Data from: 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 foundation
for developing jellyfish cyborgs capable of autonomous navigation and
environmental exploration, with potential applications in ocean monitoring
and pollution management.