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

Author:

Joachimczak Michał,Suzuki Reiji,Arita Takaya1

Affiliation:

1. Nagoya University

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.

Publisher

MIT Press - Journals

Subject

Artificial Intelligence,General Biochemistry, Genetics and Molecular Biology

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1. Evolutionary Machine Learning in Robotics;Handbook of Evolutionary Machine Learning;2023-11-02

2. Premature convergence in morphology and control co-evolution: a study;Adaptive Behavior;2023-09-07

3. How the Morphology Encoding Influences the Learning Ability in Body-Brain Co-Optimization;Proceedings of the Genetic and Evolutionary Computation Conference;2023-07-12

4. Modular Controllers Facilitate the Co-Optimization of Morphology and Control in Soft Robots;Proceedings of the Genetic and Evolutionary Computation Conference;2023-07-12

5. Differentiable Soft-Robot Generation;Proceedings of the Genetic and Evolutionary Computation Conference;2023-07-12

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