Encountered-type pin-array shape displays allow users to freely explore a simulated shape using bare fingers. However, current pin-array shape displays are limited to discontinuous shapes or 2.5D shapes. Here, we propose a novel shape display principle to render touch feedback between a virtual hand and objects in a virtual reality environment. The configuration of the shape display with 20 actuators consists of four finger-like units. The shape display renders the local contact regions between the shape and the user's finger. Users can sense the touch of the virtual objects by encountering the finger-type shape display. A fiber-reinforced origami soft actuator was designed to allow the finger-like unit to bend and laterally swing like a finger joint. The device's kinematic model and the shape planning algorithm for finger-object interaction were developed to map the virtual shape and user fingers to the finger-object contact regions and the actual actuation parameters. Six categories of 11 virtual shapes were rendered by the shape display. The results indicated that the proposed device can render 3D continuous edge and surface-type shapes. We envision that the shape display can render a wide range of shapes in combination with a mobile platform to reduce the complexity of haptic shape display in virtual worlds using a relatively small number of actuators.
The six-strut spherical tensegrity robot (TR-6), known for its lightweight, high robustness, and adaptability, demonstrates superior kinematic performance due to its significant structural deformability. Its symmetric geometry is well suited for rolling locomotion, enabling the robot to adapt to changing terrain, navigate unstructured environments, and perform missions even after suffering massive damage. This article investigates the minimization of actuation cost for tumbling motion in TR-6 by employing a hybrid optimization framework based on the beetle antennae-genetic algorithm. To this end, an energy-efficient propelling model is formulated, in which strain energy discrepancy is adopted as the objective function, and constraints such as gravitational moment, strain energy limit, and cable regulation bounds are incorporated. The nonrigid-body motion analysis method is applied to determine the robot's equilibrium posture under unbalanced forces. The proposed approach is validated through multibody dynamic simulation using the ADAMS software and further confirmed by physical prototype experiments with motor-driven TR-6 hardware. Results demonstrate that the proposed method effectively identifies actuation strategies with reduced energy consumption and can be extended to other multi-strut or strut-actuated tensegrity robotics.
This article presents a dynamics-based path-planning framework for tensegrity robots that accounts for environmental interaction. A systematic investigation is conducted into the mapping among locomotion gaits, actuations, and ground interaction forces of a six-bar tensegrity robot. The study addresses three coupled challenges within a unified framework: the dynamic effects of flexible-body deformation and environmental interaction, the construction of a finite gait library for realizing discrete motions, and a dynamics-informed local path-planning strategy for varying contact and friction conditions. A general dynamic model is established to describe contact friction and generate interaction-aware gaits. Subsequently, a gait library is developed that maps sequences of gait primitives to commanded motions. A lightweight local planning strategy, denoted as M1L2T3, is then formulated on this gait-primitive graph to enable efficient path selection under local map information. A proof-of-concept prototype is fabricated for experimental validation. Experimental results confirm the effectiveness of the interaction-coupled dynamic model for gait generation and locomotion control, showing reasonable agreement with theoretical predictions and validating the proposed planning strategy. The results demonstrate the value of incorporating interaction-aware gait realization into locomotion planning for tensegrity robots.
Endovascular interventions demand catheters that can actively steer through tortuous vessels and dynamically increase stiffness at the target to ensure precise therapy. Most existing steerable catheters achieve steering and variable stiffness (VS) by multiple sources, leading to reduced efficiency advantage, increased bulkiness, and safety risks. This work introduces a magnetic jamming method that allows steering and VS using one magnetic source. First, a carrier-free, matrix-free magnetic jamming scheme is introduced that directly encapsulates soft-magnetic powder in coaxial thin-walled tubes, enabling single-source field-driven steering and reversible VS via interparticle jamming. Next, an analytical micro-to-macro stiffness model is established that explicitly links field parameters and particle properties to catheter-level outputs (i.e., stiffness and steering angle) by incorporating particle interaction, thus providing closed-form, physics-based estimations. Finally, we validate the method at both tip and catheter levels, confirming the stiffness model and substantial field-tunable stiffness modulation (up to 300-fold), with benchmarking against vacuum jamming as a reference to validate its effectiveness and demonstrating a multifunctional prototype that enables both steering and VS using a single magnetic source. Our magnetic jamming approach enables on-demand steering and rapid, large-range stiffness modulation in millimeter-scale catheters, promising faster navigation with lower contact forces, more stable device deployment, and a clearer path to autonomous, workflow-friendly endovascular interventions.
Multimodal soft grippers can adapt grasping strategies to diverse environments, yet integrating sensors under large, coupled deformations remains challenging. Inspired by the mechanosensation of sea anemones and their ability to swallow, this article presents a soft gripper that integrates eight toroidal optical waveguides to realize three modes-contacting, expansion, and swallowing-with continuous proprioceptive feedback. Deformation-induced optical attenuation, processed by machine-learning pipelines, enables perception of object shape, hardness, and surface texture. Experiments show a 0.04 N detection limit, a 0.006 N resolution, a 55 ms response time, and sensitivity >1.4 dB/N, with machine-learning classification achieving >89% accuracy. Mode-specific experiments demonstrate sensing across the entire soft gripper with integrated optical waveguides. The outer surface localizes contact after inflation, the inner surface provides circumferential contact sensing of irregular objects during swallowing; and the pedal interface at the base distinguishes surface hardness and texture, achieving perception on the outer, inner, and bottom interfaces. We also demonstrate multi-object swallowing that grasps and counts 1-4 transparent bottles in real-time and a breakfast task that switches grasping modes to grasp a bowl and cup, swallowing fragile items without damage. These results show that our design enables mode-switching interactive perception and expands opportunities for soft robotics in fragile product handling and laboratory automation.
Soft robots require tactile sensors capable of quantifying the mechanical properties of unknown objects during manipulation, yet most existing approaches are fragile, costly, or limited in dynamic range. Here, we present a model-guided plant-inspired hydraulic tactile sensor in which contact-induced deformation of a compliant elastomer generates a measurable pressure change in an embedded liquid-filled channel. By combining pressure and deformation measurements with an analytical elastic-hydraulic contact model, the effective Young's modulus of the contacted spherical object can be inferred without direct force sensing. The accessible stiffness range is set by the sensor's elastic modulus and channel geometry; we demonstrate this design tunability using four sensor variants, enabling accurate stiffness estimation over more than two orders of magnitude. Integrated into a low-cost (under US$50) three-dimensional-printed robotic arm, the sensor performs real-time modulus estimation under quasi-static conditions using measurements of object diameter, deformation, and internal pressure. A predictable operating window, expressed as the stiffness ratio between the object and the sensor, maximizes measurement accuracy within the model's linear-elastic regime. Validation on synthetic polymers and fresh produce demonstrates applications ranging from laboratory material characterization to nondestructive monitoring of fruit ripening, advancing accessible and quantitative tactile sensing for soft robotic systems.
Soft capacitive tactile sensors are widely employed in human-machine interfaces and wearable devices due to their high sensitivity, temperature stability, and low energy consumption. However, the electrical connections between soft capacitive tactile sensors and measurement circuits introduce parasitic capacitance and series resistance, which compromise stability. While coaxial cables and shielding layers are typically used to suppress electromagnetic interference, their nonstretchable and multilayer structures hinder the structural flexibility and robustness of soft sensors. To address this challenge, inspired by biological pulse-coded signals, we propose an ultrastable soft capacitive tactile sensor with impedance-modulated signal. The impedance-modulated sensor converts capacitive signals into impedance-modulated signals by constructing a series resonant circuit, achieving ultrastability against the parasitic and stray capacitance as well as series resistance. The mechanism of the impedance-modulated sensor is theoretically and numerically analyzed, and demonstrated by experiments. In addition, we discovered that compressive stress decreases the equivalent series resistance (ESR) of the liquid metal elastomer used as the dielectric in the capacitive sensor, which in turn affects the impedance-modulated signal. The mechanism of the variation in ESR is analyzed through simulations and experiments. Finally, the applications of the impedance-modulated sensor in human-machine interaction interfaces and wearable electronics are demonstrated.
To reduce the risk of patient cross-contamination associated with reusable endoscopes, health care systems are increasingly turning to single-use devices. While this shift improves hygiene, it raises significant environmental and economic concerns due to increased medical waste and long-term costs. In response, this study proposes a semi-disposable pneumatic endoscope concept designed to support the transition toward greener endoscopy. It is composed of a reusable handle and a disposable, pneumatically actuated insertion tube. The study focuses on the design, modeling, and prototyping of the flexible part of the endoscopic tube to address challenges linked to soft actuation. A finite element model was developed to simulate the bending behavior of the flexible segment made of silicone. It allows the study of key geometrical parameters, such as pneumatic chamber diameter and wall thickness. A ring-based constraint system was implemented to address excessive radial expansion and associated bulging under pressure, which effectively minimized radial deformation without compromising bending capability. The design obtained through simulation integrates all essential functionalities of a clinical gastroscope (imaging, tool insertion, and insufflation/suction) within an outer diameter of 14 mm. It was experimentally validated using physical prototypes, confirming the ability to bend up to 180°, with deformation patterns closely matching simulation predictions. In addition, the prototype supports future integration of a variable stiffness mechanism using jamming-based techniques, further enhancing its potential for clinical use. This work thus proposes a viable path toward more sustainable and clinically relevant endoscopic solutions using pneumatic actuation.
Ionic polymer-metal composites (IPMCs) are prominent soft actuators for millimeter-scale robotics due to their large bending deformation and high force-to-weight ratio. This work introduces an integrated soft robotic system featuring a three-channel IPMC gripper attached to a borescope for micromanipulation. A laser ablation technique partitions a single IPMC into three independently actuated channels, simplifying wiring complexity and enabling stable grasping of objects with complex geometries. While the hardware provides effective manipulation, teleoperation in microscale environments presents a significant challenge, as operators struggle to reliably determine contact and grasp stability from 2D visual feedback alone. To address this, we introduce a vision-based operator assistance system. Using a YOLOv8 segmentation model, our system processes the borescope's video feed in real-time to identify the gripper fingers and target objects, providing clear visual cues for "touched" and "grabbed" states. This human-in-the-loop feedback enhances operator precision and consistency and, for the first time, enables a quantitative evaluation of the gripper's performance. Experimental results demonstrate reliable grasping of diverse objects (0.3-6 mm) and validate the system's ability to provide objective success metrics. This work contributes an integrated and intelligent micromanipulation system that augments human capabilities and establishes a framework for future autonomous control.
Shape memory alloys (SMAs) are highly suitable for flexible and soft robots due to their lightweight nature, high power density, and miniaturization potential. However, the design of SMA-based continuum bending joints for robotic applications is often restricted by the inherent trade-offs in their mechanical characteristics. In this work, we present an integrated multicomponent framework that encompasses four submodels, including bending angle, load-deflection stiffness, distal twisting angle, and output force for antagonistic SMA wire bending joints. The framework unifies all submodels under a common temperature input set and systematically clarifies their coupled relationships, providing quantitative insight into their trade-offs. A model-based optimal design methodology is then developed for the SMA bending joint, balancing the different mechanical performance metrics to deliver an optimal joint design that satisfies the task requirements. Experiments on the SMA bending joint with optimal design parameters validate the accuracy of various submodels. Two additional nonoptimal prototypes were experimentally evaluated against the optimized design, validating the feasibility of the proposed optimal design methodology. The integrated multicomponent modeling framework and optimal design methodology address the intricate coupling effects inherent in antagonistic SMA wires, minimizing the need for iterative prototyping and facilitating the adoption of SMA wires in flexible and soft robots.
Soft underwater grippers are well-suited for ecological sampling, providing flexibility in handling specimens of different sizes and shapes while reducing environmental impact through their compliance. Yet they are usually employed on big remotely operated vehicles, constraining their application by size, which can disturb aquatic habitats and limit their ability to access remote areas without shoreline access such as mountain and forest lakes. To support efficient underwater exploration, we designed, developed, and tested an aerially deployed underwater vehicle featuring a compact, lightweight soft gripper. This design reduces water disturbance and enables precise navigation in confined underwater environments, significantly expanding operational capabilities underwater. By analyzing the pod's volume changes, buoyancy actuation, and propulsion mechanisms, we derive a simplified dynamic model to describe the underwater motion. We developed a control framework that decouples buoyancy, thrust, and yaw to enable independent control of underwater motion. Precise buoyancy control, essential for navigating interstices without causing ecological harm, was achieved with feedback control loops taking water depth as feedback, showing a rise time of under 5 s and a 10% settling time within 30 s. Yaw control, achieved via inertial measurement unit feedback, exhibited a rise time of less than 10 s with oscillations of 10%-25% around the set values. This system enhances underwater grasping, extends mission reach and efficiency, and helps minimize environmental disruption.
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.
Origami techniques have significantly impacted robotics, expanding its capabilities in shape transformation. Pop-up transformations, inspired by pop-up books, offer intriguing applications in robotics fields, including deployable robots. However, designing origami-inspired robots for transitioning to a completely flat state poses unique challenges, particularly in multistate passive actuation. This article introduces the "pop-up catcher," a gripper designed for multistate passive actuation that can be folded flat and actuated passively to grasp the object. To ensure its reliable state transition, we conduct "transition path planning" with the potential energy surface modulation. We demonstrate the pop-up deployment and passive capture of the target object using our flat-foldable catcher comprised of our pop-up gripper and self-locking modular Sarrus origami that can be folded into a profile less than 25 mm thick while capturing objects over 500 mm away.
Soft eversion-based growing robots, also known as vine robots, are a subclass of soft continuum robots that navigate their environment through tip extension-an eversion-based growth mechanism inspired by climbing plants. A deeper understanding of the underlying physics and dynamics of this unique locomotion strategy is crucial for expanding the applicability of soft eversion-based growing robots in complex and constrained environments. Despite their potential, comprehensive dynamic models that capture the full system behavior, including internal pressure dynamics and the pneumatic supply system, remain limited. In this study, we develop a first-principles-based dynamic model of a pressure-driven soft eversion-based growing robot, incorporating both the internal pressure evolution and the flow dynamics of the pneumatic supply system. The proposed model is simulated and experimentally validated on custom-built soft eversion-based growing robots. The proposed model demonstrates excellent predictive capability, achieving a root mean square error (RMSE) of 0.066 m, corresponding to about 5.5% of the final everted length. These findings highlight the critical importance of integrating both pressure and flow dynamics in modeling soft eversion-based growing robots to enable improved control strategies and deeper insight into their physical behavior.
Magnetic soft continuum robots (MSCRs) offer the possibility for wireless manipulation, compliant shape-forming, and miniaturization to the milli- and submillimeter scales. This presents them as an attractive choice in the development of robotic guidewires and catheters for endovascular applications. However, few approaches have considered strategies for geometric modification to enhance navigation and therapeutic delivery. These aspects are of high relevance for applications such as intra-arterial chemotherapeutic delivery. Here, we present an octopus tentacle-inspired MSCR with a monolithic material composition, tapered geometry of ≤ 2 mm, and integrated microchannels. We consider the suitability of a discrete elastic modeling approach alongside finite element based and material point method (MPM) simulations for capturing the deflection behavior of the tapered design under magnetic actuation. The MPM demonstrates the greatest accuracy, with root mean square errors in tip angle between 2.74° and 5.28°. For higher taper designs, experimental results highlight improved deflection under low magnetic field strengths (<5 mT) and an improved workspace at high actuation angles (up to 320°). We subsequently utilize tapered designs with a 0.66 mm distal tip diameter and embedded axial and lateral microchannel networks for localized drug simulant delivery in a neurovascular tumor phantom. We demonstrate significant improvements in localized drug delivery along specific vascular pathways in comparison to systemic intra-arterial delivery.
Navigating confined and complex environments, such as pipes, biological tissues, and collapsed debris, has remained a challenge for conventional robotic systems, which often struggle with maneuverability and adaptability. Soft toroidal robots offer a promising alternative, with a compact and lightweight toroidal shape that allows continuous movement without requiring bulky external equipment. However, the lack of a steering mechanism has limited their applicability in dynamic and complex terrains. To overcome this, we developed a steering mechanism that leverages the bistable characteristics inherent in the toroidal structure to enable curvature formation. By adjusting the position of the tail within the structure, the robot can change its direction of bending, enabling flexible and responsive steering. To achieve this bistable behavior, we utilized the orthotropic properties of ripstop nylon fabric, reducing the robot's bending stiffness and enhancing its steering capabilities. Through theoretical modeling and experimental validation, we identified key design parameters, such as optimal operating pressure and steering device length. The proposed soft toroidal robot, with a diameter of 70 mm and a total length of 400 mm, achieves 1-degree of freedom (DOF) steering by exploiting this bistable deformation. Our experiments demonstrated its ability to navigate a T-shaped pipe and climb vertically in confined spaces, achieving a maximum curvature of 13.4 m-1. These findings highlight the potential of soft toroidal robots for maneuvering through both confined and open environments with enhanced adaptability and efficiency.
Fluidic circuits offer an effective route to electronics-free control in soft robots. However, limited tunability of hysteresis in existing fluidic valves constrains their ability to exploit the analog characteristics of pressure inputs. As a result, many fluidic circuits rely on digital control with multiple external inputs, increasing system complexity. Here, we develop a hysteresis-guided pressure-threshold encoder (PTE) composed of hysteresis-tunable valves (HTVs) to convert a programmed pressure input into multiple digital outputs. By customizing the hysteresis behavior of the HTVs, the PTE provides a well-defined resolution for detecting variations in the input pressure. The PTE allows two modes of input-pressure programming: automated sequencing with an electronic device and manual sequencing with a mechanical pressure regulator. Integration of the PTE as an input module within digital fluidic circuits verifies its scalability and compatibility. Experimental demonstrations on soft joints and soft robotic hands confirm that the PTE enables multiactuator control using minimal inputs. Overall, this work provides an effective encoding method to reduce the number of external pressure lines from n to 2, providing a new approach for compact, electronics-free, and scalable control systems in soft robotics.
Among various soft actuators, soft pneumatic actuators (SPAs) powered by pressurized gases have been widely adopted due to their structural simplicity, ease of fabrication, and ability to generate diverse and adaptable motions. However, conventional mechanical compressors and chemical reaction-based pneumatic sources that supply pressurized gas to these actuators often generate substantial acoustic noise and heat, limiting their practical use. In this study, we propose an electronics-free, near-silent, and low-heat pneumatic source that integrates endothermic-exothermic chemical reactions to balance heat generation while producing pressurized gas. By only using passive mechanical components that autonomously regulate the reaction process, the pneumatic source achieved a steady output pressure of 7 bar and a flow rate of 6 liters per minute through the chemical reactions between phosphoric acid and cesium bicarbonate. The proposed pneumatic source operated with minimal temperature rise (maximum 36°C) and low acoustic noise of 40 dBA, comparable to the ambient noise of a quiet residential environment. The proposed approach demonstrates an enthalpy-balanced acid-bicarbonate system for combining endothermic and exothermic reactions to achieve thermally balanced and noise-suppressed pneumatic operation. Therefore, the proposed concept can serve as a practical and versatile pneumatic source for driving a wide range of SPAs.
This article presents an integrated fabrication and simulation framework for a cable pneumatic soft robot system capable of dexterous motions and complex functions. These cable pneumatic robots can harness pneumatic actuation for large shape morphing and utilize cable actuation for superior controllability. We first created a novel and low-cost fabrication method to build the proposed robots, including the soft robot structures and the controller hardware. In parallel, we developed a lumped parameter model to simulate the complex behaviors of cable pneumatic robots. This simulation platform is computationally more efficient than conventional finite element methods because it uses specially derived lumped elements with sparser nodes and less degrees-of-freedom. In addition, we use experiments to show that the model can accurately capture the bending stiffness and the actuation angle of the cable pneumatic robots. Finally, demonstration examples are presented to highlight the capabilities of the proposed robots and the versatility of the simulation. Realistic physical prototypes are presented to show that these robots can execute adaptive grasping motions and handle sophisticated tasks. Computational examples are presented to show that the proposed model can achieve close-to-real-time simulation. More significantly, we can implement cosimulation of cable pneumatic robots and the inverse kinematics of UR5e cobots by combining the proposed lumped parameter model with existing robotic simulators. Such capabilities enable the proposed simulation to have wide applications for different soft robotic systems.
Shape memory alloys (SMAs) or shape memory polymers (SMPs) enable soft actuators to achieve advanced adaptabilities applied in soft robotics. However, actuators that combine multiple shape memory materials struggle to achieve complex deformation effects and stiffness variations with effective control strategies. To achieve controllable, shape adaptation, and programmed deforming behavior, this study proposes an integrated control strategy for an SMA-SMP based programmable morphing structure used as an actuator in soft robotics. To achieve precise control over programming deformations and stiffness variation, a multi-target thermal sensing method (MTTSM) was proposed, integrated into an interaction-driven control framework. Based on MTTSM, the coordinated actuation between the SMA springs and the SMP structure is realized, enabling standby of preheating, stepping with programmed deformations, and dynamic stiffness changes. In addition, to achieve dynamic monitoring of deformed states, the co-training-based monitoring system is developed for collaboration, enabling the use of multisensor fusion for position estimation in the absence of end-effectors that can directly measure the deformed structure of the flexible body. In conclusion, the proposed integration strategy of MTTSM and the cotraining monitoring system offers a control solution for integrating multiple shape memory materials into morphing structures as smart actuators applied to soft robotic applications.
