Pith. sign in

REVIEW 1 cited by

Neuroevolution of Decentralized Decision-Making in N-Bead Swimmers Leads to Scalable and Robust Collective Locomotion

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.09438 v2 pith:PF5WXNLP submitted 2024-07-12 physics.bio-ph cond-mat.softnlin.AOphysics.comp-phphysics.flu-dyn

classification physics.bio-phcond-mat.softnlin.AOphysics.comp-phphysics.flu-dyn
keywords decentralizedartificiallocomotionrobustshapebodycollectivedecision-making
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Many microorganisms swim by performing larger non-reciprocal shape deformations that are initiated locally by molecular motors. However, it remains unclear how decentralized shape control determines the movement of the entire organism. Here, we investigate how efficient locomotion emerges from coordinated yet simple and decentralized decision-making of the body parts using neuroevolution techniques. Our approach allows us to investigate optimal locomotion policies for increasingly large microswimmer bodies, with emerging long-wavelength body shape deformations corresponding to surprisingly efficient swimming gaits. The obtained decentralized policies are robust and tolerant concerning morphological changes or defects and can be applied to artificial microswimmers for cargo transport or drug delivery applications without further optimization "out of the box". Our work is of relevance to understanding and developing robust navigation strategies of biological and artificial microswimmers and, in a broader context, for understanding emergent levels of individuality and the role of collective intelligence in Artificial Life.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Heuristically Adaptive Diffusion-Model Evolutionary Strategy

    cs.NE 2024-11 conditional novelty 7.0 of 10

    An evolutionary algorithm that uses an online-trained diffusion model as its offspring generator can adapt to changing fitness landscapes and condition the search toward target traits without altering the fitness function.

Pith tools