# Information Processing Capability of Soft Continuum Arms

## TL;DR

This paper investigates the information processing capabilities of soft continuum arms, demonstrating their potential for reservoir computing by analyzing their dynamic responses to various input signals.

## Contribution

It introduces the concept of using soft continuum arms as computational reservoirs, exploring their information processing potential for the first time.

## Key findings

- Processing capability varies with input signal bandwidth and amplitude
- Optimal bandwidth and amplitude exist for reservoir computing implementation
- Preliminary results show potential for soft arms in computational tasks

## Abstract

Soft Continuum arms, such as trunk and tentacle robots, can be considered as the "dual" of traditional rigid-bodied robots in terms of manipulability, degrees of freedom, and compliance. Introduced two decades ago, continuum arms have not yet realized their full potential, and largely remain as laboratory curiosities. The reasons for this lag rest upon their inherent physical features such as high compliance which contribute to their complex control problems that no research has yet managed to surmount. Recently, reservoir computing has been suggested as a way to employ the body dynamics as a computational resource toward implementing compliant body control. In this paper, as a first step, we investigate the information processing capability of soft continuum arms. We apply input signals of varying amplitude and bandwidth to a soft continuum arm and generate the dynamic response for a large number of trials. These data is aggregated and used to train the readout weights to implement a reservoir computing scheme. Results demonstrate that the information processing capability varies across input signal bandwidth and amplitude. These preliminary results demonstrate that soft continuum arms have optimal bandwidth and amplitude where one can implement reservoir computing.