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Soft electromagnetic artificial muscles (SEAMs) that use electric currents are reported as their power sources. The proposed actuator consists of fully soft components: microfluidic coils, stretchable magnets, ferromagnetic silicone, and stretchable housings. The soft coils are fabricated by directly printing room‐temperature liquid metal on a stretchable substrate, enabling the generation of high‐density electromagnetic fields. Based on design optimization through modeling and simulation, the proposed actuators have a characteristic of bistability following the relationships of the forces acting on the components. Depending on the design configurations, the proposed actuators generate contraction and expansion motions as well as vibrations in a bidirectional manner, enabled by electromagnetic actuation. The main advantages of the proposed actuators are fully compliant structures, compact form factors, and short response times, which have not been observed in existing polymer‐based artificial muscles. Another advantage is the self‐detection of the actuation states by measuring the inductance change in the coils. Last, the modular design fully packaged with a coil and magnets in a soft housing makes it possible to easily resize and reconfigure the robotic systems with multiple actuator modules for different applications. Examples of applications demonstrated are a modular crawling robot, energy‐efficient grippers, a multi‐degrees of freedom (DOF) soft manipulator, and a high‐frequency swimming robot. [ABSTRACT FROM AUTHOR]

Lumbar spine injuries resulting from heavy or repetitive lifting remain a prevalent concern in workplaces. Back-support devices have been developed to mitigate these injuries by aiding workers during lifting tasks. However, existing devices often fall short in providing multidimensional force assistance for asymmetric lifting, an essential feature for practical workplace use. In addition, validation of device safety across the entire human spine has been lacking. This paper introduces the Bilateral Back Extensor Exosuit (BBEX), a robotic back-support device designed to address both functionality and safety concerns. The design of the BBEX draws inspiration from the anatomical characteristics of the human spine and back extensor muscles. Using a multi–degree-of-freedom architecture and serially connected linear actuators, the device's components are strategically arranged to closely mimic the biomechanics of the human spine and back extensor muscles. To establish the efficacy and safety of the BBEX, a series of experiments with human participants was conducted. Eleven healthy male participants engaged in symmetric and asymmetric lifting tasks while wearing the BBEX. The results confirm the ability of the BBEX to provide effective multidimensional force assistance. Moreover, comprehensive safety validation was achieved through analyses of muscle fatigue in the upper and the lower erector spinae muscles, as well as mechanical loading on spinal joints during both lifting scenarios. By seamlessly integrating functionality inspired by human biomechanics with a focus on safety, this study offers a promising solution to address the persistent challenge of preventing lumbar spine injuries in demanding work environments. Editor's summary: Repetitive tasks involving lifting of objects such as in industrial settings can cause injuries to the spine and back muscles. Although wearable exoskeleton suits can be used in these settings to alleviate risks to injury, they may not provide multidimensional movement during asymmetric lifting. Kim et al. have developed an active Bilateral Back Extensor Exosuit capable of multidimensional force assistance during lifting tasks. The wearable device offers multiple degrees of freedom in range of motion and was shown to provide back muscle force assistance and to decrease compression on the spines of human participants during asymmetric and symmetric lifting tasks. —Amos Matsiko [ABSTRACT FROM AUTHOR]

This study proposes a modularized soft robotic arm with integrated sensing of human touches for physical human–robot interactions. The proposed robotic arm is constructed by connecting multiple soft manipulator modules, each of which consists of three bellow-type soft actuators, pneumatic valves, and an on-board sensing and control circuit. By employing stereolithography three-dimensional (3D) printing technique, the bellow actuator is capable of incorporating embedded organogel channels in the thin wall of its body that are used for detecting human touches. The organogel thus serves as a soft interface for recognizing the intentions of the human operators, enabling the robot to interact with them while generating desired motions of the manipulator. In addition to the touch sensors, each manipulator module has compact, soft string sensors for detecting the displacements of the bellow actuators. When combined with an inertial measurement unit (IMU), the manipulator module has a capability of estimating its own pose or orientation internally. We also propose a localization method that allows us to estimate the location of the manipulator module and to acquire the 3D information of the target point in an uncontrolled environment. The proposed method uses only a single depth camera combined with a deep learning model and is thus much simpler than those of conventional motion capture systems that usually require multiple cameras in a controlled environment. Using the feedback information from the internal sensors and camera, we implemented closed-loop control algorithms to carry out tasks of reaching and grasping objects. The manipulator module shows structural robustness and the performance reliability over 5,000 cycles of repeated actuation. It shows a steady-state error and a standard deviation of 0.8 mm and 0.3 mm, respectively, using the proposed localization method and the string sensor data. We demonstrate an application example of human–robot interaction that uses human touches as triggers to pick up and manipulate target objects. The proposed soft robotic arm can be easily installed in a variety of human workspaces, since it has the ability to interact safely with humans, eliminating the need for strict control of the environments for visual perception. We believe that the proposed system has the potential to integrate soft robots into our daily lives. [ABSTRACT FROM AUTHOR]

Muscles in animals and actuation systems in advanced robots consist not of the actuation component alone; the motive, dissipative, and proprioceptive components exist in a complete set to achieve versatile and precise manipulation tasks. We present such a system as a linear electrostatic actuator package incorporated with sensing and braking components. Our modular actuator design is composed of these actuator films and a dielectric fluid, and we examine the performance of the proposed system both theoretically and experimentally. In addition, we introduce a mechanism of optical proprioceptive sensing utilizing the Moiré pattern innately generated on the actuator surface, which allows high-resolution reading of the position of the actuator without noise. The optical sensor is also capable of measuring the force exerted by the actuator. Lastly, we add an electroadhesive brake in the package in parallel with the actuator, introducing a method of mode switching that utilizes all three components and presenting control demonstrations with a robot arm. Our actuation system is compact and flexible and can be easily integrated with various robotic applications. [ABSTRACT FROM AUTHOR]

For safe coexistence between robots and humans, it is important for robots to detect the presence of nearby humans as well as any physical contacts made to its body. The design of a modular textile sensor array and an algorithm for multi‐modal sensing of human touches and other contacts with their contact forces proposed. Each sensor module in the array is capable of multi‐modal sensing, and the entire array with multiple modules requires only two wires to read the outputs from all the modules using band‐stop filter circuits. The proposed sensor system shows the structural modularity, achieved by simple fabrication of sequential lamination of conductive and non‐conductive textile materials, realizing electrical connections through conductive snap buttons that connect the modules to the circuit. The functional modularity is also achieved through the compensation algorithm, derived from the analysis of the transfer function in the frequency domain. The algorithm significantly reduces signal interferences between modules. The multi‐modality, the textile‐based design, and the structural and functional modularity of the proposed system enable practical applications to various robotic systems, including robotic skin for a collaborative robot, a wearable sensor, a robot hand sensor, and a human–computer interface, as demonstrated in this study. [ABSTRACT FROM AUTHOR]

In this article, a novel approach to enhance the maneuverability and adaptability of legged robots in challenging environments is proposed. This approach involves the integration of soft inflatable sensing skin, which provides additional mobile modes and environmental adaptability. The inflated skin's structural properties, such as buoyancy, volumed shape, and physical compliance, enable quadruped robots to extend their mobility to stable swimming and crawling modes. The inflated skin also offers physical protection through cushioning and backing effects, allowing robots to roll down stair‐like structures. Furthermore, the integration of tactile sensors provides the host robot with accurate and intuitive contact information, enabling increased environmental adaptability and responsive behavior. The robot can protect itself from impacts, detect and detour obstacles, and dynamically interact with its surrounding environment. Overall, the proposed approach offers a synergistic integration of soft inflatable sensing skin and tactile sensors to enhance legged robots' maneuverability and adaptability in harsh environments. The integrated system enables robots to achieve challenging missions, extending their capabilities beyond conventional locomotive modes. The proposed approach has significant potential applications in fields such as search and rescue, surveillance, and exploration. [ABSTRACT FROM AUTHOR]

Soft robots are promising candidates for robust grasping and manipulation of different types of objects; however, their development has been a scientific challenge because of the lack of compact, yet precise, controllable soft actuators. In this work, innovative haptic interactive systems based on sensor‐integrated actuators powered by small batteries are reported. A liquid‐metal strain sensor with a versatile form factor and robust sensing performance is incorporated into a fast‐switching high‐force ionic polymer actuator. The structure of the sensor‐integrated actuator is rationally designed through encapsulation technology to achieve the best sensitivity and actuation while maintaining compactness, allowing for accurate, real‐time estimation and tracking of the actuation process. In a closed loop between the user and sensor‐integrated actuator, the motion and manipulation dynamics of a soft gripper made of sensor‐integrated actuators can be controlled by hand motions during a task. By combining with a thimble‐type haptic feedback device, the states of the gripper can be transmitted back to the user in real‐time via the proprioception of the integrated sensor. The approach opens a prospective avenue for the development of future robotic technologies without requiring camera‐based sensors for versatile, wearable, and scalable haptic systems. [ABSTRACT FROM AUTHOR]

Soft robotics can be used not only as a means of achieving novel, more lifelike forms of locomotion, but also as a tool to understand complex biomechanics through the use of robotic model animals. Herein, the control of the undulation mechanics of an entirely soft robotic subcarangiform fish is presented, using antagonistic fast‐PneuNet actuators and hyperelastic eutectic gallium–indium (eGaIn) embedded in silicone channels for strain sensing. To design a controller, a simple, data‐driven lumped parameter approach is developed, which allows accurate but lightweight simulation, tuned using experimental data and a genetic algorithm. The model accurately predicts the robot's behavior over a range of driving frequencies and a range of pressure amplitudes, including the effect of antagonistic co‐contraction of the soft actuators. An amplitude controller is prototyped using the model and deployed to the robot to reach the setpoint of a tail‐beat amplitude using fully soft and real‐time strain sensing. [ABSTRACT FROM AUTHOR]

Recent efforts on wearable robots have focused on augmenting the motor performance and/or protecting the wearer's body with lightweight structures. However, providing human‐scale force and structural stiffness usually conflicts with the wearability. Inspired by sandwich‐structured composites with high structural strengths, widely employed in both nature and man‐made structures, a mechanism of selectively stiffening garments (SSGs) utilizing anisotropic cellular cores and rubber‐laminated face sheets is proposed. While the proposed mechanism shows a high compliance allowing for conformity to the wearer's body when unjammed, it provides a significantly high force density when jammed, compared to conventional jamming methods, allowing for the ability to adjust mechanical properties based on the designs and materials. In this paper, various designs of the sandwich jamming structures for the SSGs with analytical characterizations and experimental validations are introduced. Potential applications for force and motion assistance are also demonstrated and impacted mitigation. [ABSTRACT FROM AUTHOR]

The ability to grasp a wider range of objects in size and shape directly relates to the performance of robotic grippers. Adapting to complex geometries of objects requires large degrees of freedom to allow complex configurations. However, complexity in controlling many individual joints leads to introduction of underactuated mechanisms, in which traditional finger designs composed of revolute joints allow only flexion/extension motions. In this article, we propose a length-adjustable linkage mechanism in the underactuated finger controlled by an antagonistic tendon pair. The resulting gripper can elongate the fingers for an increased task space or shorten them for a finer spatial resolution. For tactile sensing, hyperelastic soft sensors are used to stretch with finger elongation. Contact pressures measured by the soft sensors are used in force-feedback control for which either the joint angles or the link lengths are adjusted. Lastly, a multimodal control scheme that combines elongation and flexion modes is demonstrated with tasks of dexterous manipulation. [ABSTRACT FROM AUTHOR]

Electromyography (EMG) is one of the most common methods to detect muscle activities and intentions. However, it has been difficult to estimate accurate hand motions represented by the finger joint angles using EMG signals. We propose an encoder-decoder network with an attention mechanism, an explainable deep learning model that estimates 14 finger joint angles from forearm EMG signals. This study demonstrates that the model trained by the single-finger motion data can be generalized to estimate complex motions of random fingers. The color map result of the after-training attention matrix shows that the proposed attention algorithm enables the model to learn the nonlinear relationship between the EMG signals and the finger joint angles, which is explainable. The highly activated entries in the color map of the attention matrix derived from model training are consistent with the experimental observations in which certain EMG sensors are highly activated when a particular finger moves. In summary, this study proposes an explainable deep learning model that estimates finger joint angles based on EMG signals of the forearm using the attention mechanism. [ABSTRACT FROM AUTHOR]

We propose the use of bio-inspired robotics equipped with soft sensor technologies to gain a better understanding of the mechanics and control of animal movement. Soft robotic systems can be used to generate new hypotheses and uncover fundamental principles underlying animal locomotion and sensory capabilities, which could subsequently be validated using living organisms. Physical models increasingly include lateral body movements, notably back and tail bending, which are necessary for horizontal plane undulation in model systems ranging from fish to amphibians and reptiles. We present a comparative study of the use of physical modeling in conjunction with soft robotics and integrated soft and hyperelastic sensors to monitor local pressures, enabling local feedback control, and discuss issues related to understanding the mechanics and control of undulatory locomotion. A parallel approach combining live animal data with biorobotic physical modeling promises to be beneficial for gaining a better understanding of systems in motion. [ABSTRACT FROM AUTHOR]

State estimation is one of the key requirements in robot control, which has been achieved by kinematic and dynamic models combined with motion sensors in traditional robotics. However, it is challenging to acquire accurate proprioceptive information in soft robots due to relatively high noise levels and hysteretic responses of soft actuators and sensors. In this article, we propose a method of estimating real-time states of soft robots by filtering noisy output signals and including hysteresis in the models using a Bayesian network. This approach is useful in constructing a state observer for soft robot control when both the kinematic model of the actuator and the model of the sensor are used. In our method, we regard a hysteresis function as a conditional random process model. We then introduce a dynamic Bayesian network composed of the actuator and the sensor models of the target system using distribution hysteresis mapping. Finally, we show that solving a Bayesian filtering problem is equivalent to suboptimal state estimation of the soft system. This article describes two ways for defining modeling and filtering; one is by Gaussian process regression combined with an extended Kalman filter, and the other is based on variational inference with a particle filter. While the first approach relaxes the uncertainty level in modeling to Gaussian, the second approach illustrates a general probability distribution. We experimentally validate the proposed methods through real-time state estimation of a sensor-integrated soft robotic gripper. The result shows significant improvement in state estimation compared to conventional estimation methods. [ABSTRACT FROM AUTHOR]

Soft robots are considered to have infinite degrees of freedom based on their structural compliance, providing high adaptability to the environments, and recent study has focused mostly on advancement of their physical designs for increasing the adaptability. However, interaction itself with the environment has not been taken into serious account in previous studies despite the importance in applications. A soft robot as a hybrid system described by both discrete and continuous states is considered and a method of analysis for enhanced manipulation is proposed. The method is tested with a soft gripper composed of a pneumatic bending actuator and an embedded soft sensor for a task of object gripping. The optimum sensor location on the actuator based on the calibration map obtained from the actuator characterization is first determined. Using the sensor information, the interaction with the environment (i.e., object) classified into four discrete states is understood. In addition, a control strategy to find the best position to grip the object based on the estimated states is developed. The gripper is able to successfully complete the task using the proposed method for three test scenarios with different initial conditions and control parameters. Finally, the results are demonstrated with supporting videos. [ABSTRACT FROM AUTHOR]

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots. [ABSTRACT FROM AUTHOR]

Soft robotics is a rapidly evolving field offering novel solutions in the development of wearable technologies. Soft pneumatic artificial muscles in particular, have seen widespread use in the development of human scale rehabilitative and assistive wearables. However, these soft actuators have not yet been adapted to address the complex dynamic regime of active (essential tremor) and resting (Parkinson's disease) hand tremor, the most common movement disorder affecting humans. Current solutions to address hand tremor involve expensive medication and surgical interventions, as well as wearable assistive devices that fall short of providing an effective compact design for the suppression of hand tremor. This study focuses on the design of a novel lightweight, compact, bending actuator that will be capable of actively suppressing hand tremor when adapted into an assistive wearable device. The proposed fiber-reinforced bending pneumatic artificial muscle (BPAM), including its design specifications, fabrication process, theoretical modeling, and experimental characterization, are detailed. The developed actuator was capable of producing sinusoidal trajectories with peak-to-peak amplitudes of 40° and a bandwidth of 8 Hz, the dynamic regime of pathological hand tremor. The ability of the fiber-reinforced BPAM to act within the dynamic regime of hand tremor demonstrates its potential to be further developed into a system capable of the active suppression of hand tremor. [ABSTRACT FROM AUTHOR]

Soft sensors have been playing a crucial role in detecting different types of physical stimuli to part or the entire body of a robot, analogous to mechanoreceptors or proprioceptors in biology. Most of the currently available soft sensors with compact form factors can detect only a single deformation mode at a time due to the limitation in combining multiple sensing mechanisms in a limited space. However, realizing multiple modalities in a soft sensor without increasing its original form factor is beneficial, because even a single input stimulus to a robot may induce a combination of multiple modes of deformation. Here, we report a multifunctional soft sensor capable of decoupling combined deformation modes of stretching, bending, and compression, as well as detecting individual deformation modes, in a compact form factor. The key enabling design feature of the proposed sensor is a combination of heterogeneous sensing mechanisms: optical, microfluidic, and piezoresistive sensing. We characterize the performance on both detection and decoupling of deformation modes, by implementing both a simple algorithm of threshold evaluation and a machine learning technique based on an artificial neural network. The proposed soft sensor is able to estimate eight different deformation modes with accuracies higher than 95%. We lastly demonstrate the potential of the proposed sensor as a method of human-robot interfaces with several application examples highlighting its multifunctionality. [ABSTRACT FROM AUTHOR]

The performance of inflatable artificial muscles depends greatly on their designs and the output shapes resulting from the geometric constraints. Although there have been attempts to apply physical constraints on the air chamber to achieve larger stroke lengths and increased force–length ratios, it has been difficult to achieve the above two goals while maintaining a compact form factor. In this article, we propose flat inflatable artificial muscles that have large contraction ratios (up to 0.5) and show increased forces in wider ranges of contractions by adding an internal geometric constraint. Addition of an external constraint, such as rigid plates, further increased the maximum contraction ratio (up to 0.553) through a synergistic effect. We show that various force–length relations can be achieved by adjusting the height of the plates. Theoretical models based on the geometry and the principle of virtual work are experimentally validated using actuator prototypes made of heat-sealable plastic sheets. Also, compact capacitive sensors are integrated in design for proprioceptive feedback of the proposed actuators, and their feasibility and effectiveness are experimentally evaluated through closed-loop control. [ABSTRACT FROM AUTHOR]

Soft sensors suffer from high manufacturing tolerances and signal drift from long‐term usage, which degrades their practicality. Although deep learning has recently been proposed to address these issues, it is expensive in terms of data collection and processing. Therefore, an adaptive calibration method is proposed for soft sensors, suitable for mass production and long‐term usage. In addition to maintaining the original benefits of deep learning characterization, this method enables fast and accurate calibration by capturing the change in the characteristics of the sensor through domain adaptation, using optimal transportation. An offline calibration method is first described, which is for alleviating the difficulty in calibrating every single unit from mass produced soft sensors. The main advantage is that identically manufactured soft sensors in a large volume with variations can be calibrated with reduced time and effort for collecting and processing data. Online calibration is then discussed, which compensates for the parameter changes when a soft sensor is continuously used for an extended period of time. For a single sensor, even though the sensor shows signal drift from the long‐term usage, the calibrated network weights can be quickly adjusted online. Finally, the proposed adaptive calibration is experimentally evaluated using actual soft sensors. [ABSTRACT FROM AUTHOR]

Soft and integrated design can enable wearable haptic devices to augment natural human taction. This paper proposes a novel, soft, haptic finger-worn wearable device based on compliant and adhesive silicone skin and lightweight twisted and coiled polymer (TCP) actuators using ultra high molecular weight polyethylene (UHMWPE) fibers to provide lateral skin stretch sensations. Recently, silicone elastomers have been used in wearable sensors and in haptic applications for their high compliance or adhesion. TCP actuators have also demonstrated high power to weight ratios, large stroke length, simple mechanism, and inherent softness. Lateral skin stretch is sensitive to small motions and has been used for intuitive proprioceptive feedback applications. We combined these characteristics to design and manufacture a wearable, functional haptic prototype. Prototype performance was evaluated using an optical tracking system, a force gauge test bench, and compared to vibrotactile haptic feedback in a experiment with 14 healthy participants. Results showed that participant mean reaction times were comparable to those of a vibrotactile feedback system, though task completion times were longer. This paper is the first to employ TCP actuators for haptic stimulation and could serve as a foundation for future applications involving soft wearable haptics in gaming, health, and virtual reality. [ABSTRACT FROM AUTHOR]

Multi-Fingered haptics is imperative for truly immersive virtual reality experience, as many real-world tasks involve finger manipulation. One of the key lacking aspect for this is the absence of technologically and economically viable wearable haptic interfaces that can simultaneously track the finger/hand motions and display multi-degree-of-freedom (DOF) contact forces. In this paper, we propose a novel wearable cutaneous haptic interface (WCHI), which consists of 1) finger tracking modules (FTMs) to estimate complex multi-DOF finger and hand motion; and 2) cutaneous haptic modules (CHMs) to convey three-DOF contact force at the finger-tip. By opportunistically utilizing such different types of sensors as inertial measurement units, force sensitive resistor sensors, and soft sensors, the WCHI can track complex anatomically consistent multi-DOF finger motion while avoiding FTM-CHM electromagnetic interference possibly stemming from their collocation in the small form-factor interface; while also providing the direction and magnitude of three-DOF finger-tip contact force, the feedback of which can significantly enhance the precision of contact force generation against variability among users via their closed-loop control. Human subject study is performed with a virtual peg insertion task to show the importance of both the multi-DOF finger tracking and the three-DOF cutaneous haptic feedback for dexterous manipulation in virtual environment. [ABSTRACT FROM AUTHOR]

Soft sensors are becoming more popular in wearables as a means of tracking human body motions due to their high stretchability and easy wearability. However, previous research not only was limited to only certain body parts, but also showed problems in both calibration and processing of the sensor signals, which are caused by the high nonlinearity and hysteresis of the soft materials and also by the misplacement and displacement of the sensors during motion. Although this problem can be alleviated through redundancy by employing an increased number of sensors, it will lay another burden of heavy processing and power consumption. Moreover, complete full-body motion tracking has not been achieved yet. Therefore, we propose use of deep learning for full-body motion sensing, which significantly increases efficiency in calibration of the soft sensor and estimation of the body motions. The sensing suit is made of stretchable fabric and contains 20 soft strain sensors distributed on both the upper and the lower extremities. Three athletic motions were tested with a human subject, and the proposed learning-based calibration and mapping method showed a higher accuracy than traditional methods that are mainly based on mathematical estimation, such as linear regression. [ABSTRACT FROM AUTHOR]

Each year 35 000 cardiac ablation procedures are performed to treat atrial fibrillation through the use of catheter systems. The success rate of this treatment is highly dependent on the force which the catheter applies on the heart wall. If the magnitude of the applied force is much higher than a certain threshold the tissue perforates, whereas if the force is lower than this threshold the lesion size may be too large and is inconsistent. Furthermore, studies have shown large variability in the applied force from trained physicians during treatment, suggesting that although there might be patient-specific differences, physicians are unable to manually regulate the levels of the force at the site of treatment. Current catheter systems do not provide the physicians with active means for contact force control and are only at most aided by visual feedback of the forces measured in situ. This paper discusses a novel design of a robotic end-effector that integrates mechanisms of sensing and actively controlling of the applied forces into a miniaturized compact form. The required specifications for design and integration were derived from the current application under investigation. An off-the-shelf miniature piezoelectric motor was chosen for actuation, and a force sensing solution was developed to meet the specifications. Experimental characterization of the actuator and the force sensor within the integrated setup show compliance with the specifications and pave the way for future experimentation where closed-loop control of the system can be implemented according to the contact force control strategies for the application. [ABSTRACT FROM PUBLISHER]

Abstract: An electrically responsive composite is introduced that exhibits muscle‐like changes in elastic stiffness (≈1–10 MPa) when stimulated with moderate voltages (5–20 V). The stiffness‐tuning element contains an embedded layer of conductive thermoplastic elastomer (cTPE), composed of a propylene–ethylene copolymer and a percolating network of carbon black. Two opposite surfaces of the cTPE layer are coated with a ≈20 µm thin film eutectic gallium–indium (EGaIn) liquid metal alloy. When a voltage is applied to these EGaIn electrodes, electric current passes through the cTPE. This causes internal Joule heating, which induces a phase transition that changes the composite from its stiff state (E = 10.4 MPa) to its compliant state (E = 0.7 MPa). Differential scanning calorimetry is performed to show that this state change is governed by a solid–liquid transition. Voltage‐dependent activation times are demonstrated that can be reduced to below 2 s and show the ability of the composite to recover its original shape after large strains. To illustrate its applicability in robotics, the composite is incorporated into an underactuated robotic finger, providing it with two different bending modes. The ability to use the composite as a moldable stiffness‐tuning splint is also demonstrated. [ABSTRACT FROM AUTHOR]

Wearable robots based on soft materials will augment mobility and performance of the host without restricting natural kinematics. Such wearable robots will need soft sensors to monitor the movement of the wearer and robot outside the lab. Until now wearable soft sensors have not demonstrated significant mechanical robustness nor been systematically characterized for human motion studies of walking and running. Here, we present the design and systematic characterization of a soft sensing suit for monitoring hip, knee, and ankle sagittal plane joint angles. We used hyper-elastic strain sensors based on microchannels of liquid metal embedded within elastomer, but refined their design with the use of discretized stiffness gradients to improve mechanical durability. We found that these robust sensors could stretch up to 396% of their original lengths, would restrict the wearer by less than 0.17% of any given joint’s torque, had gauge factor sensitivities of greater than 2.2, and exhibited less than 2% change in electromechanical specifications through 1500 cycles of loading–unloading. We also evaluated the accuracy and variability of the soft sensing suit by comparing it with joint angle data obtained through optical motion capture. The sensing suit had root mean square (RMS) errors of less than 5° for a walking speed of 0.89 m/s and reached a maximum RMS error of 15° for a running speed of 2.7 m/s. Despite the deviation of absolute measure, the relative repeatability of the sensing suit’s joint angle measurements were statistically equivalent to that of optical motion capture at all speeds. We anticipate that wearable soft sensing will also have applications beyond wearable robotics, such as in medical diagnostics and in human–computer interaction. [ABSTRACT FROM AUTHOR]

This study demonstrates real-time scan plane control dependent on three-dimensional needle bending, as measured from magnetic resonance imaging (MRI)-compatible optical strain sensors. A biopsy needle with embedded fiber Bragg grating (FBG) sensors to measure surface strains is used to estimate its full 3-D shape and control the imaging plane of an MR scanner in real-time, based on the needle's estimated profile. The needle and scanner coordinate frames are registered to each other via miniature radio-frequency (RF) tracking coils, and the scan planes autonomously track the needle as it is deflected, keeping its tip in view. A 3-D needle annotation is superimposed over MR-images presented in a 3-D environment with the scanner's frame of reference. Scan planes calculated based on the FBG sensors successfully follow the tip of the needle. Experiments using the FBG sensors and RF coils to track the needle shape and location in real-time had an average root mean square error of 4.2 mm when comparing the estimated shape to the needle profile as seen in high resolution MR images. This positional variance is less than the image artifact caused by the needle in high resolution SPGR (spoiled gradient recalled) images. Optical fiber strain sensors can estimate a needle's profile in real-time and be used for MRI scan plane control to potentially enable faster and more accurate physician response. [ABSTRACT FROM AUTHOR]