A Jellyfish Cyborg: Exploiting Natural Embodied Intelligence as Soft Robots

TL;DR

This paper demonstrates how leveraging the natural embodied intelligence of jellyfish, combined with machine learning, can enable predictive control of their locomotion for soft robotic applications like ocean monitoring.

Contribution

It introduces a novel method using reservoir computing and electrostimulation to predict and control jellyfish movements, advancing bio-hybrid robotic systems.

Findings

First investigation of self-organized criticality in jellyfish swimming

Identification of optimal stimulus periods for predictable behavior

Demonstration of jellyfish as computational resources for control

Abstract

Jellyfish cyborgs present a promising avenue for soft robotic systems, leveraging the natural energy-efficiency and adaptability of biological systems. Here we demonstrate a novel approach to 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. Using Reservoir Computing, a machine learning framework, we successfully predicted future movements based on the current body shape and natural dynamic patterns of the jellyfish. Our key findings include the first investigation of self-organized criticality in jellyfish swimming motions and the identification of optimal stimulus periods (1.5 and 2.0 seconds) for eliciting coherent and predictable swimming behaviors. These results suggest that the jellyfish body motion, combined with targeted electrostimulation, can serve as a computational resource for predictive control. Our findings pave the way for developing jellyfish cyborgs capable of autonomous navigation and environmental exploration, with potential applications in ocean monitoring and pollution management.