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

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

Evolutionary discovery of self-stabilized dynamic gaits for a soft underwater legged robot

In recent years a number of robotic platforms have been developed, that are capable of robust locomotion in presence of a simple open loop control. Relying on the self-stabilizing properties of their mechanical structure, morphology assumes a crucial role in the design process, that is, however, usually guided by a set of heuristic principles falling under what is commonly known as embodied intelligence. Despite many impressive demonstrations, the result of such a methodology may be sub-optimal, given the dimension of the design space and the complex intertwining of involved dynamical effects. Encouraged by the growing consensus that embodied solutions can indeed be produced by bio-inspired computational techniques in a more automated manner, this work proposes a computer-aided methodology to explore in simulation the design space of an existing robot, by harnessing computational techniques inspired by natural evolution. Although many works exist on the application of evolutionary algorithms in robotics, few of them embrace this design perspective. The idea is to have an evolutionary process suggesting to the human designer a number of interesting robot configurations and embodied behaviors, from whose analysis design hints can be gained to improve the platform. The focus will be on enhancing the locomotion capabilities of a multi-legged, soft, underwater robot. We investigate for the first time the suitability of a recently introduced open-ended evolutionary algorithm (novelty search) for the intended study, and demonstrate its benefits in the comparison with a more conventional genetic algorithm. Results confirm that evolutionary algorithms are indeed capable of producing new, elaborate dynamic gaits, with evolved designs exhibiting several regularities. Possible future directions are also pointed out, in which the passive exploitation of robot's morphological features could bring additional advantages in achieving diverse, robust behaviors

The Watchmaker's guide to Artificial Life: On the Role of Death, Modularity and Physicality in Evolutionary Robotics

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