Evolutionary design of soft-bodied animats with decentralized control

We show how a biologically inspired model of multicellular development combined with a simulated evolutionary process can be used to design the morphologies and controllers of soft-bodied virtual animats. An animat’s morphology is the result of a developmental process that starts from a single cell and goes through many cell divisions, during which cells interact via simple physical rules. Every cell contains the same genome, which encodes a gene regulatory network (GRN) controlling its behavior. After the developmental stage, locomotion emerges from the coordinated activity of the GRNs across the virtual robot body. Since cells act autonomously, the behavior of the animat is generated in a truly decentralized fashion. The movement of the animat is produced by the contraction and expansion of parts of the body, caused by the cells, and is simulated using a physics engine. Our system makes possible the evolution and development of animats that can run, swim, and actively navigate toward a target in a virtual environment

Evolving soft locomotion in aquatic and terrestrial environments: effects of material properties and environmental transitions

Designing soft robots poses considerable challenges: automated design approaches may be particularly appealing in this field, as they promise to optimize complex multi-material machines with very little or no human intervention. Evolutionary soft robotics is concerned with the application of optimization algorithms inspired by natural evolution in order to let soft robots (both morphologies and controllers) spontaneously evolve within physically-realistic simulated environments, figuring out how to satisfy a set of objectives defined by human designers. In this paper a powerful evolutionary system is put in place in order to perform a broad investigation on the free-form evolution of walking and swimming soft robots in different environments. Three sets of experiments are reported, tackling different aspects of the evolution of soft locomotion. The first two sets explore the effects of different material properties on the evolution of terrestrial and aquatic soft locomotion: particularly, we show how different materials lead to the evolution of different morphologies, behaviors, and energy-performance tradeoffs. It is found that within our simplified physics world stiffer robots evolve more sophisticated and effective gaits and morphologies on land, while softer ones tend to perform better in water. The third set of experiments starts investigating the effect and potential benefits of major environmental transitions (land - water) during evolution. Results provide interesting morphological exaptation phenomena, and point out a potential asymmetry between land-water and water-land transitions: while the first type of transition appears to be detrimental, the second one seems to have some beneficial effects.Comment: 37 pages, 22 figures, currently under review (journal

Material properties affect evolution's ability to exploit morphological computation in growing soft-bodied creatures

The concept of morphological computation holds that the body of an agent can, under certain circumstances, exploit the interaction with the environment to achieve useful behavior, potentially reducing the computational burden of the brain/controller. The conditions under which such phenomenon arises are, however, unclear. We hypothesize that morphological computation will be facilitated by body plans with appropriate geometric, material, and growth properties, while it will be hindered by other body plans in which one or more of these three properties is not well suited to the task. We test this by evolving the geometries and growth processes of soft robots, with either manually-set softer or stiffer material properties. Results support our hypothesis: we find that for the task investigated, evolved softer robots achieve better performances with simpler growth processes than evolved stiffer ones. We hold that the softer robots succeed because they are better able to exploit morphological computation. This four-way interaction among geometry, growth, material properties and morphological computation is but one example phenomenon that can be investigated using the system here introduced, that could enable future studies on the evolution and development of generic soft-bodied creatures

Self-organization of Symbiotic Multicellular Structures

International audienceThis paper presents a new model for the development of artificial creatures from a single cell. The model aims at providing a more biologically plausible abstraction of the morphogenesis and the specialization process, which the organogenesis follows. It is built upon three main elements: a cellular physics system that simulates division and intercellular adhesion dynamics, a simplified cell cycle offering to the cells the possibility to select actions such as division, quiescence, differentiation or apoptosis and, finally, a cell specialization mechanism quantifying the ability to perform different functions. An evolved artificial gene regulatory network is employed as a cell controller. As a proof-of-concept, we present two experiments where the morphology of a multicellular organism is guided by cell weaknesses and efficiency at performing different functions under environmental stress

Toward Organogenesis of Artificial Creatures

International audienceThis paper presents a new model for the development of artificial creatures from a single cell. The model aims at providing a more biologically plausible abstraction of the morphogenesis and the specialization process, which the organogenesis follows. It is built upon three main elements: a cellular physics simulation, a simplified cell cycle using an evolved artificial gene regulatory network and a cell specialization mechanism quantifying the ability to perform different functions. As a proof-of-concept, we present a first experiment where the morphology of a multicellular organism is guided by cell weaknesses and efficiency at performing different functions under environmental stress

Self-organization of Symbiotic Multicellular Structures

This paper presents a new model for the development of artificial creatures from a single cell. The model aims at providing a more biologically plausible abstraction of the morphogenesis and the specialization process, which the organogenesis follows. It is built upon three main elements: a cellular physics system that simulates division and intercellular adhesion dynamics, a simplified cell cycle offering to the cells the possibility to select actions such as division, quiescence, differentiation or apoptosis and, finally, a cell specialization mechanism quantifying the ability to perform different functions. An evolved artificial gene regulatory network is employed as a cell controller. As a proof-of-concept, we present two experiments where the morphology of a multicellular organism is guided by cell weaknesses and efficiency at performing different functions under environmental stress

Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding

In 1994 Karl Sims showed that computational evolution can produce interesting morphologies that resemble natural organisms. Despite nearly two decades of work since, evolved morphologies are not obviously more complex or natural, and the field seems to have hit a complexity ceiling. One hypothesis for the lack of increased complexity is that most work, including Sims’, evolves morphologies composed of rigid elements, such as solid cubes and cylinders, limiting the design space. A second hypothesis is that the encodings of previous work have been overly regular, not allowing complex regularities with variation. Here we test both hypotheses by evolving soft robots with multiple materials and a powerful generative encoding called a compositional pattern-producing network (CPPN). Robots are selected for locomotion speed. We find that CPPNs evolve faster robots than a direct encoding and that the CPPN morphologies appear more natural. We also find that locomotion performance increases as more materials are added, that diversity of form and behavior can be increased with di↵erent cost functions without stifling performance, and that organisms can be evolved at di↵erent levels of resolution. These findings suggest the ability of generative soft-voxel systems to scale towards evolving a large diversity of complex, natural, multi-material creatures. Our results suggest that future work that combines the evolution of CPPNencoded soft, multi-material robots with modern diversityencouraging techniques could finally enable the creation of creatures far more complex and interesting than those produced by Sims nearly twenty years ago

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

Moving softly : the role of morphological computation in the generation of intelligent behaviour

The central theme of this thesis is ‘morphological computation’, in particular as it pertains to the generation of purposeful and intelligent behaviour. The first part of the thesis covers experiments in simulations, aimed at exploring a previously unasked question: can genuinely computational soft-body reservoirs play a central role in generating behaviour which has previously been described as ‘minimally cognitive’? We conclude in the affirmative, but also note that it remains unclear whether or how they can also exceed such abilities. The main part of this work has been presented at ALIFE 14 and ECAL 15, and published in the Artificial Life journal. In part two, the focus moves to consideration of how morphological computation in soft bodies may collude with the action of nervous systems in the production of adaptive intelligent behaviour. The massive and hypnotic complexity of brains in present day species, and lasting traditions in the design and control of animals’ mechanical analogies (robots), still lead many to perceive bodies in the neuromuscular species as being in service to the goals of their controllers (brains), but in fact the two are of a piece, and work together to the same ends. We hypothesise that this would have been more apparent than now at a time closer to the evolutionary introduction of neuromuscular systems, and introduce our new software framework for the evolution of virtual neuromuscular swimmers, which may be used to study artificial parallels to those distant origins. We begin with a demonstration of the technical innovations which we required in order to simulate soft-bodied swimmers in two-dimensional particle-based fluids, and then proceed to a description of our new concept for mapping arbitrary neural networks to arbitrary body morphologies