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1. 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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2. 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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3. Could Living Cells Use Phase Transitions to Process Information?

   https://doi.org/10.1103/j16b-xnms  

   Murugan, Arvind; Zwicker, David; Lorenz, Charlotta; Dufresne, Eric R (May 2026, Physical Review X)

   To maintain homeostasis, living cells process information with networks of interacting molecules. Traditional models for cellular information processing have focused on networks of chemical reactions between molecules. Here, we describe how networks of physical interactions could contribute to the processing of information inside cells. In particular, we focus on the impact of biomolecular condensation, a structural phase transition found in cells. Biomolecular condensation has recently been implicated in diverse cellular processes. Some of these are essentially computational, including classification and control tasks. We place these findings in the broader context of embodied computation, an emerging framework for describing how the native dynamics of nonlinear physical systems can be leveraged to perform complex computations. The synthesis of these ideas raises questions about expressivity (the range of problems that cellular phase transitions might be able to solve) and learning (how these systems could adapt and evolve to solve different problems). This emerging area of research presents diverse opportunities across molecular biophysics, soft matter, and embodied computation. 

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4. Real-Time Trajectory Generation for Soft Robot Manipulators Using Differential Flatness

   Dickson, A; Pacheco_Garcia, JC; Jing, R; Anderson, ML; Sabelhaus, AP (April 2025, IEEE International Conference on Soft Robotics)

   Soft robots have the potential to interact with sensitive environments and perform complex tasks effectively. However, motion plans and trajectories for soft manipulators are challenging to calculate due to their deformable nature and nonlinear dynamics. This article introduces a fast realtime trajectory generation approach for soft robot manipulators, which creates dynamically-feasible motions for arbitrary kinematically-feasible paths of the robot’s end effector. Our insight is that piecewise constant curvature (PCC) dynamics models of soft robots can be differentially flat, therefore control inputs can be calculated algebraically rather than through a nonlinear differential equation. We prove this flatness under certain conditions, with the curvatures of the robot as the flat outputs. Our two-step trajectory generation approach uses an inverse kinematics procedure to calculate a motion plan of robot curvatures per end-effector position, then, our flatness diffeomorphism generates corresponding control inputs that respect velocity. We validate our approach through simulations of our representative soft robot manipulator along three different trajectories, demonstrating a margin of 23x faster than realtime at a frequency of 100 Hz. This approach could allow fast verifiable replanning of soft robots’ motions in safety-critical physical environments, crucial for deployment in the real world. 

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5. Twisting for soft intelligent autonomous robot in unstructured environments

   https://doi.org/10.1073/pnas.2200265119  

   Zhao, Yao; Chi, Yinding; Hong, Yaoye; Li, Yanbin; Yang, Shu; Yin, Jie (May 2022, Proceedings of the National Academy of Sciences)

   Soft robots that can harvest energy from environmental resources for autonomous locomotion is highly desired; however, few are capable of adaptive navigation without human interventions. Here, we report twisting soft robots with embodied physical intelligence for adaptive, intelligent autonomous locomotion in various unstructured environments, without on-board or external controls and human interventions. The soft robots are constructed of twisted thermal-responsive liquid crystal elastomer ribbons with a straight centerline. They can harvest thermal energy from environments to roll on outdoor hard surfaces and challenging granular substrates without slip, including ascending loose sandy slopes, crossing sand ripples, escaping from burying sand, and crossing rocks with additional camouflaging features. The twisting body provides anchoring functionality by burrowing into loose sand. When encountering obstacles, they can either self-turn or self-snap for obstacle negotiation and avoidance. Theoretical models and finite element simulation reveal that such physical intelligence is achieved by spontaneously snapping-through its soft body upon active and adaptive soft body-obstacle interactions. Utilizing this strategy, they can intelligently escape from confined spaces and maze-like obstacle courses without any human intervention. This work presents a de novo design of embodied physical intelligence by harnessing the twisting geometry and snap-through instability for adaptive soft robot-environment interactions. 

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