Literature DB >> 27139940

Artificial Metamorphosis: Evolutionary Design of Transforming, Soft-Bodied Robots.

Michał Joachimczak, Reiji Suzuki, Takaya Arita1.   

Abstract

We show how the concept of metamorphosis, together with a biologically inspired model of multicellular development, can be used to evolve soft-bodied robots that are adapted to two very different tasks, such as being able to move in an aquatic and in a terrestrial environment. Each evolved solution defines two pairs of morphologies and controllers, together with a process of transforming one pair into the other. Animats develop from a single cell and grow through cellular divisions and deaths until they reach an initial larval form adapted to a first environment. To obtain the adult form adapted to a second environment, the larva undergoes metamorphosis, during which new cells are added or removed and its controller is modified. Importantly, our approach assumes nothing about what morphologies or methods of locomotion are preferred. Instead, it successfully searches the vast space of possible designs and comes up with complex, surprising, lifelike solutions that are reminiscent of amphibian metamorphosis. We analyze obtained solutions and investigate whether the morphological changes during metamorphosis are indeed adaptive. We then compare the effectiveness of three different types of selective pressures used to evolve metamorphic individuals. Finally, we investigate potential advantages of using metamorphosis to automatically produce soft-bodied designs by comparing the performance of metamorphic individuals with their specialized counterparts and designs that are robust to both environments.

Keywords:  Metamorphosis; artificial development; artificial life; automated design; body-brain coevolution; evolutionary algorithm; soft robotics

Mesh:

Year:  2016        PMID: 27139940     DOI: 10.1162/ARTL_a_00207

Source DB:  PubMed          Journal:  Artif Life        ISSN: 1064-5462            Impact factor:   0.667


  2 in total

1.  Scalable co-optimization of morphology and control in embodied machines.

Authors:  Nick Cheney; Josh Bongard; Vytas SunSpiral; Hod Lipson
Journal:  J R Soc Interface       Date:  2018-06       Impact factor: 4.118

2.  Phenotypic complexity and evolvability in evolving robots.

Authors:  Nicola Milano; Stefano Nolfi
Journal:  Front Robot AI       Date:  2022-10-04
  2 in total

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