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Programmable shape-shifting materials can take different physical forms to achieve multifunctionality in a dynamic and controllable manner. Although morphing a shape from 2D to 3D via programmed inhomogeneous local deformations has been demonstrated in various ways, the inverse problem—finding how to program a sheet in order for it to take an arbitrary desired 3D shape—is much harder yet critical to realize specific functions. Here, we address this inverse problem in thin liquid crystal elastomer (LCE) sheets, where the shape is preprogrammed by precise and local control of the molecular orientation of the liquid crystal monomers. We show how blueprints for arbitrary surface geometries can be generated using approximate numerical methods and how local extrinsic curvatures can be generated to assist in properly converting these geometries into shapes. Backed by faithfully alignable and rapidly lockable LCE chemistry, we precisely embed our designs in LCE sheets using advanced top-down microfabrication techniques. We thus successfully produce flat sheets that, upon thermal activation, take an arbitrary desired shape, such as a face. The general design principles presented here for creating an arbitrary 3D shape will allow for exploration of unmet needs in flexible electronics, metamaterials, aerospace and medical devices, and more.

Magnetically actuated miniature soft robots are capable of programmable deformations for multimodal locomotion and manipulation functions, potentially enabling direct access to currently unreachable or difficult-to-access regions inside the human body for minimally invasive medical operations. However, magnetic miniature soft robots are so far mostly based on elastomers, where their limited deformability prevents them from navigating inside clustered and very constrained environments, such as squeezing through narrow crevices much smaller than the robot size. Moreover, their functionalities are currently restricted by their predesigned shapes, which is challenging to be reconfigured in situ in enclosed spaces. Here, we report a method to actuate and control ferrofluid droplets as shape-programmable magnetic miniature soft robots, which can navigate in two dimensions through narrow channels much smaller than their sizes thanks to their liquid properties. By controlling the external magnetic fields spatiotemporally, these droplet robots can also be reconfigured to exhibit multiple functionalities, including on-demand splitting and merging for delivering liquid cargos and morphing into different shapes for efficient and versatile manipulation of delicate objects. In addition, a single-droplet robot can be controlled to split into multiple subdroplets and complete cooperative tasks, such as working as a programmable fluidic-mixing device for addressable and sequential mixing of different liquids. Due to their extreme deformability, in situ reconfigurability and cooperative behavior, the proposed ferrofluid droplet robots could open up a wide range of unprecedented functionalities for lab/organ-on-a-chip, fluidics, bioengineering, and medical device applications.

Textiles are promising candidates for use in soft robots and wearable devices due to their inherent compliance, high versatility, and skin comfort. Planar fluidic textile‐based actuators exhibit low profile and high conformability, and can seamlessly integrate additional components (e.g., soft sensors or variable stiffness structures [VSSs]) to create advanced, multifunctional smart textile actuators. In this article, a new class of programmable, fluidic soft textile muscles (STMs) that incorporate multilayered silicone sheets with embedded fluidic channels is introduced. The STMs are scalable and fabricated by apparel engineering techniques, offering a fabrication approach able to create large‐scaled multilayered structures that can be challenging for current microfluidic bonding methods. They are also highly automation compatible due to no manual insertion of elastic tubes/bladders into textile structures. Liquid metal is employed for creating fluidic channels. It is not only used for actuation but also used as channels for additional features such as soft piezoresistive sensors with enhanced sensitivity to STMs’ pressure‐induced elongation, or VSSs of either low‐melting‐point alloys or a new thermo‐responsive epoxy with low viscosity and transition temperature. The STMs hold promising prospects for soft robotic and wearable applications, which is demonstrated by an example of a textile‐based wearable 3D skin‐stretch haptic interface. In this study, a new class of planar, soft textile muscles (STMs) inspired by natural sheet‐shaped actuators is introduced. The STMs can be fabricated by apparel techniques and a new method with high potential for automated processes and scalable sizes. The STMs can also incorporate additional components (e.g., soft sensors or variable stiffness structures) to create advanced, multifunctional smart textile structures.

Liquid crystal elastomers (LCEs) are shape-morphing materials whose large and reversible shape transformations are caused by the coupling between the mobile anisotropic properties of liquid crystal (LC) units and the rubber elastic of polymer networks. Their shape-changing behaviors under certain stimuli are largely directed by the LC orientation; therefore, various strategies have been developed to spatially modulate the LC alignments. However, most of these methods are limited as they require complex fabrication technologies or have intrinsic limitations in applicability. To address this issue, programmable complex shape changes in some LCE types, such as polysiloxane side-chain LCEs, thiol-acrylate main-chain LCEs, etc., were achieved by using a mechanical alignment programming process coupled with two-step crosslinking. Here, we report a polysiloxane main-chain LCE with programmable 2- and 3D shape-changing abilities that were created by mechanically programming the polydomain LCE with two crosslinking steps. The resulting LCEs exhibited a reversible thermal-induced shape transformation between the initial and programmed shapes due to the two-way memory between the first and second network structures. Our findings expand on the applications of LCE materials in actuators, soft robotics, and smart structures where arbitrary and easily programmed shape morphing is needed.

Liquid crystal elastomers (LCEs) are polymer networks that exhibit anisotropic liquid crystalline properties while maintaining the properties of elastomers, presenting reversible high-speed and large-scale actuation in response to external stimuli. Herein, we formulated a non-toxic, low-temperature liquid crystal (LC) ink for temperature-controlled direct ink writing 3D printing. The rheological properties of the LC ink were verified under different temperatures given the phase transition temperature of 63 °C measured by the DSC test. Afterwards, the effects of printing speed, printing temperature, and actuation temperature on the actuation strain of printed LCEs structures were investigated within adjustable ranges. In addition, it was demonstrated that the printing direction can modulate the LCEs to exhibit different actuation behaviors. Finally, by sequentially conforming structures and programming the printing parameters, it showed the deformation behavior of a variety of complex structures. By integrating with 4D printing and digital device architectures, this unique reversible deformation property will help LCEs presented here apply to mechanical actuators, smart surfaces, micro-robots, etc.

Shape‐morphing devices, a crucial branch in soft robotics, hold significant application value in areas like human–machine interfaces, biomimetic robotics, and tools for biological systems. To achieve 3D programmable shape morphing (PSM), the deployment of array‐based actuators is essential. However, a critical knowledge gap in 3D PSM is controlling the complex systems formed by these soft actuator arrays to mimic the morphology of the target shapes. This study, for the first time, represents the configuration of shape‐morphing devices using point cloud data and employing deep learning to map these configurations to control inputs. Shape Morphing Net (SMNet), a method that realizes the regression from point cloud to high‐dimensional control input vectors, is proposed. It has been applied to 3D PSM devices with three different actuator mechanisms, demonstrating its universal applicability to inversely reproduce the target shapes. Further, applied to previous 2D PSM devices, SMNet significantly enhances control precision from 82.23% to 97.68%. In the demonstrations of morphology mimicking, 3D PSM devices successfully replicate arbitrary target shapes obtained either through 3D scanning of physical objects or via 3D modeling software. The results show that within the deformable range of 3D PSM devices, accurate reproduction of the desired shapes is achievable. Soft robots capable of morphing into various 3D shapes are crucial for applications like human‐machine interfaces and biological manipulation. However, controlling 3D shape‐morphing robots with soft actuators remains a challenge. This work introduces a machine learning model that maps complex 3D deformations to control inputs, enabling robots to mimic real‐world object shapes, regardless of the actuator type.

Abstract We propose an algorithmic framework of a pluripotent structure evolving from a simple compact structure into diverse complex 3D structures for designing the shape-transformable, reconfigurable, and deployable structures and robots. Our algorithmic approach suggests a way of transforming a compact structure consisting of uniform building blocks into a large, desired 3D shape. Analogous to a fertilized egg cell that can grow into a preprogrammed shape according to coded information, compactly stacked panels named the zygote structure can evolve into arbitrary 3D structures by programming their connection path. Our stacking algorithm obtains this coded sequence by inversely stacking the voxelized surface of the desired structure into a tree. Applying the connection path obtained by the stacking algorithm, the compactly stacked panels named the zygote structure can be deployed into diverse large 3D structures. We conceptually demonstrated our pluripotent evolving structure by energy-releasing commercial spring hinges and thermally actuated shape memory alloy hinges, respectively. We also show that the proposed concept enables the fabrication of large structures in a significantly smaller workspace.

In most macroscale robotic systems, propulsion and controls are enabled through a physical tether or complex onboard electronics and batteries. A tether simplifies the design process but limits the range of motion of the robot, while onboard controls and power supplies are heavy and complicate the design process. Here, we present a simple design principle for an untethered, soft swimming robot with preprogrammed, directional propulsion without a battery or onboard electronics. Locomotion is achieved by using actuators that harness the large displacements of bistable elements triggered by surrounding temperature changes. Powered by shape memory polymer (SMP) muscles, the bistable elements in turn actuate the robot’s fins. Our robots are fabricated using a commercially available 3D printer in a single print. As a proof of concept, we show the ability to program a vessel, which can autonomously deliver a cargo and navigate back to the deployment point.

Shape memory photonic crystals (SMPCs) are smart composite materials with changeable structural color integrated by shape memory polymer and photonic crystals. SMPC can produce one or more temporary shapes through nanoscale deformation, memorizing current states. SMPC can be recovered to their original shapes or some intermediate states under external stimuli, accompanied by the variation of structural color. As porous carriers with built-in sensing properties, SMPCs promoted the interdisciplinary development of nanophotonic technology in materials science, environmental engineering, biomedicine, chemical engineering, and mechanics. Herein, the recent progress on multifunctional SMPCs and practical applications, including traditional and cold programmable SMPCs, is summarized and discussed. The primary concern is shape programming at the nanoscale that has demonstrated numerous attractive functions, including smart sensing, ink-free printing, solvent detection, reprogrammable gradient wetting, and controllable bubble transportation, under variations of the surface nanostructure. It aims to figure out the nanoscale shape memory effects on structural color conversion and additional performance, inspiring the fabrication of the next generation of SMPCs. Finally, perspectives on future research directions and applications are also presented. It is believed that multifunctional SMPCs are powerful nanophotonic tools for the interdisciplinary development of numerous disciplines in the future.