Spider web-inspired sensing and computation with fiber network physical reservoirs

TL;DR

This paper explores a spider web-inspired fiber network as a physical reservoir for computation, analyzing how design and operating conditions influence its nonlinear processing and memory capabilities, with experimental validation.

Contribution

It introduces a fiber network design inspired by spider webs for physical reservoir computing and investigates how topology, geometry, and tension affect its computational performance.

Findings

Network topology and tension significantly influence computational capacity.

Readout reduction strategies impact performance based on output placement.

Experimental results validate simulation insights and design guidance.

Abstract

Physical reservoir computing leverages the intrinsic dynamics of mechanical systems to perform computation through their natural responses to input signals. Here, we study a compliant fiber network inspired by orb-weaving spider webs and investigate how its mechanical design and operating conditions shape its computational capability. Using Cosserat rod-based simulations, we identify how network topology, geometry, actuation, and axial tension impact the nonlinear computation and memory capacity of the network. We further evaluate several readout reduction strategies to assess how computational performance varies with the number and placement of measured outputs. We then experimentally validate these results using a physical fiber-network prototype. Overall, results provide insights and guidance on design, actuation, and sensing choices to enable fiber networks for mechano-intelligent computation. They demonstrate the ability of structured compliant fibers networks to serve as physical reservoirs capable of nonlinear transformation and input-history retention.