Rebecca Kramer‐Bottiglio

Multiscale periodic metamaterials have been designed for numerous applications, such as impact absorption, acoustic cloaking, photonic band gaps, and mechanical logic gates. This prior work has focused on optimizing mesoscale structure for desired bulk isotropic properties. In contrast, we seek to develop materials with highly anisotropic elastic properties. To quantify elastic anisotropy, we introduce two rotationally invariant, normalized quantities that characterize the anisotropic response to shear and compression, respectively, $A_G$ and $A_C$. We find that typical crystalline solids possess average elastic anisotropy $\overline{A}_G \approx 0.15$ and $\overline{A}_C \approx 0.09$. Compared to atomic crystals, jammed granular materials can attain elastic anisotropies that are several orders of magnitude larger. Since grain rearrangements reduce anisotropy in granular materials, to preserve strong elastic anisotropy, we design tessellated granular materials that consist of multiple connected grain-filled voxels, which limit rearrangements and enable highly anisotropic elastic properties. Bulk granular packings with $N$ grains prepared at pressure $p$ have maximal anisotropy for $pN^2\sim1$ and become isotropic in the large-$pN^2$ limit. We show that homogeneously tessellated granular systems can inherit the elastic response of the constituent voxel configurations with elastic anisotropy up to $100$ times that of crystalline compounds over a range of $pN^2$. We show further methods to tune the elastic anisotropy of tessellations by designing heterogeneously patterned voxel configurations and tessellations that allow large boundary deformations.

Multiscale periodic metamaterials have been designed for numerous applications, such as impact absorption, acoustic cloaking, photonic band gaps, and mechanical logic gates. This prior work has focused on optimizing mesoscale structure for desired bulk isotropic properties. In contrast, we seek to develop materials with highly anisotropic elastic properties. To quantify elastic anisotropy, we introduce two rotationally invariant, normalized quantities that characterize the anisotropic response to shear and compression, respectively, $A_G$ and $A_C$. We find that typical crystalline solids possess average elastic anisotropy $\overline{A}_G \approx 0.15$ and $\overline{A}_C \approx 0.09$. Compared to atomic crystals, jammed granular materials can attain elastic anisotropies that are several orders of magnitude larger. Since grain rearrangements reduce anisotropy in granular materials, to preserve strong elastic anisotropy, we design tessellated granular materials that consist of multiple connected grain-filled voxels, which limit rearrangements and enable highly anisotropic elastic properties. Bulk granular packings with $N$ grains prepared at pressure $p$ have maximal anisotropy for $pN^2\sim1$ and become isotropic in the large-$pN^2$ limit. We show that homogeneously tessellated granular systems can inherit the elastic response of the constituent voxel configurations with elastic anisotropy up to $100$ times that of crystalline compounds over a range of $pN^2$. We show further methods to tune the elastic anisotropy of tessellations by designing heterogeneously patterned voxel configurations and tessellations that allow large boundary deformations.

ABSTRACT As stretchable electronics enable fully soft robots and more comfortable wearables that conform to the human body, researchers have engineered a plethora of stretchable conductors. Inconsistencies in experimental techniques plague many of these candidate materials, leading to contradictory claims between similar studies. For example, the room‐temperature liquid metal eutectic gallium‐indium (EGaIn) has been reported to have a variety of electromechanical responses, ranging from strain insensitivity to strain sensitivity even greater than that of an incompressible bulk conductor. In this work, we seek to provide a unified theory for the electromechanical response of EGaIn. Specifically, we provide analytical and experimental results supporting the hypothesis that liquid metal is a bulk conductor. The key insight is that parasitic resistance—arising from contact points and unstrained regions—masks true material behavior, leading to apparent discrepancies across studies and falsely suppressed strain responses. Linear normalization can remove these differences and collapse a wide range of previously published data into a common curve, which matches bulk‐conductor theory. Our experimental results, spanning various trace geometries, also match bulk conductor theory. We hope that this work resolves the debate on whether liquid metal is a bulk conductor while providing a framework to evaluate other stretchable conductors.

General-purpose simulators have accelerated the development of robots. Traditional simulators based on first-principles, however, typically require full-state observability or depend on parameter search for system identification. This work presents \texttt{CableRobotGraphSim}, a novel Graph Neural Network (GNN) model for cable-driven robots that aims to address shortcomings of prior simulation solutions. By representing cable-driven robots as graphs, with the rigid-bodies as nodes and the cables and contacts as edges, this model can quickly and accurately match the properties of other simulation models and real robots, while ingesting only partially observable inputs. Accompanying the GNN model is a sim-and-real co-training procedure that promotes generalization and robustness to noisy real data. This model is further integrated with a Model Predictive Path Integral (MPPI) controller for closed-loop navigation, which showcases the model's speed and accuracy.

General-purpose simulators have accelerated the development of robots. Traditional simulators based on first-principles, however, typically require full-state observability or depend on parameter search for system identification. This work presents \texttt{CableRobotGraphSim}, a novel Graph Neural Network (GNN) model for cable-driven robots that aims to address shortcomings of prior simulation solutions. By representing cable-driven robots as graphs, with the rigid-bodies as nodes and the cables and contacts as edges, this model can quickly and accurately match the properties of other simulation models and real robots, while ingesting only partially observable inputs. Accompanying the GNN model is a sim-and-real co-training procedure that promotes generalization and robustness to noisy real data. This model is further integrated with a Model Predictive Path Integral (MPPI) controller for closed-loop navigation, which showcases the model's speed and accuracy.

As Moore’s law approaches its terminus, the need for alternative computing paradigms becomes increasingly pressing. A promising alternative exploits mechanical interactions in materio to perform computation. One way to achieve this is with computational granular metamaterials (CGMMs), materials that have been optimized to harness mechanical signals such as force, shear, or wave propagation to process information. When materials are designed to perform several computations simultaneously, each at a unique vibrational frequency, the resulting polycomputational materials may eventually achieve functional densities superior to traditional computing substrates. However, the relationship between material structure and computational ability is not yet understood. To address this gap, we adopt lesioning methods from neuroscience to probe the structure-function relationship within CGMMs. By systematically disabling grains in optimized configurations, we identify critical components and reveal how specific grains participate in computation. We complement our in silico work with a hardware demonstration of a vibrational granular metamaterial, illustrating how future, more complex, and useful CGMMs, designed in silico, may be physically realized. These findings offer a new understanding of the computational dynamics of CGMMs, which may suggest ways to further increase their computational density in the future. This may eventually allow them to take their place among the next generation of computing systems in the post-Moore’s Law era.

Liquid crystal elastomers (LCEs) are promising candidates for artificial muscles due to their thermo‐responsive nematic‐to‐isotropic transition, which enables high strains at accessible temperatures and mimics the adaptable resting state of natural muscle. However, LCEs have lower force outputs than other soft actuators. This work reports an LCE formulation that achieves a 5× increase in actuation force through mechanical intervention. Incorporating low‐melting‐point alloy Field's metal (FM) particles into the LCE matrix both enhances actuation stress and introduces tunable stiffness. At low FM concentrations (≤10 vol%), actuation stress increases fivefold due to mechanically enhanced network entropy. At higher concentrations (≈30 vol%), the composite exhibits variable stiffness, behaving metal‐like when below the FM's melting point and softening once the FM melts. These formulations not only enhance actuator performance in terms of stress and strain but also mimic muscle‐like rheological behavior, advancing LCEs toward practical artificial muscle applications.

Achieving lifelike autonomy remains a long-term aspiration, yet soft robots so far have mostly demonstrated rudimentary physical intelligence that relies on manipulation of external stimuli to generate continuous motion. To realize autonomous physical intelligence (API) capable of self-regulated sensing, decision-making, and actuation, a promising approach is creating nonlinear time-lag feedback embedded within materials, where a constant stimulus elicits delayed responses to enable autonomous motion. This Review explores such feedback mechanisms, traces the evolution of physically intelligent robots, outlines strategies for embedding API in soft robots under diverse environments, and further discusses challenges and future directions beyond simple locomotion.

The biomimetic approach holds that the structure and performance of animals can be used as inspiration for the development and improvement of engineered technologies. In examining locomotion, sea lions and sea turtles demonstrate amphibious capabilities that can be emulated for the development of robotic systems that can move from the water onto the land. Both sea lions and sea turtles have elongated fore flippers for thrust production when swimming. While these species are different phylogenetically, they have converged on morphologies and mechanisms for efficient swimming and have the capability for quadrupedal movement onto land. Turning is accomplished using fore and hind flippers for both species. Sea lions display faster turning rates for translational with small turning radii compared to sea turtles, which are constrained by their rigid shell. However, sea turtles are capable of performing pure rotational turns with a zero radius. The flipper and body morphologies and swimming and turning performance along with terrestrial ability were integrated into two robotic systems based on the sea lion and sea turtle. These amphibious bio-robotic systems present a new and innovative approach in the development of autonomous underwater vehicles with advanced capabilities.

Phase-change granular actuators combine the high volumetric expansion and actuation stress of bulk phase-change systems with the tunable properties of granular materials. One form of these actuators utilizes microcapsule grains made from an elastic matrix encapsulating multiple solvent cores, where phase changes from liquid to gas drive volumetric expansion. Previous work demonstrated grain expansions up to 700%, though material selection was not optimized. This study explores how shell and core material choices affect the synthesis and performance of phase-change granular actuators. We identify specific combinations of solvents and silicone matrices that influence encapsulation efficiency, grain morphology, and processing requirements. Results show that increasing shell stiffness and core solvent boiling point raises actuation temperatures, while softer shells enable greater volumetric expansions. Overall, tuning the material composition allows for control of actuation metrics.

Soft robots can achieve exceptional adaptability through tunable morphological and mechanical properties. Incorporating materials with dynamically adjustable characteristics can enhance this versatility further. Granular meta-materials, consisting of discrete particles with individually variable properties, offer a promising approach to bulk property adaptation by adjusting the properties of constituent particles. This work introduces variable size and variable stiffness (VS<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) particles, in which both particle size and stiffness are independently modulated through concentric pneumatic chambers. We characterize the achievable workspace, mapping particle responses to independent chamber inflation. To demonstrate their use in a granular assembly, we arrange an array of VS<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> particles in a hexagonal packing and validate that behavior in packed configurations aligns with free-space characterizations. This study establishes a foundation for adaptive granular materials and provides a platform for further computational and experimental exploration of 2D and 3D granular metamaterials with tunable properties.

Recent advances in locomoting robotics have demonstrated how shape morphing can enhance efficiency, mobility, and speed during transitions between domains, such as from land to water. However, prior approaches often optimize a robot's propulsor shape, stiffness, and gait to minimize the cost of transport in distinct domains while neglecting the energy required to achieve these shape and stiffness changes at domain interfaces. Additionally, many shape‐morphing robots rely on thermally driven materials that couple shape and stiffness changes to environmental temperatures, limiting their applicability in real‐world multidomain scenarios. This work introduces the Jamming Amphibious Robotic Turtle (JART), which employs pressure‐responsive, topologically altered kirigami laminar jamming to transform its limbs between hydrodynamic flippers and load‐bearing legs. Energetic analyses reveal that JART achieves a 98.5% reduction in the energetic cost of morphing compared to thermally driven predecessors. Its pressure‐responsive morphing mechanism also enables temperature‐independent energy expenditures when morphing, rapid stiffness switching, decoupled control of stiffness and shape, and robust postloading shape recovery. System‐level evaluations highlight JART's ability to efficiently transition between domains, demonstrated through a continuous terrestrial–aquatic–terrestrial transition at an ocean inlet. This study provides valuable insights into the design of deployable, multidomain robots, advancing their potential for real‐world applications.

We describe size-varying cylindrical particles made from silicone elastomers that can serve as building blocks for robotic granular materials. The particle size variation, which is achieved by inflation, gives rise to changes in stiffness under compression. We design and fabricate inflatable particles that can become stiffer or softer during inflation, depending on key parameters of the particle geometry, such as the ratio of the fillet radius to the wall thickness, r/t. We also conduct numerical simulations of the inflatable particles and show that they only soften during inflation when localization of large strains occurs in the regime r/t -&gt; 0. This work introduces novel particle systems with tunable size and stiffness that can be implemented in numerous soft robotic applications.

Robotic burrowing holds promise for applications in agriculture, resource extraction, and infrastructure development, but current approaches are ineffective, inefficient, or cause significant environmental disruption. In contrast, natural burrowers penetrate substrates with minimal disturbance, providing biomechanical principles that could inspire more efficient and sustainable mechanisms. A notable feature of many natural burrowers is their reliance on soft body compositions, raising the question of whether softness contributes to their burrowing success. This review explores the role of soft materials in biological burrowing and their implications for robotic design. We examine the mechanisms that soft-bodied organisms and soft robots employ for submerging and subterranean locomotion, focusing on how softness enhances efficiency and adaptability in granular media. We analyze the gaps between the capabilities of natural burrowers and soft robotic burrowers, identify grand challenges, and propose opportunities to enhance robotic burrowing performance. By bridging biological principles with engineering innovation, this review aims to inform the development of next-generation burrowing robots capable of operating with the efficiency and efficacy seen in nature.

Future robots will navigate perilous, remote environments with resilience and autonomy. Researchers have proposed building robots with compliant bodies to enhance robustness, but this approach often sacrifices the autonomous capabilities expected of rigid robots. Inspired by tensegrity architecture, we introduce a tensegrity robot -- a hybrid robot made from rigid struts and elastic tendons -- that demonstrates the advantages of compliance and the autonomy necessary for task performance. This robot boasts impact resistance and autonomy in a field environment and additional advances in the state of the art, including surviving harsh impacts from drops (at least 5.7 m), accurately reconstructing its shape and orientation using on-board sensors, achieving high locomotion speeds (18 bar lengths per minute), and climbing the steepest incline of any tensegrity robot (28 degrees). We characterize the robot's locomotion on unstructured terrain, showcase its autonomous capabilities in navigation tasks, and demonstrate its robustness by rolling it off a cliff.

Robotic skins with embedded sensors and actuators are designed to wrap around soft, passive objects to control those objects from their surface. Prior state estimation and control models relied on specific actuator and sensor placement in robotic skins wrapped around soft cylinders, as well as used simplistic assumptions based on geometry and an ideal connection between the robotic skin and underlying structure. Such assumptions limit model fidelity and affect its utility in the design and control of surface-actuated systems. In this work, we relax prior assumptions and present a new quasi-static model with mechanics, controls, state estimation, and kinematic sub-models, or modules, for robotic skins placed around cylindrical structures. The kinematics module is used post-process to analyze the performance of the other three modules. We test the utility of the model on two robotic skin designs and compare the performance against a previous model and physical experiments. We demonstrate that the mechanics, controls, and state estimation modules presented herein outperform the previous model and the mechanics module can be used to predict the behavior of new robotic skin designs. The accuracy of the model increases as the stiffness of the host body material increases. This expanded theory could be utilized to reduce fabrication costs and speed up the design process and could be further extended to include system dynamics and model systems with multiple robotic skins.

Joule heating, which softens the particle. As the particle cools to room temperature, the alloy solidifies and the particle recovers its original modulus. To optimize the mechanical response of granular packings containing both soft and stiff particles, we employ an evolutionary algorithm coupled with discrete element method simulations to predict the patterns of particle moduli that will yield specific force outputs on the assembly boundaries. The predicted patterns of particle moduli from the simulations were realized in experiments using quasi-2D assemblies of VM particles and the force outputs on the assembly boundaries were measured using photoelastic techniques. These studies represent a step towards making robotic granular metamaterials that can dynamically adapt their mechanical properties in response to different environmental conditions or perform specific tasks on demand.

→ 0. This work introduces novel particle systems with tunable size and stiffness that can be implemented in particle packings for soft robotic applications.

If you ever did the egg drop challenge, you know it is hard to build something that can protect a fragile egg from crashing into the ground and breaking. Engineers are building soft robots called tensegrity robots, which are designed to survive harsh crashes. The word tensegrity comes from “tension” and “integrity”. It means the robot is made of stiff bars held together with stretchy cables. This flexible structure helps a tensegrity robot absorb the impact from crashes. Someday, these robots might be used to explore dangerous places like deep caves or other planets. These robots could fall off cliffs or into craters. Right now, engineers are making tensegrity robots better and easier to control. In this article, we will explain how tensegrity robots work. We will discuss their advantages, their disadvantages, and what they can be used for.

Externally driven dense packings of particles can exhibit nonlinear wave phenomena that are not described by effective medium theory or linearized approximate models. Such nontrivial wave responses can be exploited to design sound-focusing/scrambling devices, acoustic filters, and analog computational units. At high amplitude vibrations or low confinement pressures, the effect of nonlinear particle contacts becomes increasingly noticeable, and the interplay of nonlinearity, disorder, and discreteness in the system gives rise to remarkable properties, particularly useful in designing structures with exotic properties. In this paper, we build upon the data-driven methods in dynamical system analysis and show that the Koopman spectral theory can be applied to granular crystals, enabling their phase space analysis beyond the linearizable regime and without recourse to any approximations considered in the previous works. We show that a deep neural network can map the dynamics to a latent space where the essential nonlinearity of the granular system unfolds into a high-dimensional linear space. As a proof of concept, we use data from numerical simulations of a two-particle system and evaluate the accuracy of the trajectory predictions under various initial conditions. By incorporating data from experimental measurements, our proposed framework can directly capture the underlying dynamics without imposing any assumptions about the physics model. Spectral analysis of the trained surrogate system can help bridge the gap between the simulation results and the physical realization of granular crystals and facilitate the inverse design of materials with desired behaviors.

An ideal robot could autonomously complete diverse tasks such as ocean surveying, kitchen cleaning, and aerial environmental monitoring. However, robots optimized for each task typically have different shapes, posing a challenge in reconciling form and function. This challenge inspires the pursuit of general shape-changing robots (GSCRs). While soft materials and actuators are promising for GSCRs due to their ability to accommodate extreme deformations, there is a gap between the vision of GSCRs and the simple examples we see today. Two critical components are needed: robot-agnostic stretchable shape sensing and stretchable computing. Together, these components would enable closed-loop shape control and the first instantiations of GSCRs. This review aims to consolidate the literature on these components, encouraging researchers to bridge the gap between today's shape-changing robots and the envisioned GSCRs, ultimately advancing the field toward more versatile and adaptive robots.

Tensegrity robots are composed of rigid struts and flexible cables. They constitute an emerging class of hybrid rigid-soft robotic systems and are promising systems for a wide array of applications, ranging from locomotion to assembly. They are difficult to control and model accurately, however, due to their compliance and high number of degrees of freedom. To address this issue, prior work has introduced a differentiable physics engine designed for tensegrity robots based on first principles. In contrast, this work proposes the use of graph neural networks to model contact dynamics over a graph representation of tensegrity robots, which leverages their natural graph-like cable connectivity between end caps of rigid rods. This learned simulator can accurately model 3-bar and 6-bar tensegrity robot dynamics in simulation-to-simulation experiments where MuJoCo is used as the ground truth. It can also achieve higher accuracy than the previous differentiable engine for a real 3-bar tensegrity robot, for which the robot state is only partially observable. When compared against direct applications of recent mesh-based graph neural network simulators, the proposed approach is computationally more efficient, both for training and inference, while achieving higher accuracy. Code and data are available at https://github.com/nchen9191/tensegrity_gnn_simulator_public

Mechanical metamaterials are structured materials designed to exhibit unconventional mechanical responses. Most mechanical metamaterials have a single material response, but the ability to tune the intensity of a programmed response is useful to enhance the adaptability of the metamaterials to varying application requirements. This work presents variable-stiffness metamaterials (VSMMs) made from a thermally responsive, variable-stiffness Field's metal-silicone composite embedded in a soft silicone elastomer. We investigate VSMMs with different metamaterial structures and constituent material stiffness ratios, and we demonstrate that the VSMM properties are dominated by the embedded metamaterial in a stiff state and the host elastomer in a soft state. We showcase the utility of the VSMM by producing capacitive sensors that can switch between property states: high measurement sensitivity with limited deformations (<10%) or low sensitivity with large deformations (>60%).

To achieve real-world functionality, robots must have the ability to carry out decision-making computations. However, soft robots stretch and therefore need a solution other than rigid computers. Examples of embedding computing capacity into soft robots currently include appending rigid printed circuit boards to the robot, integrating soft logic gates, and exploiting material responses for material-embedded computation. Although promising, these approaches introduce limitations such as rigidity, tethers, or low logic gate density. The field of stretchable electronics has sought to solve these challenges, but a complete pipeline for direct integration of single-board computers, microcontrollers, and other complex circuitry into soft robots has remained elusive. We present a generalized method to translate any complex two-layer circuit into a soft, stretchable form. This enabled the creation of stretchable single-board microcontrollers (including Arduinos) and other commercial circuits (including SparkFun circuits), without design simplifications. As demonstrations of the method's utility, we embedded highly stretchable (>300% strain) Arduino Pro Minis into the bodies of multiple soft robots. This makes use of otherwise inert structural material, fulfilling the promise of the stretchable electronic field to integrate state-of-the-art computational power into robust, stretchable systems during active use.

Abstract A robot that uses fabrics as its core body material can be lightweight, compact, and highly flexible. Ideally, the robot's actuation, sensing, and structural support are provided by fiber‐based components, designed to integrate with the fabric's soft and conformable nature while preserving its fiber architecture. Typically, variable stiffness fibers are used for the structural elements, functioning as “bones” that can be turned on and off as needed. However, many variable stiffness fibers are passively‐rigid, only allowing the fabric to become soft when powered, while some require bulky external air or power supplies, making them untenable for untethered robotics. In this work, an electrically‐driven variable stiffness fiber is presented that performs a flat‐to‐curved geometry transition, providing a rigid load‐bearing structure when powered but remaining flexible otherwise. Design principles for pairing the actively‐rigid variable stiffness fiber with a materially compatible fiber‐based actuator are presented, and the actuator performance in different configurations is characterized. The variable stiffness fiber can be arranged into sturdy legs, stable enough for a robotic fabric to lift and hold its own battery pack and onboard electronics. This capability is demonstrated with a first‐of‐its‐kind fully‐untethered locomoting robotic fabric using two different quadruped gaits.

Biological organisms exhibit phenomenal adaptation through morphology-shifting mechanisms including self-amputation, regeneration, and collective behavior. For example, reptiles, crustaceans, and insects amputate their own appendages in response to threats. Temporary fusion between individuals enables collective behaviors, such as in ants that temporarily fuse to build bridges. The concept of morphological editing often involves the addition and subtraction of mass and can be linked to modular robotics, wherein synthetic body morphology may be revised by rearranging parts. This work describes a reversible cohesive interface made of thermoplastic elastomer that allows for strong attachment and easy detachment of distributed soft robot modules without direct human handling. The reversible joint boasts a modulus similar to materials commonly used in soft robotics, and can thus be distributed throughout soft robot bodies without introducing mechanical incongruities. To demonstrate utility, the reversible joint is implemented in two embodiments: a soft quadruped robot that self-amputates a limb when stuck, and a cluster of three soft-crawling robots that fuse to cross a land gap. This work points toward future robots capable of radical shape-shifting via changes in mass through autotomy and interfusion, as well as highlights the crucial role that interfacial stiffness change plays in autotomizable biological and artificial systems.

Continuous advances in soft robotic technolo-gies have promoted the feasibility of exploration of complex environments and terrains. One prominent example is the class of tip-everting “vine” robots, which have enabled a new set of real-world applications. Vine robots navigate their environment through growth and have recently been used in practice for in-pipe inspection, maintenance, and exploration. While locomotion through these directed cylindrical systems is simplified by a vine robot's growth, there are challenges with navigation. In complex pipe networks with many junctions, one question is how a vine can navigate around its own body during exploration. For example, a vine may navigate a pipe network that forces the robot to traverse a section of a pipe it already traversed in the opposite direction. This work presents an experimental approach to investigating and characterizing the interaction of a vine with its own body inside of a pipe T-junction. The results of this work provide initial design recommendations for facilitating the successful navigation of a vine robot in a T-junction.

Liquid crystal elastomers (LCEs) are a promising class of responsive materials for shape-morphable structures due to their large deformation, tunable properties, and low power consumption. It is well-known that by laminating a contracting LCE with an inert but flexible material layer, bending actuation can be achieved. We report a method to fabricate thermoresponsive LCE and fabric bilayer actuators and characterize their utility as bending actuators. We find that the use of different strain-limiting fabrics yields varying actuation outputs and curvatures, which we discover to be reliant on the interfacial adhesion of the bilayer, rather than the stiffness of the strain-limiting layer. We demonstrate the efficacy of the proposed LCE-fabric bending actuator in a soft robotic gripper. The findings herein can be applied to the design of LCE and fabric bilayer structure patterns to realize complex and programmable out-of-plane morphing.

Diverse space infrastructure is required for exploration missions to the Moon, Mars, and beyond. However, the cost of sending materials into space is high. One approach to ease this cost is the use of adaptive infrastructure, which may leverage discrete building blocks that can be assembled, disassembled, and reassembled into diverse mechanical structures based on the relevant environment and task demands. Indeed, the NASA Automated Reconfigurable Mission Adaptive System (ARMADAS) project is taking this approach. The discrete building component selected by ARMADAS engineers is a cuboctahedron, or more simply a “voxel,” as a volumetric pixel. The voxels are lightweight and simple, and assemble into programmable mechanical metamaterial structures with high stiffness and stability. However, transportation of complete voxels remains volume-inefficient, and fabrication of voxels in-situ adds notable complexity to the system. Herein, we introduce a cuboctahedron voxel design that compresses to 35% of its deployed volume during transport and passively locks in its expanded state at its destination, where a multitude of voxels can then be assembled. Inspired by the Hoberman sphere, the voxel is designed to deploy using a 1D force input. We further confirm that the new deployable voxel is compatible with existing ARMADAS assembly agents.

Modular and climbing robots have employed electropermanent magnets as a power-efficient alternative to electromagnets for tasks that involve attaching modules or exerting forces on ferromagnetic surfaces. In this paper, we present compliant electropermanent magnets that extend the benefits of electropermanent magnets to the field of soft robotics. We describe a process for designing compliant electropermanent magnets with different materials and mixing ratios to achieve desired properties without sacrificing the mechanical compliance necessary for integration into soft robots. Finally, we characterize the performance of the compliant electropermanent magnets and demonstrate their ability to repeatably and reversibly switch their magnetization ON and OFF.

Terrestrial and aquatic animals exhibit appendages adapted to the propulsion physics of their primary habitats. Terrestrial appendages typically assume stiff and load-bearing form factors, while aquatic appendages tend to adopt flexible and streamlined profiles. Bio-inspired robots with synthetic appendages often mirror this dichotomy of specialization: they are designed with fixed legs or flippers for locomotion on land or in water, respectively. Appendages that adjust their shape and stiffness can serve to specialize a robot's propulsion physics on demand, enabling transitions between multiple environments. Herein, we report a morphing limb combining layer jamming and pouch-based pneumatic actuation that rapidly and efficiently switches between a flexible flipper for swimming and a rigid leg for walking/crawling. The internal pouch actuator contributes to pressure that jams the external layers of the limb, which we refer to as positive pressure-reinforced jamming. We quantify the extent of shape-morphing conferred by the pouch actuator, the maximum load-bearing capability of the limb in leg mode, and the hydrodynamic characteristics of the limb in flipper mode. We find that the new limb boasts better performance than previous designs with respect to morphing shape, speed, efficiency, and hydrodynamics. Crucially, we also find that positive pressure-reinforced jamming increases the leg's compressive strength by 30% relative to just jamming the layers via negative pressure. With its own lightweight and compact electronics system, the morphing limb is a plug-and-play component for building an untethered multi-environment robot.

Shape morphing can be achieved using thin, pla-nar sheets patterned with strain-limiting constraints to direct differential growth into desired shapes. Our previous work produced such shape-shifting sheets using an inflatable sheet with variable stiffness fibers placed in patterns optimized by a multi-objective evolutionary algorithm. With our pipeline, we generated two specialized fiber patterns <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\{P_{i},\ P_{j}\}$</tex> to produce shapes <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\{S_{i},\ S_{j}\}$</tex>, which we then layered onto the same sheet. However, this layering approach had translation inefficiencies in hardware, such as fiber redundancies. To reduce fiber crowding and increase shape fidelity, we propose that fibers identified as similar in both <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{i}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{j}$</tex> can be implemented as a single fiber belonging to both patterns. Extending our previous work, herein we implement a post-optimization fiber merging protocol. Applying the protocol to sheets patterned with fibers targeting two shape-pairs, cylinder/sphere and simple saddle/monkey saddle, we demonstrate that fiber merging reduces the total number of fibers on each sheet, thus reducing sheet bulk and weight. We further measure the error between the target shapes and actual hardware shapes: For the cylinder/sphere shape-pair, the error increases with merging implemented when compared to the original, unmerged fiber patterns. For the monkey saddle/simple saddle shape-pair, the error decreases for one or both shapes for all fiber mergers implemented. The results indicate that fiber merging is increasingly useful with increasing fiber pattern complexity as measured by fiber count. Therefore, fiber merging is a potentially useful strategy to simplify the complexity of fiber designs required for shape-matching.

There is growing interest in engineering unconventional computing devices that leverage the intrinsic dynamics of physical substrates to perform fast and energy-efficient computations. Granular metamaterials are one such substrate that has emerged as a promising platform for building wave-based information processing devices with the potential to integrate sensing, actuation, and computation. Their high-dimensional and nonlinear dynamics result in nontrivial and sometimes counter-intuitive wave responses that can be shaped by the material properties, geometry, and configuration of individual grains. Such highly tunable rich dynamics can be utilized for mechanical computing in special-purpose applications. However, there are currently no general frameworks for the inverse design of large-scale granular materials. Here, we build upon the similarity between the spatiotemporal dynamics of wave propagation in material and the computational dynamics of Recurrent Neural Networks to develop a gradient-based optimization framework for harmonically driven granular crystals. We showcase how our framework can be utilized to design basic logic gates where mechanical vibrations carry the information at predetermined frequencies. We compare our design methodology with classic gradient-free methods and find that our approach discovers higher-performing configurations with less computational effort. Our findings show that a gradient-based optimization method can greatly expand the design space of metamaterials and provide the opportunity to systematically traverse the parameter space to find materials with the desired functionalities.

Abstract Multiphase mixtures containing both liquid metal and solid inclusions in a soft polymeric matrix can exhibit unique combinations of mechanical, electrical, magnetic, and thermal properties. Gallium‐based liquid metals have excellent electrical and thermal properties, and incorporating additional conductive, magnetic, or other solid fillers into liquid metal‐embedded elastomers can yield heightened electrical and thermal conductivities, enhanced elasticity due to lowered percolation thresholds, and positive piezoconductivity. This emerging class of liquid metal + x composites, where x denotes any solid filler type, has applications in stretchable electronics, wearables, soft robotics, and energy harvesting and storage. In this review, the recent literature is consolidated on liquid metal + x composites and their potential to offer uniquely amplified or multiplied bulk properties is highlighted. The literature related to the materials and processing of liquid metal + x composites is reviewed, through which it is found that the properties of the resulting multiphase composites are sensitive to the sequence in which the distinct liquid metal and solid inclusions are incorporated into the continuous phase. This review further includes a summary of relevant predictive modeling approaches, as well as identifies grand challenges and opportunities to advance liquid metal + x composites.

The original version of this Article contained an error in Fig. 3, in which the insets of Fig. 3a and 3b were not correctly visualized. The correct version of Fig. 3 is: