Field‐Programable Dynamics in a Soft Magnetic Actuator Enabling True Random Number Generation and Reservoir Computing

Complex and even chaotic dynamics, though prevalent in many natural and engineered systems, have been largely avoided in the design of electromechanical systems due to concerns about wear and controlability. Here, we demonstrate that complex dynamics might be particularly advantageous in soft robotics, offering new functionalities beyond motion not easily achievable with traditional actuation methods. We designed and realized resilient magnetic soft actuators capable of operating in a tunable dynamic regime for tens of thousands of cycles without fatigue. We experimentally demonstrated the application of these actuators for true random number generation and stochastic computing. We validate soft robots as physical reservoirs capable of performing Mackey–Glass time series prediction. These findings show that exploring the complex dynamics in soft robotics would extend the application scenarios in soft computing, human–robot interaction and collaborative robots, as we demonstrate with biomimetic blinking and randomized voice modulation.

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Publication Details

Journal
Advanced Intelligent Systems
Published
2026-05-21
DOI
https://doi.org/10.1002/aisy.70432
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Field‐Programable Dynamics in a Soft Magnetic Actuator Enabling True Random Number Generation and Reservoir Computing

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Advanced Intelligent Systems
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Field‐Programable Dynamics in a Soft Magnetic Actuator Enabling True Random Number Generation and Reservoir Computing

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article en

Abstract

Complex and even chaotic dynamics, though prevalent in many natural and engineered systems, have been largely avoided in the design of electromechanical systems due to concerns about wear and controlability. Here, we demonstrate that complex dynamics might be particularly advantageous in soft robotics, offering new functionalities beyond motion not easily achievable with traditional actuation methods. We designed and realized resilient magnetic soft actuators capable of operating in a tunable dynamic regime for tens of thousands of cycles without fatigue. We experimentally demonstrated the application of these actuators for true random number generation and stochastic computing. We validate soft robots as physical reservoirs capable of performing Mackey–Glass time series prediction. These findings show that exploring the complex dynamics in soft robotics would extend the application scenarios in soft computing, human–robot interaction and collaborative robots, as we demonstrate with biomimetic blinking and randomized voice modulation.

Advanced Intelligent Systems
University of Messina (IT), University of Patras (GR), Helmholtz-Zentrum Dresden-Rossendorf (DE), Istituto Nazionale di Geofisica e Vulcanologia (IT), Kyiv Academic University (UA), Helmholtz-Zentrum Hereon (DE), Technische Universität Dresden (DE)
European Commission, Deutsche Forschungsgemeinschaft, Ministero dell’Istruzione, dell’Università e della Ricerca
Openalex Percentile: Top 5%
Neural Networks and Reservoir Computing
DOIOpen Access PDFOpenAlex
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