophobic modifications (including both tethered and untethered robots). The study emphasizes the distinct advantages of SSRs, such as improved maneuverability and reduced drag forces, and outlines their potential applications. With continued innovation, rational surface engineering is expected to endow SSRs with exceptional mobility and functionality, which can broaden their applications, enhance their penetration depth, reduce surface fouling, and inhibit bacterial adhesion.

Ontogeny of collective behaviour

During their lifetime, superorganisms, like unitary organisms, undergo transformations that change the machinery of their collective behaviour. Here, we suggest that these transformations are largely understudied and propose that more systematic research into the ontogeny of collective behaviours is needed if we hope to better understand the link between proximate behavioural mechanisms and the development of collective adaptive functions. In particular, certain social insects engage in self-assemblage, forming dynamic and physically connected architectures with striking similarities to developing multicellular organisms, making them good model systems for ontogenetic studies of collective behaviour. However, exhaustive time series and three-dimensional data are required to thoroughly characterize the different life stages of the collective structures and the transitions between these stages. The well-established fields of embryology and developmental biology offer practical tools and theoretical frameworks that could speed up the acquisition of new knowledge about the formation, development, maturity and dissolution of social insect self-assemblages and, by extension, other superorganismal behaviours. We hope that this review will encourage an expansion of the ontogenetic perspective in the field of collective behaviour and, in particular, in self-assemblage research, which has far-reaching applications in robotics, computer science and regenerative medicine. This article is part of a discussion meeting issue 'Collective behaviour through time'.

Computational capability of ecological dynamics

Ecological dynamics is driven by complex ecological networks. Computational capabilities of artificial networks have been exploited for machine learning purposes, yet whether an ecological network possesses a computational capability and whether/how we can use it remain unclear. Here, we developed two new computational/empirical frameworks based on reservoir computing and show that ecological dynamics can be used as a computational resource. In silico ecological reservoir computing (ERC) reconstructs ecological dynamics from empirical time series and uses simulated system responses for information processing, which can predict near future of chaotic dynamics and emulate nonlinear dynamics. The real-time ERC uses real population dynamics of a unicellular organism, Tetrahymena thermophila. The temperature of the medium is an input signal and population dynamics is used as a computational resource. Intriguingly, the real-time ecological reservoir has necessary conditions for computing (e.g. synchronized dynamics in response to the same input sequences) and can make near-future predictions of empirical time series, showing the first empirical evidence that population-level phenomenon is capable of real-time computations. Our finding that ecological dynamics possess computational capability poses new research questions for computational science and ecology: how can we efficiently use it and how is it actually used, evolved and maintained in an ecosystem?

There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines

The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., a tendency to oversimplify) and prior technological limitations in favor of a more continuous view, necessitated by the study of evolution, developmental biology, and intelligent machines. Form and function are tightly entwined in nature, and in some cases, in robotics as well. Thus, efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as “polycomputing”—the ability of the same substrate to simultaneously compute different things, and make those computational results available to different observers. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of their computational materials, as reported in the rapidly growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of mesoscale events, as it has already done at quantum and relativistic scales. To develop our understanding of how life performs polycomputing, and how it can be convinced to alter one or more of those functions, we can first create technologies that polycompute and learn how to alter their functions. Here, we review examples of biological and technological polycomputing, and develop the idea that the overloading of different functions on the same hardware is an important design principle that helps to understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as to evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.

Amorphous entangled active matter

The design of amorphous entangled systems, specifically from soft and active materials, has the potential to open exciting new classes of active, shape-shifting, and task-capable 'smart' materials. However, the global emergent mechanics that arise from the local interactions of individual particles are not well understood. In this study, we examine the emergent properties of amorphous entangled systems in an in silico collection of u-shaped particles ("smarticles") and in living entangled aggregate of worm blobs (L. variegatus). In simulations, we examine how material properties change for a collective composed of smarticles as they undergo different forcing protocols. We compare three methods of controlling entanglement in the collective: external oscillations of the ensemble, sudden shape-changes of all individuals, and sustained internal oscillations of all individuals. We find that large-amplitude changes of the particle's shape using the shape-change procedure produce the largest average number of entanglements, with respect to the aspect ratio (l/w), thus improving the tensile strength of the collective. We demonstrate applications of these simulations by showing how the individual worm activity in a blob can be controlled through the ambient dissolved oxygen in water, leading to complex emergent properties of the living entangled collective, such as solid-like entanglement and tumbling. Our work reveals principles by which future shape-modulating, potentially soft robotic systems may dynamically alter their material properties, advancing our understanding of living entangled materials, while inspiring new classes of synthetic emergent super-materials.

MorphoSim: an efficient and scalable phase-field framework for accurately simulating multicellular morphologies

The phase field model can accurately simulate the evolution of microstructures with complex morphologies, and it has been widely used for cell modeling in the last two decades. However, compared to other cellular models such as the coarse-grained model and the vertex model, its high computational cost caused by three-dimensional spatial discretization hampered its application and scalability, especially for multicellular organisms. Recently, we built a phase field model coupled with in vivo imaging data to accurately reconstruct the embryonic morphogenesis of Caenorhabditis elegans from 1- to 8-cell stages. In this work, we propose an improved phase field model by using the stabilized numerical scheme and modified volume constriction. Then we present a scalable phase-field framework, MorphoSim, which is 100 times more efficient than the previous one and can simulate over 100 mechanically interacting cells. Finally, we demonstrate how MorphoSim can be successfully applied to reproduce the assembly, self-repairing, and dissociation of a synthetic artificial multicellular system - the synNotch system.

Control-Oriented Models for Hyperelastic Soft Robots Through Differential Geometry of Curves

The motion complexity and use of exotic materials in soft robotics call for accurate and computationally efficient models intended for control. To reduce the gap between material and control-oriented research, we build upon the existing piece-wise constant curvature framework by incorporating hyperelastic and viscoelastic material behavior. In this work, the continuum dynamics of the soft robot are derived through the differential geometry of spatial curves, which are then related to finite-element data to capture the intrinsic geometric and material nonlinearities. To enable fast simulations, a reduced-order integration scheme is introduced to compute the dynamic Lagrangian matrices efficiently, which in turn allows for real-time (multilink) models with sufficient numerical precision. By exploring the passivity and using the parameterization of the hyperelastic model, we propose a passivity-based adaptive controller that enhances robustness toward material uncertainty and unmodeled dynamics-slowly improving their estimates online. As a study-case, a soft robot manipulator is developed through additive manufacturing, which shows good correspondence with the dynamic model under various conditions, for example, natural oscillations, forced inputs, and under tip-loads. The solidity of the approach is demonstrated through extensive simulations, numerical benchmarks, and experimental validations.

Three-dimensional printing of mycelium hydrogels into living complex materials

Biological living materials, such as animal bones and plant stems, are able to self-heal, regenerate, adapt and make decisions under environmental pressures. Despite recent successful efforts to imbue synthetic materials with some of these remarkable functionalities, many emerging properties of complex adaptive systems found in biology remain unexplored in engineered living materials. Here, we describe a three-dimensional printing approach that harnesses the emerging properties of fungal mycelia to create living complex materials that self-repair, regenerate and adapt to the environment while fulfilling an engineering function. Hydrogels loaded with the fungus Ganoderma lucidum are three-dimensionally printed into lattice architectures to enable mycelial growth in a balanced exploration and exploitation pattern that simultaneously promotes colonization of the gel and bridging of air gaps. To illustrate the potential of such mycelium-based living complex materials, we three-dimensionally print a robotic skin that is mechanically robust, self-cleaning and able to autonomously regenerate after damage.

Turning biology to life: some reflections

This essay presents various reflections on living systems, what they are and how they evolve, prompted by editing Teleonomy in Living Systems (a special issue of the Biological Journal of the Linnean Society). Conclusions include the suggestion that the linked notions of teleonomy and agency represent fundamental properties of matter that become apparent only when organized in the way that we consider to be that of a living system. As such, they are factors that form part of the intrinsic ‘a priori’ of living systems, as they evolve in form through space and time. Biology, the science of life and living systems, needs to be ‘biological’ if it is to be anything at all. Understanding the role of teleonomy (internal, inherent goal-seeking) will always play a necessary part in this endeavour: teleonomy represents one of the fundamental properties of living systems.

Mind the matter: Active matter, soft robotics, and the making of bio-inspired artificial intelligence

Philosophical and theoretical debates on the multiple realisability of the cognitive have historically influenced discussions of the possible systems capable of instantiating complex functions like memory, learning, goal-directedness, and decision-making. These debates have had the corollary of undermining, if not altogether neglecting, the materiality and corporeality of cognition-treating material, living processes as "hardware" problems that can be abstracted out and, in principle, implemented in a variety of materials-in particular on digital computers and in the form of state-of-the-art neural networks. In sum, the matter in se has been taken not to matter for cognition. However, in this paper, we argue that the materiality of cognition-and the living, self-organizing processes that it enables-requires a more detailed assessment when understanding the nature of cognition and recreating it in the field of embodied robotics. Or, in slogan form, that the matter matters for cognitive form and function. We pull from the fields of Active Matter Physics, Soft Robotics, and Basal Cognition literature to suggest that the imbrication between material and cognitive processes is closer than standard accounts of multiple realisability suggest. In light of this, we propose upgrading the notion of multiple realisability from the standard version-what we call 1.0-to a more nuanced conception 2.0 to better reflect the recent empirical advancements, while at the same time averting many of the problems that have been raised for it. These fields are actively reshaping the terrain in which we understand materiality and how it enables, mediates, and constrains cognition. We propose that taking the materiality of our embodied, precarious nature seriously furnishes an important research avenue for the development of embodied robots that autonomously value, engage, and interact with the environment in a goal-directed manner, in response to existential needs of survival, persistence, and, ultimately, reproduction. Thus, we argue that by placing further emphasis on the soft, active, and plastic nature of the materials that constitute cognitive embodiment, we can move further in the direction of autonomous embodied robots and Artificial Intelligence.

Recent trends in bioartificial muscle engineering and their applications in cultured meat, biorobotic systems and biohybrid implants

Recent advances in tissue engineering and biofabrication technology have yielded a plethora of biological tissues. Among these, engineering of bioartificial muscle stands out for its exceptional versatility and its wide range of applications. From the food industry to the technology sector and medicine, the development of this tissue has the potential to affect many different industries at once. However, to date, the biofabrication of cultured meat, biorobotic systems, and bioartificial muscle implants are still considered in isolation by individual peer groups. To establish common ground and share advances, this review outlines application-specific requirements for muscle tissue generation and provides a comprehensive overview of commonly used biofabrication strategies and current application trends. By solving the individual challenges and merging various expertise, synergetic leaps of innovation that inspire each other can be expected in all three industries in the future.

Fake cells and the aura of life: A philosophical diagnostic of synthetic life

Synthetic biology is often seen as the engineering turn in biology. Philosophically speaking, entities created by synthetic biology, from synthetic cells to xenobots, challenge the ontological divide between the organic and inorganic, as well as between the natural and the artificial. Entities such as synthetic cells can be seen as hybrid or transitory objects, or neo-things. However, what has remained philosophically underexplored so far is the impact these hybrid neo-things will have on (our phenomenological experience of) the living world. By extrapolating from Walter Benjamin's account of how technological reproducibility affects the aura of art, we embark upon an exploratory inquiry that seeks to fathom how the technological reproducibility of life itself may influence our experience and understanding of the living. We conclude that, much as technologies that enabled reproduction corroded the aura of original artworks (as Benjamin argued), so too will the aura of life be under siege in the era of synthetic lifeforms. This article zooms in on a specific case study, namely the research project Building a Synthetic Cell (BaSyC) and its mission to create a synthetic cell-like entity, as autonomous as possible, focusing on the properties that differentiate organic from synthetic cells.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Operant conditioning of stochastic chemical reaction networks

Adapting one’s behavior to environmental conditions and past experience is a key trait of living systems. In the biological world, there is evidence for adaptive behaviors such as learning even in naturally occurring, non-neural, single-celled organisms. In the bioengineered world, advances in synthetic cell engineering and biorobotics have created the possibility of implementing lifelike systems engineered from the bottom up. This will require the development of programmable control circuitry for such biomimetic systems that is capable of realizing such non-trivial and adaptive behavior, including modification of subsequent behavior in response to environmental feedback. To this end, we report the design of novel stochastic chemical reaction networks capable of probabilistic decision-making in response to stimuli. We show that a simple chemical reaction network motif can be tuned to produce arbitrary decision probabilities when choosing between two or more responses to a stimulus signal. We further show that simple feedback mechanisms from the environment can modify these probabilities over time, enabling the system to adapt its behavior dynamically in response to positive or negative reinforcement based on its decisions. This system thus acts as a form of operant conditioning of the chemical circuit, in the sense that feedback provided based on decisions taken by the circuit form the basis of the learning process. Our work thus demonstrates that simple chemical systems can be used to implement lifelike behavior in engineered biomimetic systems.

Exploring standards for multicellular mammalian synthetic biology

Synthetic biology is moving towards bioengineering multicellular mammalian systems that are poised to advance tissue engineering, biomedicine, and the food industry. Despite progress, the field lacks a framework of standards that could greatly accelerate further development. Here, we explore the landscape of standards for multicellular mammalian synthetic biology. We discuss the limits of current technical standards and categorise unaddressed parameters into an abstraction hierarchy. We then define the concept of a 'synthetic multicellular mammalian system' and apply our standard hierarchy framework to illustrate how it could aid bioengineering endeavours. We conclude with promising areas that could shape the future of the field, flagging the need for a critical and holistic consideration of standards that requires cross-disciplinary dialogue.

Counting with Cilia: The Role of Morphological Computation in Basal Cognition Research

"Morphological computation" is an increasingly important concept in robotics, artificial intelligence, and philosophy of the mind. It is used to understand how the body contributes to cognition and control of behavior. Its understanding in terms of "offloading" computation from the brain to the body has been criticized as misleading, and it has been suggested that the use of the concept conflates three classes of distinct processes. In fact, these criticisms implicitly hang on accepting a semantic definition of what constitutes computation. Here, I argue that an alternative, mechanistic view on computation offers a significantly different understanding of what morphological computation is. These theoretical considerations are then used to analyze the existing research program in developmental biology, which understands morphogenesis, the process of development of shape in biological systems, as a computational process. This important line of research shows that cognition and intelligence can be found across all scales of life, as the proponents of the basal cognition research program propose. Hence, clarifying the connection between morphological computation and morphogenesis allows for strengthening the role of the former concept in this emerging research field.

Cell clusters adopt a collective amoeboid mode of migration in confined nonadhesive environments

Cell migration is essential to living organisms and deregulated in cancer. Single cell's migration ranges from traction-dependent mesenchymal motility to contractility-driven propulsive amoeboid locomotion, but collective cell migration has only been described as a focal adhesion-dependent and traction-dependent process. Here, we show that cancer cell clusters, from patients and cell lines, migrate without focal adhesions when confined into nonadhesive microfabricated channels. Clusters coordinate and behave like giant super cells, mobilizing their actomyosin contractility at the rear to power their migration. This polarized cortex does not sustain persistent retrograde flows, of cells or actin, like in the other modes of migration but rather harnesses fluctuating cell deformations, or jiggling. Theoretical physical modeling shows this is sufficient to create a gradient of friction forces and trigger directed cluster motion. This collective amoeboid mode of migration could foster metastatic spread by enabling cells to cross a wide spectrum of environments.

On the Criticality of Adaptive Boolean Network Robots

Systems poised at a dynamical critical regime, between order and disorder, have been shown capable of exhibiting complex dynamics that balance robustness to external perturbations and rich repertoires of responses to inputs. This property has been exploited in artificial network classifiers, and preliminary results have also been attained in the context of robots controlled by Boolean networks. In this work, we investigate the role of dynamical criticality in robots undergoing online adaptation, i.e., robots that adapt some of their internal parameters to improve a performance metric over time during their activity. We study the behavior of robots controlled by random Boolean networks, which are either adapted in their coupling with robot sensors and actuators or in their structure or both. We observe that robots controlled by critical random Boolean networks have higher average and maximum performance than that of robots controlled by ordered and disordered nets. Notably, in general, adaptation by change of couplings produces robots with slightly higher performance than those adapted by changing their structure. Moreover, we observe that when adapted in their structure, ordered networks tend to move to the critical dynamical regime. These results provide further support to the conjecture that critical regimes favor adaptation and indicate the advantage of calibrating robot control systems at dynamical critical states.

Competition for finite resources as coordination mechanism for morphogenesis: An evolutionary algorithm study of digital embryogeny

The standard view of embryogenesis is one of cooperation driven by the cells' shared genetics and evolutionary interests. However, numerous examples from developmental biology and agriculture reveal a surprising amount of competition among body cells, tissues, and organs for both metabolic and informational resources. To explain the existence of such competition we had hypothesized that evolution uses limiting "reservoirs" of resource molecules as a communication medium - a global scratchpad, to enable tissues across the body to coordinate growth. Here, we test this hypothesis via an evolutionary simulation of embryogeny in silico. Genomes encode state transition rules for cells, such as proliferation, differentiation, and resource use, enabling virtual embryos to develop a specific large-scale morphology. An evolutionary algorithm operates over the genomes, with fitness defined as a function of specific morphological requirements for the final embryo shape. We found that not only does such an algorithm rapidly discover rules for cellular behavior that reliably make embryos with specific anatomical properties, but that it discovers the strategy of using finite resources to coordinate development. Given the option of using finite or infinite reservoirs (which determine cells' ability to carry out specific actions), evolution preferentially uses finite reservoirs, which results in higher fitness and increased consistency (without needing direct selection for morphological invariance). We report aspects of anatomical, physiological/transcriptional, and genomic analysis of evolved virtual embryos that help understand how evolution can use competition among genetically identical subunits within a multicellular body to coordinate reliable, complex morphogenesis. Our results suggest that under some conditions, composite multi-scale systems will promote conflict and artificial scarcity for their components.

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.