  In the advanced field of bio-inspired robotics, the emergence of cyborgs
represents the successful integration of engineering and biological systems.
Building on previous research that showed how electrical stimuli could initiate
and speed up a jellyfish's movement, this study presents a groundbreaking
approach that explores how the natural embodied intelligence of the animal can
be harnessed to address pivotal challenges such as spontaneous exploration,
navigation in various environments, control of whole-body motion, and real-time
predictions of behavior. We have developed a comprehensive data acquisition
system and a unique setup for stimulating jellyfish, allowing for a detailed
study of their movements. Through careful analysis of both spontaneous
behaviors and behaviors induced by targeted stimulation, we have identified
subtle differences between natural and induced motion patterns. By using a
machine learning method called physical reservoir computing, we have
successfully shown that future behaviors can be accurately predicted by
directly measuring the jellyfish's body shape when the stimuli align with the
animal's natural dynamics. Our findings also reveal significant advancements in
motion control and real-time prediction capabilities of jellyfish cyborgs. In
summary, this research provides a comprehensive roadmap for optimizing the
capabilities of jellyfish cyborgs, with potential implications in marine
reconnaissance and sustainable ecological interventions.
