Harnessing natural embodied intelligence for spontaneous jellyfish cyborgs

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 fou..., , # Harnessing natural embodied intelligence for spontaneous jellyfish cyborgs (2025)

A brief summary of dataset:
Time series data on the three-dimensional swimming locomotion trajectories of an individual jellyfish (ID: JF41) in a water tank environment. It encompasses eight marker positions (R1, R2, O1, O2, Y1, Y2, B1, B3) and examines two distinct conditions: spontaneous swimming (free) and muscle electrical stimulation (stim). The analysis is conducted across five different stimulus parameters, specifically the burst period (tau).

Journal: Nature Communications
Title: Harnessing Natural Embodied Intelligence for Spontaneous Jellyfish Cyborgs
Authors: Dai Owaki(1), Max Austin(2), Shuhei Ikeda(3), Kazuya Okuizumi(3), Kohei Nakajima(2)
Affiliations:
(1)Department of Robotics, Graduate School of Engineering,Tohoku University, 6-6-01 Aoba, Aramaki, Aoba-ku, Sendai, 980-8579, Japan.
(2)Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, ...,