The shape-morphing capability and interaction forces of soft grippers are pivotal in determining the grasping performance, particularly in tasks that require gentle and safe handling, such as the manipulation of fragile or delicate objects. However, most soft gripper designs primarily focus on enhancing the deformation range and load capacity at the fingertip, often neglecting precise regulation of deformation and interaction forces during contact with objects. In this article, a novel topology-optimization framework for soft gripper design is proposed, aiming to achieve large-area contact with specified objects and uniformly distributed interaction forces. The grasped object is modeled by a set of springs, and the optimization objective concurrently regulates the gripper's shape morphing and the contact forces in terms of their distribution and resultant force. The gradient-based optimization algorithm generates a soft adaptive gripper design with interpretable structural features. Quantitative experiments demonstrate that the optimized gripper satisfies the specified requirements, exhibiting uniform wrapping over a large area and sufficient grasping force biased toward the palm side as desired to counteract gravity. Grasping tests with various objects, ranging from small cherry tomatoes to a 1.55 L bottle of water, further highlight the gripper's superior compliance, adaptability, and load capacity.

Suckers are significant for robots in picking, transferring, manipulation and locomotion on diverse surfaces. However, conventional suckers lack high-fidelity tactile perception, which impedes them from resolving the fine-grained geometric features and interaction status of the target surface. This limits their robust performance with irregular objects and in complex, unstructured environments. Inspired by the adaptive structure and high-performance sensory capabilities of cephalopod suckers, we propose a novel, intelligent sucker, named SuckTac, that integrates a camera-based tactile sensor directly within its optimized structure to provide high-density perception and robust suction. Specifically, through joint structural optimization and a multi-material integrated casting technique, a camera and light source are embedded into the sucker, which enables in-situ, high-density perception of fine details such as surface shape, texture, and roughness. To further enhance robustness and adaptability, the sucker's mechanical design is also optimized by refining its profile, adding a compliant lip, and incorporating surface microstructure. Extensive experiments, including challenging tasks such as robotic cloth manipulation and soft mobile robot inspection, demonstrate the superior performance and broad applicability of the proposed system.

Inspired by organisms that utilize multimodal locomotion strategies to adapt to diverse environments, the development of analogous capabilities in soft robots has garnered growing attention. This review comprehensively surveys recent advances in multimodal locomotion within soft robotics. Typical locomotion modes are summarized and categorized. Furthermore, the underlying mechanisms enabling multimodal locomotion, encompassing both the integration of distinct locomotion modes and transitions between them, are discussed in detail and classified into three primary categories: active control‐based, reconfiguration‐based, and environment‐responsive strategies. Leveraging these mechanisms, soft robots demonstrate enhanced adaptability for applications such as cross‐domain transition, surface adaptation, and obstacle negotiation. Finally, key challenges in advancing the capabilities of multimodal locomotion to address real‐world applications are discussed.

Highly maneuverable robots can cross obstacles or move quickly with running, jumping, ejection, or multimodal combinations, exhibiting advantages in aerospace, agriculture, medical treatment, etc. Due to the behaviors of high-energy bursts and the huge vibrations during motions, these robots need to be simple and reliable. 4D printing of highly maneuverable robots addresses this challenge by combining the power amplification of stimulus-response materials and additive manufacturing technologies. It integrates actuation, sensing, and control modules into one component with a highly integrated structure and controllable high-mobility movement. Herein, this paper presents a review of highly maneuverable robots based on 4D printing, focusing on the key technologies from design to fabrication. Firstly, the manufacturing processes of 4D printing for highly maneuverable robots are overviewed. Then, from a biomimetic perspective, structural designs for power amplification are reviewed. After summarizing stimulus-response materials applicable to the 4D-printed highly maneuverable robots, the strategies employed to control the direction, speed, mode, and response time of robotic motion are elaborated. Finally, current challenges and the perspectives on 4D printing of highly maneuverable robots are highlighted.

The robot workspace, defining the complete set of reachable end-effector positions and orientations, is critical for functional capability. However, quantitative workspace design remains an open challenge in soft robotics. This is due to complicated implicit kinematics governed by nonlinear continuum mechanics, and prohibitive computational cost of evaluating deformations across the full actuation spectrum. This article presents a computational morphogenesis framework for automatic optimization of position and orientation workspaces in multi-chamber soft pneumatic actuators. Our approach is built on three key innovations: (i) a continuum Jacobian, defined as the derivative of the end-effector's degrees of freedom (DoFs) with respect to the actuation inputs, which transforms the workspace integral from the configuration space to the actuation space, making the volume computation analytically tractable, (ii) a second-order adjoint method to derive the analytical shape derivatives of both the displacement field and this Jacobian, providing the explicit gradient of the workspace volume with respect to the robot's morphology, and (iii) a differentiable, singularity-free geometric model, complemented by an adaptive surface reconstruction algorithm, to represent and evolve the robot's free-form shape. The framework is validated by designing multi-chamber pneumatic soft actuators, achieving an 8-fold increase in 3D positional workspace volume and a 1.6-fold increase in 2D orientational workspace volume compared to the baseline Pneu-Nets actuators.

Contact frequently occurs in soft robots and significantly influences their deformation characteristics. However, the inherent nondifferentiability and the design-dependent nature of contact behaviors pose major challenges for gradient-based design optimization. Here, we propose an optimization-oriented static, frictionless contact model that acts as a smooth surrogate to approximate emergent contact behaviors across diverse design candidates during optimization, and more importantly remains continuously differentiable across contact state transitions with respect to both deformation states and geometric design parameters. This capability is achieved via a smooth penetration-penalty function and an automated contact region detection scheme. Building on this contact model, we develop a contact-aware morphological optimization framework for pneumatic soft robots, wherein gradients of contact-related objectives are efficiently evaluated using the adjoint method combined with automatic differentiation. Our results validate the model’s accuracy through benchmarking against Abaqus, demonstrate the necessity of contact-aware optimization for bidirectional bending actuators prone to self-contact, and showcase the framework’s capability to proactively exploit contact interactions for functional gains, as evidenced by a vacuum-driven actuator utilizing self-contact for enhanced torsional stiffness and a soft gripper featuring an expanded contact area with the target object. This work paves the way for programming contact-induced mechanical intelligence in soft robots.

Although various soft robots with remarkable mobility have been developed, enabling a single robot to achieve multi-terrain locomotion with transitions across ground, vertical walls, and ceilings remains a formidable challenge. This letter presents a multimodal dual-segment soft robot capable of omnidirectional terrestrial locomotion, vertical climbing and ceiling crawling, and smooth inter-surface transitioning. The robot's wall and ceiling locomotion ability is realized through improved segment design featuring lightweight construction, enhanced deformability and load capacity, whereas its transitional capability is facilitated by coordinated dual-segmental gaits and compensation of gravity-induced deformation. Through cross-sectional improvement, the redesigned segment demonstrates enhanced bending capability with a maximum bending angle exceeding 180 degrees, achieving a 105% workspace expansion compared to the baseline design. Experimental characterization reveals performance metrics of the robot: a maximum terrestrial velocity of 33.2 mm/s, angular turning rate of 30(degrees)/s, and transition capability while carrying 50 g payload. Furthermore, we demonstrate the robot's practical utility by integrating an onboard camera to successfully execute multi-surface inspection tasks in confined space and aircraft wing cavity, validating its potential for deployment in real-world unstructured environments.

Model-guided design of dielectric elastomer actuators (DEAs) is essential for enabling their application in soft robotics. However, current modeling methods primarily rely on the finite element method (FEM), which suffers from low computational efficiency. Additionally, the simulation-to-reality (Sim2Real) gap, mainly arising from variations in material properties and manufacturing processes, poses a significant challenge. In this work, we propose a data-driven modeling framework aimed at accurately and rapidly predicting voltage-induced displacements while minimizing the Sim2Real gap. The framework integrates a multi-layer perceptron (MLP) model, which serves as a computationally efficient surrogate for the FEM model, and a cycle-generative adversarial network (CycleGAN) model, which mitigates the Sim2Real gap by leveraging adversarial learning to process both simulation and experimental data. Dimensional analysis is performed to extend the framework’s applicability across different DEA scales. The surrogate model delivers global predictions in just 0.8 s, achieving linear coefficients of determination (R2) of 0.99106 for release distance prediction and 0.99375 for actuation distance prediction compared to experimental results. Our model can quickly identify the feasible range of biaxial prestretch ratios required for generating the desired deformation, thereby streamlining the design process. Finally, a soft robotic gripper is designed and fabricated, demonstrating versatile object-grasping capabilities.

The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we construct a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics.

Soft creatures like Drosophila larvae can quickly ascend tubular surfaces via rolling, a capability not yet replicated by soft robots. Here, we present a single-piece soft robot capable of rolling along tubular structures by sequentially actuating its built-in axial muscles. We reveal that the sequential actuation generates distributed spinning torques along the robot's curved axis, enabling continuous non-coaxial rolling-distinct from current gravity-dependent rolling solutions. This non-coaxial rolling mechanism allows the robot to swiftly navigate tubular surfaces while conforming to their shapes and maintaining a stable grip. The robot's deformation and gripping force are actively adjusted to enhance its adaptability to various surfaces. We demonstrate that our robot can ascend pipes with varying geometries (e.g., varying-diameter, spiral-shaped, or non-cylindrical), traverse diverse terrains, pass through confined tunnels, and transition smoothly between planar rolling and pipe climbing. The robot's great adaptability and rapid movement underscore its potential for navigating scenarios with intricate surface geometries.

Soft pneumatic robots are widely developed for grasping and locomotion tasks, where the routing of embedded muscles plays a pivotal role in programming the deformation behavior. However, existing routing patterns are often limited to predefined regular configurations due to the absence of accurate and efficient design-oriented models. In this article, we propose a differentiable kinematic model based on the absolute nodal coordinate formulation to predict the global configuration and local deformation of soft robots with customizable fiber-reinforced pneumatic muscles. By parameterizing the freeform muscle routing using B-splines and accurately modeling the work done by distributed pneumatic forces, we ensure that the routing pathways are fully differentiable. We then integrate the kinematic model into an optimization framework to automatically design muscle routing pathways for achieving desired deformation behaviors. The inclusion of analytical shape derivative enables efficient exploration of the high-dimensional design space. We verify the proposed model through comparisons with finite element analysis and experiments on a three-channel robot. The average position error of the end effector is less than 5% of the workspace's characteristic length, with an average computational time of 0.11 s per point. In addition, we demonstrate robots capable of achieving desired complex out-of-plane configurations and multiple target shapes.

Nature exhibits remarkable adaptability to complex environments through the coevolution of structural strategies, such as bistable mechanisms and helical geometries. The integration of these two principles has inspired the development of biomimetic helical bistable structures. However, design of bistable helical structures is hindered by the lack of theoretical models, due to the challenges introduced by misalignment between geometric and curvature coordinates, and intrinsic nonlinearity of soft materials. In this work, we develop a nonlinear framework for soft helical bistable structures based on minimum potential energy method. This model enables the prediction of critical transition points between bistable and monostable states, as well as the resulting deformed shapes. The theoretical predictions are validated through experiments. The effects of various geometric parameters are explored using the validated model. This work provides insights into the helical bistability of soft structures.

Dielectric elastomer actuators (DEAs) enable to create soft robots with fast response speed and high-energy density, but the fast optimization design of DEAs still remains elusive because of their continuous electromechanical deformation and high-dimensional design space. Existing approaches usually involve repeating and vast finite element calculation during the optimization process, leading to low efficiency and time consuming. The advance of deep learning has shown the potential to accelerate the optimization process, but the high-dimensional design space leads to challenge on the accuracy and generality of the deep learning model. In this work, we propose a deep learning-based automatic design framework for DEAs, capable of rapidly generating high-dimensional distributed electrode patterns based on different design objects. This framework is developed as follows: (1) a dataset construction strategy combining with a finite element model is developed to optimize the data distribution within the high-dimensional design space; (2) a neural network-embedded physical information is designed and trained to achieve accurate prediction of the continuous deformation within 0.011s; and (3) a genetic algorithm with the neural network is proposed to automatically and rapidly optimize the electrode pattern of DEAs based on various design objects. To verify the effectiveness, a series of case studies (including maximum displacement, specific displacement, multiplicity of solutions, multiple degree-of-freedom actuations, and complex actuations) has been conducted. Both simulation results and experimental data demonstrate that our design framework can automatically design the electrode pattern within 2 min and obviously improve the performance of DEAs. This work proposes a deep learning-based design approach with automatic and rapid property, thereby paving the way for broader applications of DEAs.

In this letter, we aim to develop an efficient and accurate kinematic model of a soft wrist that consists of pneumatic bellows configured in parallel, toward dexterous manipulation in confined space. The challenge arises from the distributed nature and deformation-dependency of the generated pneumatic actuation forces along the air chamber, making it difficult to obtain the equivalent generalized forces. To achieve a trade-off between accuracy and computational efficiency, we first establish a simplified geometric model of local deformation of the bellow by using the global state variables, based on simulations and experimental observations of bellow-type actuators. Then we define the deformation gradient of the chamber's wall. Subsequently, we develop forward and inverse kinematics by utilizing the principle of minimum potential energy. The analyses of the wrist's workspace and payload limit are conducted, and trajectory tracking experiments are performed to validate the proposed kinematics. The average error for the circle and tetrahedron trajectories with no load reaches about 2.40% and 5.25% of the characteristic length of the trajectory, respectively, with an average computation time of 0.17 s and 0.16 s per point, and the model maintains a certain level of accuracy under a permissible range of loads. Finally, we mount a suction cup at the end of the soft wrist and successfully perform the pick-and-place task in confined space.

Pinch is an indispensable grasping primitive of human hands and traditional rigid grippers, eminently suitable for handling small-sized and dense objects, but it is rather under-researched in the context of soft robotics. In this article, with the aim of combining the inherent advantages of soft materials and the pinch grasp primitive to enable delicate object manipulation in confined spaces without causing damage, we present a compliant, compact, and powerful gripper capable of pinching small objects in two deformation modes: abduction and adduction. The design is enabled by a density-based multimaterial topology optimization approach that automatically seeks the optimal tradeoff between the expected deformation and gripping force. The optimized design mainly contains two materials, and is fabricated with a customized voxel 3-D printing strategy by controlling the local mixing ratio of the soft and hard inks. The simulation and experiments show that the obtained multimaterial design remarkably outperforms the single material design in terms of deformation and payload. We demonstrate that an array of the designed grippers can work collectively to grasp dense objects in a single process. Further, the gripper can work as an end effector of a hyper-redundant robot arm that navigates a narrow space, and fetch small objects therein with compliance and safety.

Soft robots inherit several merits associated with compliant mechanisms, including frictionless, monolithic fabrication, and backlash-free operation. Moreover, the incorporation of soft actuation bestows upon soft robots distinctive advantages in terms of achieving active mobility. However, current soft robot design usually focuses on the motion range in the pursued direction only, lacking of deliberate consideration of stiffness characteristics in other directions to withstand unexpected disturbances, which has greatly limited the use of soft robots in practical scenarios. In this article, we propose novel soft robotic joints with anisotropic stiffness characteristics that produce the desired active target motion upon actuation and, in the meantime, constrain the undesired deformation under disturbances. Such functionality and robustness are embodied in the delicate design of the joints' compliant structure, with a multiobjective topology optimization approach. We develop three basic soft joints, i.e., elongation, twisting, and omnidirectional bending, and the design process is automated by a unified computation framework. The optimized soft joint prototypes are quantitatively characterized in terms of the free motion, blocking force, and multidirectional stiffness. The experiment results show that the joints not only generate practicable active motions but equally importantly, attain greatly improved stiffness to withstand unexpected disturbances. We further showcase a dexterous wrist-hand system by assembling the soft joints and a soft gripper to navigate and manipulate objects in confined space, and develop cable-driven soft fingers with enhanced lateral and twisting stiffness for stable grasping.

A homogeneous pneumatic soft robot may generate complex output motions using a simple input pressure, resulting from its morphological shape that locally deforms the soft material to different degrees by simultaneously tailoring the structural characteristics and orienting the input pressure. To date, design of the morphological shape (inverse problem) has not been fully addressed. This article outlines a geometry–mechanics–optimization integrated approach to automatically shaping a pneumatic soft actuator or robot that achieves the desired deformation behavior. Instead of constraining the robot's geometry within any predefined regular shape, we employ B-splines to allow generation of freeform boundary surfaces, and use nonlinear mechanical modelling and shape derivative based optimization to navigate the high-dimensional design space. Our design framework can readily regulate the surface quality during the morphological evolution, by imposing the geometric constraints in terms of the principal curvatures and the minimal distance between surfaces as penalty functions. The effect of external forces including the gravity and the interaction force at the end-effector is also taken into account to generalize the method for design problems in which the load capability is also pursued. To improve the computational efficiency, suboptimization problems are constructed within a trust region in which the displacement-dependent objective function is approximated by its first-order Taylor polynomial based on the gradient information to avoid frequently performing time-consuming nonlinear finite element analysis. The suboptimization problems are then solved by the quasi-Newton method combined with the backtracking line search strategy. We showcase various applications to validate our design approach, including actuators for basic extension, bending, and twisting motions, and continuous robot arms that can perform desired in-plane and out-of-plane configurations. We also show that our method can address design of multiple chambers for achieving multiple target deformation behaviors, by co-optimizing the morphological shape and air pressures, which is validated by two examples.

The field of soft robotics is rapidly evolving, and there is a growing interest in developing soft robots with bioinspired features for use in various applications. This research presented the design and development of 3D-printed origami actuators for a soft robot with amphibious locomotion and tongue hunting capabilities. Two different types of programmable origami actuators were designed and manufactured, namely Z-shaped and twist tower actuators. In addition, two actuator variations were developed based on the Z-shaped actuator, including the pelvic fin and the coiling/uncoiling types. The Z-shaped actuators were used for the rear legs to facilitate the locomotion of the water-like frogs. Meanwhile, the twisted tower actuators were used for the rotation joints in the forelegs and for locomotion on land. The pelvic fin actuator was developed to imitate the land locomotion of the mudskipper, and the coiling/uncoiling actuator was designed for tongue hunting motion. The origami actuators and soft robot prototype were tested through a series of experiments, which showed that the robot was capable of efficiently moving in water and on land and performing tongue hunting motions. Our results demonstrate the effectiveness of these actuators in producing the desired motions and provide insights into the potential of applying 3D-printed origami actuators in the development of soft robots with bioinspired features.

Dielectric elastomer actuators (DEAs) possess characteristics closest to human muscles and have been rapidly developed. The rolled DEA is considered more suitable as a driving module due to its ability to output unidirectional deformation. This paper proposes a high-performance actuator, which is connected in series by a spring and a DEA, wherein the spring realizes the pre-stretching of the DEA. To predict the mechanical behavior of the structure, the rolled DEA is simplified into a single-layer tubular DEA, and a static model is established to predict the free displacement and blocking force under different input voltages, spring stiffness coefficients, and geometric parameters of the structure. The optimal pre-stretch effect was tailored by finding the optimal combination of spring stiffness and pre-stretch through a two-dimensional search, thereby maximizing the free displacement, blocking force, or equivalent work. Under the optimal parameter combination aimed at maximizing the equivalent work, the system achieves a free displacement of 0.43 mm and a blocking force of 0.57 N. These values are 3.6 times higher for free displacement and 1.54 times higher for blocking force compared to the springless structure. The effectiveness of the theoretical analysis model is verified by experiments, and relevant manufacturing processes are introduced. This study offers a promising approach to analyzing the effect of pre-stretch on the performance of rolled DEAs, opening up new possibilities for soft robotics applications.