Soft Robotics

Gecko-inspired soft robots offer unique advantages for confined-space operations. Here, we introduce a soft climbing robot designed for tasks such as spacecraft inspection and disaster response. Unlike conventional attachment mechanisms relying on claws, suction-based adhesion, or electromagnetic components, the proposed system integrates polyurethane-based dry adhesive footpads, which passively adapt to a wide range of surfaces and require no external power. A key innovation of this work is the incorporation of variable stiffness footpads capable of actively tuning their mechanical properties in response to surface interaction demands, thereby optimizing both attachment stability and detachment efficiency during locomotion. Experimental characterization indicates that these footpads achieve an adhesion-to-detachment force ratio of 11:77, demonstrating a favorable balance between strong attachment and reliable release. Furthermore, the robot integrates a bioinspired crawling mechanism that synchronizes limb actuation with the deformation of a flexible spine structure, effectively enhancing propulsion and maneuverability across irregular terrain. Validation experiments conducted on diverse surface types confirm the robot’s environmental adaptability and highlight its potential for deployment in constrained, unstructured, and dynamically changing operational contexts.

This article presents a method for modifying the propagation speed of deformation waves on a thermoplastic bistable lattice, which consists of multiple bistable structures mechanically connected in a chain. This can be achieved by altering the energy difference between its stable states using thermal bending. Unlike traditional methods requiring material or structural changes, the proposed approach dynamically switches the propagation speed without disassembly. In contrast to magnetic and pneumatic control methods, the thermally tunable bistable lattice requires no external magnetic field, pumps, or tubing and adds minimal extra mass, enabling in situ and local retuning of the propagation speed while preserving a simple, compact system architecture. A caterpillar-like robot, composed of four pairs of oppositely arranged legs, each actuated by a bistable structure, was used to demonstrate the efficacy of the proposed method. The robot achieves crawling locomotion using only a single motor, as the leg movements are sequentially triggered through mechanical propagation of deformation, with the propagation speed tunable via the thermal treatment. Experimental results showed that slower wave propagation enhanced locomotion on a 45° incline by increasing rail grip, achieving a climbing speed of 0.95 mm/s. These results highlight the unique advantages of thermally tunable bistable lattices over conventional actuation schemes and contribute to the development of soft robots capable of adaptive locomotion in unstructured environments such as pipeline/conduit inspection and endoluminal/endoscopic navigation.

Human fingers are one of the most remarkable organs for handling complex tasks or manipulating unknown objects, not only due to its dexterous and powerful movement capabilities but also its rich kinematic sense at joints and tactile sensing at skins. Shape and tactile sensing are crucial for soft pneumatic fingers to achieve embodied intelligence. Reliable tactile sensing of soft pneumatic-driven robots is particularly challenging due to its large deformation and adaptability. Here, we propose a distributed local curvature sensing-based solution for simultaneous shape and tactile perceptions in real-time. Utilizing 4 seamlessly integrated bidirectional bending curvature sensing units, real-time shape curve, contact location, and contact force can be obtained. Experimental results indicate a maximum shape reconstruction error of 0.3 mm (when the reconstruction length is 90 mm) and a force estimation error of 0.02 N (RMSE, range 0–0.4 N). Moreover, a two-finger gripper was developed; shape and tactile sensing during grasping of diverse objects (varies in weight, size, stiffness) and force-controlled grasping are achieved. Utilizing the shape-sensing and contact-event detection capabilities, dimension of the grasped objects can be recognized in real-time. This work provides an effective, highly robust, easy-to-implement, and transformative perception solution for soft bionic fingers and beyond.

In nature, birds perch to rest and to survey their predators and prey. In human-managed contexts, perching also facilitates interaction with humans such as falconry. Recently, researchers have developed perching-capable aerial robots as a way to save energy, and deformable structures demonstrate significant advantages in efficiency of perching and compactness of configuration. However, ensuring flight stability remains challenging for deformable aerial robots due to the difficulty of controlling flexible arms. Furthermore, perching for human interaction requires high compliance along with safety. Thus, this study aims to develop a deformable aerial robot capable of perching on humans with high flexibility and grasping ability. To overcome the challenges of stability of both flight and perching, we propose a hybrid morphing structure that combines a unilateral flexible arm and pneumatic inflatable actuators. This design allows the robot’s arms to remain rigid during flight and soft while perching for more effective grasping. We also develop a pneumatic control system that improves pressure regulation while integrating safe and compliant contact and adjustable grasping forces, enhancing interaction capabilities and reducing energy consumption. Besides, we focus on the structural characteristics of the unilateral flexible arm and identify sufficient conditions under which standard quadrotor modeling and control remain effective in terms of flight stability. Finally, the developed prototype demonstrates the feasibility of compliant perching maneuvers on humans, as well as the robust recovery even after arm deformation caused by thrust reductions during flight. To the best of our knowledge, this work is the first to achieve an aerial robot capable of perching on humans for interaction.

Soft growing robots, commonly referred to as vine robots, have demonstrated a remarkable ability to interact safely and robustly with unstructured and dynamic environments. It is therefore natural to exploit contact with the environment for planning and design optimization tasks. Previous research has focused on planning under contact for passively deforming robots with preformed bends. However, adding active steering to these soft growing robots is necessary for successful navigation in more complex environments. To this end, we develop a unified modeling framework that integrates vine robot growth, bending, actuation, and obstacle contact. We extend the beam moment model to include the effects of actuation on kinematics under growth and then use these models to develop a fast parallel simulation framework. We validate our model and simulator with real robot experiments. To showcase the capabilities of our framework, we apply our model in a design optimization task to find designs for vine robots navigating through cluttered environments, identifying designs that minimize the number of required actuators by exploiting environmental contacts. We show the robustness of the designs to environmental and manufacturing uncertainties. Finally, we fabricate an optimized design and successfully deploy it in an obstacle-rich environment.

Biohybrid robots actuated by living cells/tissues, a soft robot system that integrates many advantages of life systems and mechanical systems, are promising candidates for developing a new generation of biomedical and environmental monitoring robots. However, due to the limited muscle contraction performance and lack of flexible muscle contraction modes, biohybrid robots’ low speed and flexibility have become a major challenge for their application. To overcome the limitation, different from the existing contraction mode along the longitudinal axis with pulse stimulation, we firstly adopted the square wave stimulation on triceps femoris tissue with pennate fibers arrangement from bullfrogs and found a novel muscle swinging mode with high flexibility and controllability. Based on it, we developed a biomimetic crawler actuated by triceps femoris tissue. The crawler achieved fast forward movement (average speed: ∼6.19 mm/s; maximum speed: ∼7.35 mm/s) and flexible turning ability (∼14.77°/s and ∼9.55°/s for left and right turning speed, respectively) in a liquid environment at room temperature. We believe that the results provide valuable references for the development of soft robots driven by muscle tissue and pave the way to fulfill lifelike motions and break through limitations in conventional biohybrid robots.

Patients suffering from chronic severe pulmonary thromboembolism need pulmonary thromboendarterectomy (PTE) to remove the thrombus and intima located inside pulmonary artery (PA). During the surgery, a surgeon holds tweezers and a dissector to delicately strip the blockage, but available tools for this surgery are rigid and straight, lacking distal dexterity to access into thin branches of PA. Therefore, this work presents a novel robotized dissector based on concentric push/pull robot (CPPR) structure, enabling entering deep thin branch of tortuous PA. Compared with conventional rigid dissectors, our design characterizes slenderness and dual-segment-bending dexterity. Owing to the hollow and thin-walled structure of the CPPR-based dissector, as it has a slender body of 3.5 mm in diameter, the central lumen accommodates two channels for irrigation and tip tool, and space for endoscopic camera’s signal wire. To provide accurate surgical manipulation, optimization-based kinematics model was established, realizing a 2-mm accuracy in positioning the tip tool (60-mm length) under open-loop control strategy. As such, with the endoscopic camera, traditional PTE is possible to be upgraded as endoscopic PTE. Basic physical performance of the robotized dissector, including stiffness, motion accuracy, and maneuverability, was evaluated through experiments. Surgery simulation on ex vivo porcine lung also demonstrates its dexterity and notable advantages in PTE.

Insects navigate cluttered environments using slender, flexible antennae densely packed with mechanosensors, a lightweight, energy-efficient solution for tactile perception. We introduce CITRAS (Cockroach-Inspired Tactile Robotic Antenna Sensor), a miniature, compliant, multi-segment tactile probe aimed at enabling similarly capable close-range perception on insect-scale robots under stringent size, mass, and power constraints. CITRAS (total size:   73.7   ×   15.6   mm   ×   2.11   mm; mass: 491   mg ) features eight flexural hinge segments, each with high-resolution capacitive sensors embedded within a compliant multilayer laminate structure, that detect femtofarad-scale capacitance changes induced by hinge deflection. Through systematic mechanical and sensing characterization under both quasi-static and dynamic conditions, we demonstrate sub-degree angular precision (max error ≤  0.8 ∘), accurate shape reconstruction, and consistent repeatable performance with minimal hysteresis in slow bending. Under rapid interactions, CITRAS exhibits low damping and rich dynamic responses that encode environmental features. We further validate the system in three core tactile tasks: estimating body-to-wall distance (error ≤  8 % ), measuring object gap width (error ≤  7 % ), and discriminating between smooth and rough surface textures via spatiotemporal tactile images. These results show that CITRAS delivers a compact, distributed, bioinspired tactile modality capable of reliable environment sensing, filling a critical gap in perception for insect-scale robots. Furthermore, the antenna consumes only 32   mW (excluding MCU), making it suitable for future full deployment onboard insect-scale robots and thus paves the way for autonomous navigation and interaction in confined, unstructured, or delicate environments at this scale.

This article presents the flexible RIM Hand, a biomimetic robotic hand that precisely replicates the carpometacarpal joints and employs superelastic Nitinol wires throughout its skeletal framework. By modeling the full carpal-to-metacarpal anatomy, the design enables realistic palm deformation through tendon-driven fingers while enhancing joint restoration and supports skeletal structure with Nitinol-based dorsal extensors. A flexible silicone skin further increases contact friction and contact area, enabling stable grasps for diverse objects. Experiments show that the palm can deform up to 28%, matching human hand flexibility, while achieving more than twice the payload capacity and three times the contact area compared with a rigid palm design. The RIM Hand thus offers improved dexterity, compliance, and anthropomorphism, making it promising for prosthetic and service-robot applications.

Accurate and efficient simulation tools are essential in robotics, enabling the visualization of system dynamics and the validation of control laws before committing resources to physical experimentation. Developing physically accurate simulation tools is particularly challenging in soft robotics, largely due to the prevalence of geometrically nonlinear deformation. A variety of robot simulators tackle this challenge by using simplified modeling techniques—such as lumped mass models—which lead to physical inaccuracies in real-world applications. In contrast, high-fidelity simulation methods for soft structures, such as finite element analysis, offer increased accuracy but lead to higher computational costs. In light of this, we present a discrete differential geometry-based simulator that provides a balance between physical accuracy and computational efficiency. Building on an extensive body of research on rod and shell-based representations of soft robots, our tool provides a pathway to accurately model soft robots in a computationally tractable manner. Our open-source MATLAB-based framework is capable of simulating the deformations of rods, shells, and their combinations, primarily utilizing implicit integration techniques. The software design is modular for the user to customize the code—for example, add new external forces and impose boundary conditions—to suit their requirements. The implementations for prevalent forces encountered in robotics, including gravity, contact, kinetic and viscous damping, and hydrodynamic and aerodynamic drag, have been provided. We provide several illustrative examples that showcase the capabilities and validate the physical accuracy of the simulator. The open-source code is available at https://github.com/StructuresComp/dismech-matlab. We anticipate that the proposed simulator can serve as an effective digital twin tool, enhancing the Sim2Real pathway in soft robotics research.

Millimeter cable-driven continuum robots exhibit shape conforming, dexterous manipulation capabilities in constrained environments. They are increasingly used for narrow space and endoluminal intervention. For delicate manipulation, quantifying the force interaction between the robot and its surrounding environment is important for both shape adjustment and avoiding damages to luminal structures. In this work, we propose a real-time, whole-body contact estimation framework for small-scale continuum robots, based on actuation fibers and model-informed neural networks. The physical relationship among external body contact, internal actuation, and shape sensing of the continuum robot is formulated based on rod theory, and body contact estimation is treated as an inverse problem given the actuation tension profile and robot shape as inputs. The contact position and force are estimated using a neural network, and a generative adversarial network-based data augmentation strategy is proposed to reduce the need for large amounts of real data from the continuum robot under external forces. In addition, an automatic data acquisition platform is developed to efficiently collect the small amount of required data. Experiments with notched continuum robots were conducted to demonstrate the general applicability and accuracy of the proposed approach. The results show that the mean estimation errors for the three-dimensional (3D) contact position and contact force magnitude are 1.7 mm (2.3%) and 8.7 mN (5.8%), respectively, with an estimation frequency of 25 Hz. It paves the way for embodied integration using multiplexed fibers for the simultaneous actuation and sensing of millimeter-scale continuum robots, enabling their safer operation in confined spaces through machine intelligence.

It is challenging to simultaneously estimate the shape and contact force of miniature continuum surgical robots due to the limited sensing capabilities, deformable structure, and complexity of the external environment they operate in. In this work, the integration of a quaternion-based multicontact Cosserat model with contact force regularization enables a dual-pronged approach for simultaneous shape and body contact force estimation using minimum shape measurement input (i.e., tip position). In addition to internal actuation, cable friction, and material nonlinearity, the proposed state estimation approach also accounts for multiple environmental contacts with clinically relevant mixed geometric constraints (point, plane, and curved surface), as these complex and variable anatomical interactions critically influence the robot’s behavior. The solving procedure is then reformulated as a nonlinear large-scale optimization problem to improve the state estimation robustness. Finally, both simulations and experiments on a cable-driven variable-stiffness notched-tube continuum robot were conducted to validate the proposed state estimation approach, demonstrating a comparable level of accuracy to state-of-the-art methods.