Harnessing natural embodied intelligence for spontaneous jellyfish cyborgs

Dai Owaki (), Max Austin, Shuhei Ikeda, Kazuya Okuizumi and Kohei Nakajima
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Dai Owaki: Tohoku University
Max Austin: The University of Tokyo
Shuhei Ikeda: Kamo Aquarium
Kazuya Okuizumi: Kamo Aquarium
Kohei Nakajima: The University of Tokyo

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Jellyfish cyborgs present a promising avenue for soft robotic systems, leveraging the natural energy-efficiency and adaptability of biological systems. Here we present an approach for predicting and controlling jellyfish locomotion by harnessing the natural embodied intelligence of these animals. We developed an integrated muscle electrostimulation and 3D motion capture system to quantify both spontaneous and stimulus-induced behaviors in Aurelia coerulea jellyfish. Our key findings include an investigation of self-organized criticality in jellyfish swimming motions and the identification of optimal periods of electro-stimulus input signal (1.5 and 2.0 seconds) for eliciting coherent and predictable swimming behaviors. Furthermore, using Reservoir Computing, a machine learning framework, we successfully predicted future movements of the stimulated jellyfish, which also characterizes how the jellyfish swimming motions are synchronized with the electro-stimulus. Our findings provide a foundation for developing jellyfish cyborgs capable of autonomous navigation and environmental exploration, with potential applications in ocean monitoring and pollution management.

Date: 2025
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DOI: 10.1038/s41467-025-59889-7

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