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1. Physical reservoir computing with origami: a feasibility study

   Bhovad, Priyanka; Li, Suyi (March 2021, Proc. SPIE 11589, Behavior and Mechanics of Multifunctional Materials XV)

   null (Ed.)

   In the field of soft robotics, harnessing the nonlinear dynamics of soft and compliant bodies as a computational resource to enable embodied intelligence and control is known as morphological computation. Physical reservoir computing (PRC) is a true instance of morphological computation wherein; a physical nonlinear dynamic system is used as a fixed reservoir to perform complex computational tasks. These dynamic reservoirs can be used to approximate nonlinear dynamical systems and even perform machine learning tasks. By numerical simulation, this study illustrates that an origami meta-material can also be used as a dynamic reservoir for pattern generation, output modulation, and input sensing. These results could pave the way for intelligently designed origami-based robots that interact with the environment through a distributed network of sensors and actuators. This embodied intelligence will enable the next generations of soft robots to autonomously coordinate and modulate their activities, such as locomotion gait generation and limb manipulation while resisting external disturbances. 

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2. Skyrmion based energy-efficient straintronic physical reservoir computing

   https://doi.org/10.1088/2634-4386/aca178  

   Rajib, Md Mahadi; Misba, Walid Al; Chowdhury, Md_Fahim F; Alam, Muhammad Sabbir; Atulasimha, Jayasimha (November 2022, Neuromorphic Computing and Engineering)

   Physical Reservoir Computing (PRC) is an unconventional computing paradigm that exploits the nonlinear dynamics of reservoir blocks to perform temporal data classification and prediction tasks. Here, we show with simulations that patterned thin films hosting skyrmion can implement energy-efficient straintronic reservoir computing (RC) in the presence of room-temperature thermal perturbation. This RC block is based on strain-induced nonlinear breathing dynamics of skyrmions, which are coupled to each other through dipole and spin-wave interaction. The nonlinear and coupled magnetization dynamics were exploited to perform temporal data classification and prediction. Two performance metrics, namely Short-Term Memory (STM) and Parity Check (PC) capacity are studied and shown to be promising (4.39 and 4.62 respectively), in addition to showing it can classify sine and square waves with 100% accuracy. These demonstrate the potential of such skyrmion based PRC. Furthermore, our study shows that nonlinear magnetization dynamics and interaction through spin-wave and dipole coupling have a strong influence on STM and PC capacity, thus explaining the role of physical interaction in a dynamical system on its ability to perform RC. 

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3. Building Intelligence in the Mechanical Domain—Harvesting the Reservoir Computing Power in Origami to Achieve Information Perception Tasks

   https://doi.org/10.1002/aisy.202300086  

   Wang, Jun; Li, Suyi (July 2023, Advanced Intelligent Systems)

   Herein, the cognitive capability of a simple, paper‐based Miura‐ori—using the physical reservoir computing framework—is experimentally examined to achieve different information perception tasks. The body dynamics of Miura‐ori (aka its vertices displacements), which is excited by a simple harmonic base excitation, can be exploited as the reservoir computing resource. By recording these dynamics with a high‐resolution camera and image processing program and then using linear regression for training, it is shown that the origami reservoir has sufficient computing capacity to estimate the weight and position of a payload. It can also recognize the input frequency and magnitude patterns. Furthermore, multitasking is achievable by simultaneously applying two targeted functions to the same reservoir state matrix. Therefore, it is demonstrated that Miura‐ori can assess the dynamic interactions between its body and ambient environment to extract meaningful information—an intelligent behavior in the mechanical domain. Given that Miura‐ori has been widely used to construct deployable structures, lightweight materials, and compliant robots, enabling such information perception tasks can add a new dimension to the functionality of such a versatile structure. 

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4. Nanoelectronics-enabled reservoir computing hardware for real-time robotic controls

   https://doi.org/10.1126/sciadv.adu2663  

   Chen, Mingze; An, Xiaoqiu; Ki, Seung Jun; Liu, Xirong; Sekhon, Nihal; Boyarov, Artyom; Acharya, Anushka; Tawil, Justin; Bederman, Maxwell; Liang, Xiaogan (March 2025, Science Advances)

   Traditional robotic vehicle control algorithms, implemented on digital devices with firmware, result in high power consumption and system complexity. Advanced control systems based on different device physics are essential for the advancement of sophisticated robotic vehicles and miniature mobile robots. Here, we present a nanoelectronics-enabled analog control system mimicking conventional controllers’ dynamic responses for real-time robotic controls, substantially reducing training cost, power consumption, and footprint. This system uses a reservoir computing network with interconnected memristive channels made from layered semiconductors. The network’s nonlinear switching and short-term memory characteristics effectively map input sensory signals to high-dimensional data spaces, enabling the generation of motor control signals with a simply trained readout layer. This approach minimizes software and analog-to-digital conversions, enhancing energy and resource efficiency. We demonstrate this system with two control tasks: rover target tracking and drone lever balancing, achieving similar performance to traditional controllers with ~10-microwatt power consumption. This work paves the way for ultralow-power edge computing in miniature robotic systems. 

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5. Noise-enhanced learning in Duffing adaptive oscillator

   https://doi.org/10.1098/rspa.2025.0002  

   Perkins, Edmon (June 2025, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences)

   Noise typically degrades the performance of physical systems. In some cases, however, noise can provide an enhanced effect. For instance, stochastic resonance may be observed in a nonlinear oscillator when a weak signal is boosted by noise. On the other hand, adaptive oscillators are a type of nonlinear oscillator that can learn and store information in dynamic states. Here, noise is shown to increase the learning rate of an adaptive oscillator, using a Duffing adaptive oscillator. To highlight this effect, stochastic simulations are employed, and the Fokker–Planck equation is semi-analytically solved using the cumulant neglect method. As adaptive oscillators can also be used as a powerful physical reservoir computer architecture, this work shows that the Duffing adaptive oscillator can be optimized for fast learning in noisy environments. 

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