Behaviour Diversity in a Walking and Climbing Centipede-Like Virtual Creature

Robot controllers are often optimized for a single robot in a single environment. This approach proves brittle, as such a controller will often fail to produce sensible behavior for a new morphology or environment. In comparison, animal gaits are robust and versatile. By observing animals, and attempting to extract general principles of locomotion from their movement, we aim to design a single, decentralized controller applicable to diverse morphologies and environments. The controller implements the three components of (a) undulation, (b) peristalsis, and (c) leg motion, which we believe are the essential elements in most animal gaits. This work is a first step toward a general controller. Accordingly, the controller has been evaluated on a limited range of simulated centipede-like robot morphologies. The centipede is chosen as inspiration because it moves using both body contractions and legged locomotion. For a controller to work in qualitatively different settings, it must also be able to exhibit qualitatively different behaviors. We find that six different modes of locomotion emerge from our controller in response to environmental and morphological changes. We also find that different parts of the centipede model can exhibit different modes of locomotion, simultaneously, based on local morphological features. This controller can potentially aid in the design or evolution of robots, by quickly testing the potential of a morphology, or be used to get insights about underlying locomotion principles in the centipede.

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Publication Details

Journal
Artificial Life
Published
2025-01-01
DOI
https://doi.org/10.1162/artl_a_00476
Citations
2
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
3.39
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article

Behaviour Diversity in a Walking and Climbing Centipede-Like Virtual Creature

Kotaro Yasui, Emma Stensby Norstein, Kyrre Glette, Takeshi Kano et al.
2 citations
Artificial Life
Teaching and Learning Programming
3.39
article

Behaviour Diversity in a Walking and Climbing Centipede-Like Virtual Creature

Kotaro Yasui, Emma Stensby Norstein, Kyrre Glette, Takeshi Kano, Akio Ishiguro
article en
2 citations

Abstract

Robot controllers are often optimized for a single robot in a single environment. This approach proves brittle, as such a controller will often fail to produce sensible behavior for a new morphology or environment. In comparison, animal gaits are robust and versatile. By observing animals, and attempting to extract general principles of locomotion from their movement, we aim to design a single, decentralized controller applicable to diverse morphologies and environments. The controller implements the three components of (a) undulation, (b) peristalsis, and (c) leg motion, which we believe are the essential elements in most animal gaits. This work is a first step toward a general controller. Accordingly, the controller has been evaluated on a limited range of simulated centipede-like robot morphologies. The centipede is chosen as inspiration because it moves using both body contractions and legged locomotion. For a controller to work in qualitatively different settings, it must also be able to exhibit qualitatively different behaviors. We find that six different modes of locomotion emerge from our controller in response to environmental and morphological changes. We also find that different parts of the centipede model can exhibit different modes of locomotion, simultaneously, based on local morphological features. This controller can potentially aid in the design or evolution of robots, by quickly testing the potential of a morphology, or be used to get insights about underlying locomotion principles in the centipede.

Artificial LifeVol. 31(3)
University of Oslo (NO), Tohoku University (JP), Future University Hakodate (JP)
Openalex Percentile: Top 6%
Teaching and Learning Programming
3.39
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Behaviour Diversity in a Walking and Climbing Centipede-Like Virtual Creature — Kotaro Yasui, Emma Stensby Norstein, et al. · Artificial Life (2025) | TGRS Research Map | TGRS