Re-purposing a modular origami manipulator into an adaptive physical computer for machine learning and robotic perception

TL;DR

This paper explores how an origami-inspired modular robotic manipulator can be repurposed into an adaptive physical reservoir computer, demonstrating its capacity for time series prediction, perception, and practical robotic control through systematic physical and input configuration analysis.

Contribution

It introduces a novel approach to physical computing using a modular origami robot, linking mechanical design to computational performance and demonstrating practical applications with shape memory alloy actuation.

Findings

Reservoir performance correlates with Peak Similarity Index (PSI).

The system accurately extracts payload weight and orientation.

Shape memory alloy actuation enables practical robotic control.

Abstract

Physical computing has emerged as a powerful tool for performing intelligent tasks directly in the mechanical domain of functional materials and robots, reducing our reliance on the more traditional COMS computers. However, no systematic study explains how mechanical design can influence physical computing performance. This study sheds insights into this question by repurposing an origami-inspired modular robotic manipulator into an adaptive physical reservoir and systematically evaluating its computing capacity with different physical configurations, input setups, and computing tasks. By challenging this adaptive reservoir computer to complete the classical NARMA benchmark tasks, this study shows that its time series emulation performance directly correlates to the Peak Similarity Index (PSI), which quantifies the frequency spectrum correlation between the target output and reservoir dynamics. The adaptive reservoir also demonstrates perception capabilities, accurately extracting its payload weight and orientation information from the intrinsic dynamics. Importantly, such information extraction capability can be measured by the spatial correlation between nodal dynamics within the reservoir body. Finally, by integrating shape memory alloy (SMA) actuation, this study demonstrates how to exploit such computing power embodied in the physical body for practical, robotic operations. This study provides a strategic framework for harvesting computing power from soft robots and functional materials, demonstrating how design parameters and input selection can be configured based on computing task requirements. Extending this framework to bio-inspired adaptive materials, prosthetics, and self-adaptive soft robotic systems could enable next-generation embodied intelligence, where the physical structure can compute and interact with their digital counterparts.