Novelty Search for Soft Robotic Space Exploration

Methenitis, Georgios; Hennes, Daniel; Izzo, Dario; Visser, A.

doi:10.1145/2739480.2754731

Cited by 36 publications

(34 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The genetic representation is based on a very general and evolvable network encoding known as Compositional Pattern Producing Networks (CPPNs) [76], which is used in combination with the VoxCad simulator as in a number of previous studies [51] [53] [55] [54] [56]. Designed to capture the formation of regular patterns during development without modeling developmental dynamics per se [77], CPPNs encode the genetic material as a composition of functions, represented in the form of a graph, or network.…”

Section: Compositional Pattern Producing Network (Cppns)mentioning

confidence: 99%

Evolving Soft Locomotion in Aquatic and Terrestrial Environments: Effects of Material Properties and Environmental Transitions

et al. 2018

View full text Add to dashboard Cite

Designing soft robots poses considerable challenges; automated design approaches may be particularly appealing in this field, as they promise to optimize complex multimaterial machines with very little or no human intervention. Evolutionary soft robotics is concerned with the application of optimization algorithms inspired by natural evolution to let soft robots (both their 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 article, a powerful evolutionary system is put in place to perform a broad investigation on the free-form evolution of simulated 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 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 trade-offs. 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.

show abstract

Section: Compositional Pattern Producing Network (Cppns)mentioning

confidence: 99%

Evolving Soft Locomotion in Aquatic and Terrestrial Environments: Effects of Material Properties and Environmental Transitions

et al. 2018

View full text Add to dashboard Cite

show abstract

“…As these features are ubiquitous in natural systems, their presence here may suggest the potential for more competent and scalable virtual creatures. Moreover, these properties appear to be selected for by our task and environment, as this level of morphological regularity is not common in similar settings (Cheney et al, 2013(Cheney et al, , 2015Methenitis et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

“…Third, differently from other works adopting VoxCad (Cheney et al, 2015(Cheney et al, , 2013Methenitis et al, 2015), in this experiment there is no fast-twitch actuation mechanism (i.e. the fast control based on an oscillating global signal is disabled).…”

Section: Methodsmentioning

confidence: 92%

Material properties affect evolutions ability to exploit morphological computation in growing soft-bodied creatures

Bongard

Laschi

Lipson

et al. 2016

Proceedings of the Artificial Life Conference 2016

View full text Add to dashboard Cite

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.

show abstract

“…Several studies have built off of the results of Cheney et al (2013). Different environmental settings have been studied, including aquatic environments (Corucci et al, 2016), different gravitational environments (Methenitis et al, 2015), and constricted spaces (Cheney et al, 2015). New approaches to encoding the robots using a developmental lifespan have shown promise (Kriegman et al, 2017).…”

Section: Previous Workmentioning

confidence: 99%

Behavioral search drivers and the role of elitism in soft robotics

Cava¹,

Moore²

2018

The 2018 Conference on Artificial Life

View full text Add to dashboard Cite

Behavioral search drivers allow more information about the behavior of individuals in an environment to be used during selection. In this paper, we examine several selection methods based on de-aggregating the motion of soft robots into behavior vectors used to drive search. We adapt three behavioral search drivers to this task:-lexicase selection, discovery of objectives by clustering, and novelty search. These methods are compared to age-fitness pareto optimization and random search. We analyze how these search drivers affect the diversity and quality of soft robots that are tasked with moving as far of a distance as possible. Perhaps the most surprising finding is that random search with elitism is competitive with previously published methods. Overall, we find that elitism plays an important role in the ability to find high fitness solutions, and that lexicase selection and discovery of objectives by clustering with elitism tend to produce the most fit solutions.

show abstract

Novelty Search for Soft Robotic Space Exploration

Cited by 36 publications

References 16 publications

Evolving Soft Locomotion in Aquatic and Terrestrial Environments: Effects of Material Properties and Environmental Transitions

Evolving Soft Locomotion in Aquatic and Terrestrial Environments: Effects of Material Properties and Environmental Transitions

Material properties affect evolutions ability to exploit morphological computation in growing soft-bodied creatures

Behavioral search drivers and the role of elitism in soft robotics

Contact Info

Product

Resources

About