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1. Roadmap on embodying mechano-intelligence and computing in functional materials and structures

   https://doi.org/10.1088/1361-665X/adb7aa  

   Alù, Andrea; Arrieta, Andres F; Dottore, Emanuela Del; Dickey, Michael; Ferracin, Samuele; Harne, Ryan; Hauser, Helmut; He, Qiguang; Hopkins, Jonathan B; Hyatt, Lance P; et al (June 2025, Smart Materials and Structures)

   This is a roadmap article with multiple contributors on different aspects of embodying intelligence and computing in the mechanical domain of functional materials and structures. Overall, an IOP roadmap article is a broad, multi-author review with leaders in the field discussing the latest developments, commissioned by the editorial board. The intention here is to cover various topics of adaptive structural and material systems with mechano-intelligence in the overall roadmap, with twelve sections in total. These sections cover topics from materials to devices to systems, such as computational metamaterials, neuromorphic materials, mechanical and material logic, mechanical memory, soft matter computing, physical reservoir computing, wave-based computing, morphological computing, mechanical neural networks, plant-inspired intelligence, pneumatic logic circuits, intelligent robotics, and embodying mechano-intelligence for engineering functionalities via physical computing. In this paper, we view all the sections with equal contributions to the overall roadmap article and thus list the authorship on the front page via alphabetical order of their last names. On the other hand, for each individual section, the authors decide on their own the order of authorship. 

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2. A Dual‐Memory Ferroelectric Transistor Emulating Synaptic Metaplasticity for High‐Speed Reservoir Computing

   https://doi.org/10.1002/aelm.202500844  

   Wang, Yifan; Shahriar, Muhammad Sakib; Soliman, Salma; Vaillancourt, Noah; Fernandes, Lance; Padovani, Andrea; Khan, Asif Islam; Hasan, Md Sakib; Islam, Raisul (July 2026, Advanced Electronic Materials)

   ABSTRACT The exponential growth of edge artificial intelligence demands material‐focused solutions to overcome energy consumption and latency limitations when processing real‐time temporal data. Physical reservoir computing (PRC) offers an energy‐efficient paradigm but faces challenges due to limited device scalability and reconfigurability. Additionally, reservoir and readout layers require memory of different timescales, short‐term and long‐term respectively‐a material challenge hindering CMOS‐compatible implementations. This work demonstrates a CMOS‐compatible ferroelectric transistor using hafnium‐zirconium‐oxide (HZO) and silicon, enabling dual‐memory operation. This system exhibits non‐volatile long‐term memory (LTM) from ferroelectric HZO polarization and volatile short‐term memory (STM) from engineered non‐quasi‐static (NQS) channel‐charge relaxation driven by gate‐source/drain overlap capacitance. Ferroelectric polarization acts as non‐volatile programming of volatile dynamics: by modulating threshold voltage, the ferroelectric state deterministically switches the NQS time constant and computational behavior between paired‐pulse facilitation (PPF) and depression (PPD). This establishes a generalizable material‐design principle applicable to diverse ferroelectric‐semiconductor heterostructures, extending beyond silicon to oxide semiconductors and heterogeneously‐integrated systems. The device solves second‐order nonlinear tasks with 3.69 10 normalized error using only 16 reservoir states‐ reduction—achieving 20 response time ( faster) and 1.5 10 J energy consumption, providing an immediately manufacturable pathway for neuromorphic hardware and energy‐efficient edge intelligence. 

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3. Re‐Purposing a Modular Origami Manipulator Into an Adaptive Physical Computer for Machine Learning and Robotic Perception

   https://doi.org/10.1002/advs.202509389  

   Wang, Jun; Li, Suyi (December 2025, Advanced Science)

   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 CMOS 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 with 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 can enable next‐generation embodied intelligence, where the physical structure can compute and interact with its digital counterparts. 

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4. 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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5. Physical reservoir computing with origami and its application to robotic crawling

   https://doi.org/10.1038/s41598-021-92257-1  

   Bhovad, Priyanka; Li, Suyi (December 2021, Scientific Reports)

   null (Ed.)

   Abstract A new paradigm called physical reservoir computing has recently emerged, where the nonlinear dynamics of high-dimensional and fixed physical systems are harnessed as a computational resource to achieve complex tasks. Via extensive simulations based on a dynamic truss-frame model, this study shows that an origami structure can perform as a dynamic reservoir with sufficient computing power to emulate high-order nonlinear systems, generate stable limit cycles, and modulate outputs according to dynamic inputs. This study also uncovers the linkages between the origami reservoir’s physical designs and its computing power, offering a guideline to optimize the computing performance. Comprehensive parametric studies show that selecting optimal feedback crease distribution and fine-tuning the underlying origami folding designs are the most effective approach to improve computing performance. Furthermore, this study shows how origami’s physical reservoir computing power can apply to soft robotic control problems by a case study of earthworm-like peristaltic crawling without traditional controllers. These results can pave the way for origami-based robots with embodied mechanical intelligence . 

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