Our Objective

Our objective is to develop a computational analog to living octopuses, a CyberOctopus that adapts, learns, and evolves to novel tasks, situations, and environments. Our rationale is that this software suite will drive the convergence and synthesis of novel control theories and algorithms, simulation of soft-bodied creatures, and new insights into cephalopod neurodynamics enabled by unprecedented in-vivo sensing techniques. Overall, our efforts will provide a framework towards the integration of octopus biophysical principles into modern engineering design and control systems, and will form a solid foundation to enable further investigations beyond the time-frame of this MURI.

Research Thrusts

Experiment design to elucidate neurodynamic mechanisms

The MURI team will design a set of biological experiments to elucidate the neurodynamic principles underlying octopus’ abilities, and ontogeny of behavioral adaptation in cephalopods.

Development of in-vivo/ex-vivo sensors

This research thrust will bring together established and novel sensing technologies into a unique “full view” testing environment to simultaneously acquire in-vivo and ex-vivo data from freely moving octopuses. These sensing technologies include novel conformal and wireless electronics to be surgically implanted, machine vision systems that can detect behavioral primitives, and high-resolution pressure sensors patterned in the octopus’ environment.

The CyberOctopus: distributed control in a distributed body

 In this thrust, the team will develop a virtual environment in which dynamic models of octopus neurons and derived control abstractions can be tested in a biophysically accurate muscular system. To simulate the highly compliant octopus body, we will use a novel approach based on Cosserat rods, and model its neural infrastructure using data from the previous thrusts relative to the different organizational levels in the octopus nervous system.

Mathematics of distributed learning & control

The MURI team will distill experiments and simulation results into theory, algorithms, and software to enable the CyberOctopus to learn complex sensori motor tasks quickly from experience. Our work will result in new learning models that are fundamentally more capable of handling distributed dynamic systems characterized by large numbers of degrees of freedom.