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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]

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]

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 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]

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]