Advancing Soft Robot Proprioception Through 6D Strain Sensors Embedding

Soft robots and bioinspired systems have revolutionized robot design by incorporating flexibility and deformable materials inspired by nature's ingenious designs. Similar to many robotic applications, sensing and perception are paramount to enable soft robots to adeptly navigate the unpredictable real world, ensuring safe interactions with both humans and the environment. Despite recent progress, soft robot sensorization still faces significant challenges due to the virtual infinite degrees of freedom of the system and the need for efficient computational models capable of estimating valuable information from sensor data. In this article, we present a new model-based proprioceptive system for slender soft robots based on strain sensing and a strain-based modeling approach called Geometric Variable-Strain (GVS). We develop a flexible 2-Plate 6D strain sensor (Flex-2P6D) capable of measuring the 6 dimensions (6D) strain at specific points of the soft robot with an accuracy higher than 95%. Coupled with the GVS approach, the proposed methodology is able to directly measure the configuration variables and reconstruct complex robot shapes with very high accuracy, even in very challenging conditions. The sensors are embedded inside the soft body, which makes them also suitable for underwater operation and physical interaction with the environment. Something that we also demonstrate experimentally. We believe that our approach has the potential to be applied across a wide variety of applications, including observation and exploration missions, as well as human-robot interaction, where the states of the system are required for implementing precise closed-loop control and estimation methods.

Unsupervised Sim-to-Real Adaptation of Soft Robot Proprioception Using a Dual Cross-Modal Autoencoder

Data-driven calibration methods have shown promising results for accurate proprioception in soft robotics. This process can be greatly benefited by adopting numerical simulation for computational efficiency. However, the gap between the simulated and real domains limits the accurate, generalized application of the approach. Herein, we propose an unsupervised domain adaptation framework as a data-efficient, generalized alignment of these heterogeneous sensor domains. A dual cross-modal autoencoder was designed to match the sensor domains at a feature level without any extensive labeling process, facilitating the computationally efficient transferability to various tasks. Moreover, our framework integrates domain adaptation with anomaly detection, which endows robots with the capability for external collision detection. As a proof-of-concept, the methodology was adopted for the famous soft robot design, a multigait soft robot, and two fundamental perception tasks for autonomous robot operation, involving high-fidelity shape estimation and collision detection. The resulting perception demonstrates the digital-twinned calibration process in both the simulated and real domains. The proposed design outperforms the existing prevalent benchmarks for both perception tasks. This unsupervised framework envisions a new approach to imparting embodied intelligence to soft robotic systems via blending simulation.

Shape Reconstruction of Extensible Continuum Manipulator Based on Soft Sensors

Continuum manipulators can improve spatial adaptability and operational flexibility in constrained environments by endowing them with contraction and extension capabilities. There are currently desired requirements to quantify the shape of an extensible continuum manipulator for strengthening its obstacle avoidance capability and end-effector position accuracy. To address these issues, this study proposes a methodology of using silicone rubber strain sensors (SRSS) to estimate the shape of an extensible continuum manipulator. The way is to measure the strain at specific locations on the deformable body of the manipulator, and then reconstruct the shape by integrating the information from all sensors. The slender sensors are fabricated by a rolling process that transforms planar silicone rubber sensors into cylindrical structures. The proprioceptive model relationship between the strain of the sensor and the deformation of the manipulator is established with considering the phenomenon of torsion of the manipulator caused by compression. The physically extensible continuum manipulator equipped with three driving tendons and nine SRSS was designed. Comprehensive evaluations of various motion trajectories indicate that this method can accurately reconstruct the shape of the manipulator, especially under end-effector loads. The experimental results demonstrate that the mean (maximum) absolute position error of the endpoint is 1.61% (3.45%) of the manipulator length.

Parallel Farby-Perot Interferometers in an Etched Multicore Fiber for Vector Bending Measurements

Vector bending sensors can be utilized to detect the bending curvature and direction, which is essential for various applications such as structural health monitoring, mechanical deformation measurement, and shape sensing. In this work, we demonstrate a temperature-insensitive vector bending sensor via parallel Farby-Perot interferometers (FPIs) fabricated by etching and splicing a multicore fiber (MCF). The parallel FPIs made in this simple and effective way exhibit significant interferometric visibility with a fringe contrast over 20 dB in the reflection spectra, which is 6 dB larger than the previous MCF-based FPIs. And such a device exhibits a curvature sensitivity of 0.207 nm/m-1 with strong bending-direction discrimination. The curvature magnitude and orientation angle can be reconstructed through the dip wavelength shifts in two off-diagonal outer-core FPIs. The reconstruction results of nine randomly selected pairs of bending magnitudes and directions show that the average relative error of magnitude is ~4.5%, and the average absolute error of orientation angle is less than 2.0°. Furthermore, the proposed bending sensor is temperature-insensitive, with temperature at a lower sensitivity than 10 pm/°C. The fabrication simplicity, high interferometric visibility, compactness, and temperature insensitivity of the device may accelerate MCF-based FPI applications.

Wearable and Implantable Soft Robots

Soft robotics presents innovative solutions across different scales. The flexibility and mechanical characteristics of soft robots make them particularly appealing for wearable and implantable applications. The scale and level of invasiveness required for soft robots depend on the extent of human interaction. This review provides a comprehensive overview of wearable and implantable soft robots, including applications in rehabilitation, assistance, organ simulation, surgical tools, and therapy. We discuss challenges such as the complexity of fabrication processes, the integration of responsive materials, and the need for robust control strategies, while focusing on advances in materials, actuation and sensing mechanisms, and fabrication techniques. Finally, we discuss the future outlook, highlighting key challenges and proposing potential solutions.

Fatigue-Resistant Mechanoresponsive Color-Changing Hydrogels for Vision-Based Tactile Robots

Mechanoresponsive color-changing materials that can reversibly and resiliently change color in response to mechanical deformation are highly desirable for diverse modern technologies in optics, sensors, and robots; however, such materials are rarely achieved. Here, a fatigue-resistant mechanoresponsive color-changing hydrogel (FMCH) is reported that exhibits reversible, resilient, and predictable color changes under mechanical stress. At its undeformed state, the FMCH remains dark under a circular polariscope; upon uniaxial stretching of up to six times its initial length, it gradually shifts its color from black, to gray, yellow, and purple. Unlike traditional mechanoresponsive color-changing materials, FMCH maintains its performance across various strain rates for up to 10 000 cycles. Moreover, FMCH demonstrates superior mechanical properties with fracture toughness of 3000 J m-2, stretchability of 6, and fatigue threshold up to 400 J m-2. These exceptional mechanical and optical features are attributed to FMCH's substantial molecular entanglements and desirable hygroscopic salts, which synergistically enhance its mechanical toughness while preserving its color-changing performance. One application of this FMCH as a tactile sensoris then demonstrated for vision-based tactile robots, enabling them to discern material stiffness, object shape, spatial location, and applied pressure by translating stress distribution on the contact surface into discernible images.

Multi-Degree-of-Freedom Force Sensor Incorporated into Soft Robotic Gripper for Improved Grasping Stability

In recent years, soft robotic grippers have emerged as a promising solution for versatile and safe manipulation of objects in various fields. However, precise force control is critical, especially when handling delicate or fragile objects, to avoid excessive grip force application or to prevent object slippage. Herein, we propose a novel three-degree-of-freedom force sensor incorporated within a soft robotic gripper to realize stable grasping with force feedback. The proposed optical sensor employs lightweight and compact optical fibers, thereby allowing for cost-effective fabrication, and a robust sensing system that is immune to electromagnetic fields. By innervating the soft gripper with optical fibers, a durable system is achieved with the fibers functioning as a strengthening layer, thereby eliminating the need for embedding an external stiffening structure for efficient bending actuation. The innovative contact-based light loss sensing mechanism allows for a robust and stable sensing mechanism with low drift (<0.1% over 9000 cycles) that can be applied to soft pneumatic bending grippers. We used the developed sensor-incorporated soft gripper to grasp various objects, including magnetic materials, and achieved slip detection along with grip force feedback without any signal interference. Overall, this study proposes a robust measuring multi-degree-of-freedom force sensor that can be incorporated into grippers for improved grasping stability.

Advanced Design of Soft Robots with Artificial Intelligence

A comprehensive review focused on the whole systems of the soft robotics with artificial intelligence, which can feel, think, react and interact with humans, is presented. The design strategies concerning about various aspects of the soft robotics, like component materials, device structures, prepared technologies, integrated method, and potential applications, are summarized. A broad outlook on the future considerations for the soft robots is proposed.

In recent years, breakthrough has been made in the field of artificial intelligence (AI), which has also revolutionized the industry of robotics. Soft robots featured with high-level safety, less weight, lower power consumption have always been one of the research hotspots. Recently, multifunctional sensors for perception of soft robotics have been rapidly developed, while more algorithms and models of machine learning with high accuracy have been optimized and proposed. Designs of soft robots with AI have also been advanced ranging from multimodal sensing, human–machine interaction to effective actuation in robotic systems. Nonetheless, comprehensive reviews concerning the new developments and strategies for the ingenious design of the soft robotic systems equipped with AI are rare. Here, the new development is systematically reviewed in the field of soft robots with AI. First, background and mechanisms of soft robotic systems are briefed, after which development focused on how to endow the soft robots with AI, including the aspects of feeling, thought and reaction, is illustrated. Next, applications of soft robots with AI are systematically summarized and discussed together with advanced strategies proposed for performance enhancement. Design thoughts for future intelligent soft robotics are pointed out. Finally, some perspectives are put forward.

Somatosensory Electro-Thermal Actuator through the Laser-Induced Graphene Technology

The biological system realizes the unity of action and perception through the muscle tissue and nervous system. Correspondingly, artificial soft actuators realize the unity of sensing and actuating functions in a single functional material, which will have tremendous potential for developing intelligent and bionic soft robotics. This paper reports the design of a laser-induced graphene (LIG) electrothermal actuator with self-sensing capability. LIG, a functional material formed by a one-step direct-write lasing procedure under ambient air, is used as electrothermal conversion materials and piezoresistive sensing materials. By transferring LIG to a flexible silicone substrate, the design ability of the LIG-based actuator unit is enriched, along with an effectively improved sensing sensitivity. Through the integration of different types of well-designed LIG-based actuator units, the transformations from multidimensional precursors to 2D and 3D structures are realized. According to the piezoresistive effect of the LIG units during the deformation process, the visual synchronous deformation state feedback of the LIG-based actuator is proposed. The multimodal crawling soft robotics and the switchable electromagnetic shielding cloak serve as the demonstrations of the self-sensing LIG-based actuator, showing the advantage of the design in remote control of the soft robot without relying on the assistance of visual devices.

Soft Polymer Optical Fiber Sensors for Intelligent Recognition of Elastomer Deformations and Wearable Applications

In recent years, soft robotic sensors have rapidly advanced to endow robots with the ability to interact with the external environment. Here, we propose a polymer optical fiber (POF) sensor with sensitive and stable detection performance for strain, bending, twisting, and pressing. Thus, we can map the real-time output light intensity of POF sensors to the spatial morphology of the elastomer. By leveraging the intrinsic correlations of neighboring sensors and machine learning algorithms, we realize the spatially resolved detection of the pressing and multi-dimensional deformation of elastomers. Specifically, the developed intelligent sensing system can effectively recognize the two-dimensional indentation position with a prediction accuracy as large as ~99.17%. The average prediction accuracy of combined strain and twist is ~98.4% using the random forest algorithm. In addition, we demonstrate an integrated intelligent glove for the recognition of hand gestures with a high recognition accuracy of 99.38%. Our work holds promise for applications in soft robots for interactive tasks in complex environments, providing robots with multidimensional proprioceptive perception. And it also can be applied in smart wearable sensing, human prosthetics, and human-machine interaction interfaces.

Bioinspired Stimuli-Responsive Materials for Soft Actuators

Biological species can walk, swim, fly, jump, and climb with fast response speeds and motion complexity. These remarkable functions are accomplished by means of soft actuation organisms, which are commonly composed of muscle tissue systems. To achieve the creation of their biomimetic artificial counterparts, various biomimetic stimuli-responsive materials have been synthesized and developed in recent decades. They can respond to various external stimuli in the form of structural or morphological transformations by actively or passively converting input energy into mechanical energy. They are the core element of soft actuators for typical smart devices like soft robots, artificial muscles, intelligent sensors and nanogenerators. Significant progress has been made in the development of bioinspired stimuli-responsive materials. However, these materials have not been comprehensively summarized with specific actuation mechanisms in the literature. In this review, we will discuss recent advances in biomimetic stimuli-responsive materials that are instrumental for soft actuators. Firstly, different stimuli-responsive principles for soft actuators are discussed, including fluidic, electrical, thermal, magnetic, light, and chemical stimuli. We further summarize the state-of-the-art stimuli-responsive materials for soft actuators and explore the advantages and disadvantages of using electroactive polymers, magnetic soft composites, photo-thermal responsive polymers, shape memory alloys and other responsive soft materials. Finally, we provide a critical outlook on the field of stimuli-responsive soft actuators and emphasize the challenges in the process of their implementation to various industries.

Soft Robotic Finger with Energy-Coupled Quadrastability

The performance of the human finger is a significant inspiration for designing soft robotic fingers that can achieve high speed and high force or perform delicate and complex tasks. Existing soft grippers and actuators can be excellent in specific capabilities. However, it is still challenging for them to meet an all-around performance as the human finger, characterized by high actuation speed, wide grasping range, sensing ability, and gentle and high-load grasping capability. The proposed tendon pulley quadrastable (TPQ) finger has combined these qualities in the conducted gripping tasks. A pair of elastic tendons is utilized as the sole energy reservoir to create a novel energy distribution pattern: energy-coupled quadrastability. An energy model is built to analyze and predict the behaviors of the TPQ finger. Mechanical instability is utilized to enhance the actuation speed. The proposed soft lever mechanism endows the TPQ finger with sensing ability. The energy barrier adjusting plates control the energy barrier, adjusting the sensitivity of both active and passive actuation mechanisms. The transition of four stable states forms preplanned trajectories that are applied to create multiple grasping manners. Experiments show that it can respond to stimuli and finish a grasping task in merely 31 ms, and its payload can reach 33.25 kg. At the same time, it can also handle fragile objects such as a piece of rose and grasp a wide range of objects ranging from a thin nut (3.3 mm in height) or a thin card (0.76 mm thick) to a football (220 mm).

High-stretchability and low-hysteresis strain sensors using origami-inspired 3D mesostructures

Stretchable strain sensors are essential for various applications such as wearable electronics, prosthetics, and soft robotics. Strain sensors with high strain range, minimal hysteresis, and fast response speed are highly desirable for accurate measurements of large and dynamic deformations of soft bodies. Current stretchable strain sensors mostly rely on deformable conducting materials, which often have difficulties in achieving these properties simultaneously. In this study, we introduce capacitive strain sensor concepts based on origami-inspired three-dimensional mesoscale electrodes formed by a mechanically guided assembly process. These sensors exhibit up to 200% stretchability with 1.2% degree of hysteresis, <22 ms response time, small sensing area (~5 mm2), and directional strain responses. To showcase potential applications, we demonstrate the use of distributed strain sensors for measuring multimodal deformations of a soft continuum arm.

Soft Robot Proprioception Using Unified Soft Body Encoding and Recurrent Neural Network

Compared with rigid robots, soft robots are inherently compliant and have advantages in the tasks requiring flexibility and safety. But sensing the high dimensional body deformation of soft robots is a challenge. Encasing soft strain sensors into the internal body of soft robots is the most popular solution to address this challenge. But most of them usually suffer from problems like nonlinearity, hysteresis, and fabrication complexity. To endow the soft robots with body movement awareness, this work presents a bioinspired architecture by taking cues from human proprioception system. Differing from the popular usage of smart material-based sensors embedded in soft actuators, we created a synthetic analog to the human muscle system, using paralleled soft pneumatic chambers to serve as receptors for sensing body deformation. We proposed to build the system with redundant receptors and explored deep learning tools for generating the kinematic model. Based on the proposed methodology, we demonstrated the design of three degrees of freedom continuum joint and how its kinematic model was learned from the unified pressure information of the actuators and receptors. In addition, we investigated the response of the soft system to receptor failures and presented both hardware and software level solutions for achieving graceful degradation. This approach offers an alternative to enable soft robots with proprioception capability, which will be useful for closed-loop control and interaction with environment.

Passively addressed robotic morphing surface (PARMS) based on machine learning

Reconfigurable morphing surfaces provide new opportunities for advanced human-machine interfaces and bio-inspired robotics. Morphing into arbitrary surfaces on demand requires a device with a sufficiently large number of actuators and an inverse control strategy. Developing compact, efficient control interfaces and algorithms is vital for broader adoption. In this work, we describe a passively addressed robotic morphing surface (PARMS) composed of matrix-arranged ionic actuators. To reduce the complexity of the physical control interface, we introduce passive matrix addressing. Matrix addressing allows the control of N2 independent actuators using only 2N control inputs, which is substantially lower than traditional direct addressing (N2 control inputs). Using machine learning with finite element simulations for training, our control algorithm enables real-time, high-precision forward and inverse control, allowing PARMS to dynamically morph into arbitrary achievable predefined surfaces on demand. These innovations may enable the future implementation of PARMS in wearables, haptics, and augmented reality/virtual reality.

Machine-Learning Assisted Electronic Skins Capable of Proprioception and Exteroception in Soft Robotics

Inspired by natural biological systems, soft robots have recently been developed, showing tremendous potential in real-world applications because of their intrinsic softness and deformability. The confluence of electronic skins and machine learning is extensively studied to create effective biomimetic robotic systems. Based on a differential piezoelectric matrix, this study presents a shape-sensing electronic skin (SSES) that can recognize surface conformations with minimal interference from pressing, stretching, or other surrounding stimuli. It is then integrated with soft robots to reconstruct their shape during movement, serving as a proprioceptive sense. Additionally, the robot can utilize machine learning to identify various terrains, demonstrating exteroception and pointing toward more advanced autonomous robots capable of performing real-world tasks in challenging environments.

SoftSAR: The New Softer Side of Socially Assistive Robots-Soft Robotics with Social Human-Robot Interaction Skills

When we think of "soft" in terms of socially assistive robots (SARs), it is mainly in reference to the soft outer shells of these robots, ranging from robotic teddy bears to furry robot pets. However, soft robotics is a promising field that has not yet been leveraged by SAR design. Soft robotics is the incorporation of smart materials to achieve biomimetic motions, active deformations, and responsive sensing. By utilizing these distinctive characteristics, a new type of SAR can be developed that has the potential to be safer to interact with, more flexible, and uniquely uses novel interaction modes (colors/shapes) to engage in a heighted human-robot interaction. In this perspective article, we coin this new collaborative research area as SoftSAR. We provide extensive discussions on just how soft robotics can be utilized to positively impact SARs, from their actuation mechanisms to the sensory designs, and how valuable they will be in informing future SAR design and applications. With extensive discussions on the fundamental mechanisms of soft robotic technologies, we outline a number of key SAR research areas that can benefit from using unique soft robotic mechanisms, which will result in the creation of the new field of SoftSAR.

Finger-palm synergistic soft gripper for dynamic capture via energy harvesting and dissipation

Robotic grippers, inspired by human hands, show an extraordinary ability to manipulate objects of various shapes, sizes, or materials. However, capturing objects with varying kinetic energy remains challenging, regardless of the classical rigid-bodied or frontier soft-bodied grippers. Here, we demonstrate a rapid energy harvesting and dissipation mechanism for the soft grippers leveraging the finger-palm synergy. Theoretically and experimentally, this mechanism enables a soft gripper to reliably capture high-speed targets by dissipating and harvesting almost all the target's kinetic energy within 30 milliseconds. The energy harvesting and dissipating capability are adjustable and can be enhanced by inflating pressure. Additionally, the harvested energy is autonomously transferred into fingers to enhance their grasping force and reduce the response time. To highlight, the grippers we developed are integrated into a six-rotor drone and successfully capture flying objects in an outdoor experiment. These results significantly advance robotics development in achieving dynamic capture of dynamic targets.

A Learning-Based Approach to Sensorize Soft Robots

Soft actuators and their sensors have always been separate entities with two distinct roles. The omnidirectional compliance of soft robots thus means that multiple sensors have to be used to sense different modalities in the respective planes of motion. With the recent emergence of self-sensing actuators, the two roles have gradually converged to simplify sensing requirements. Self-sensing typically involves embedding a conductive sensing element into the soft actuator and provides multiple state information along the continuum. However, most of these self-sensing actuators are fabricated through manual methods, which results in inconsistent sensing performance. Soft material compliance also imply that both actuator and sensor exhibit nonlinear behaviors during actuation, making sensing more complex. In this regard, machine learning has shown promise in characterizing the nonlinear behavior of soft sensors. Beyond characterization, we show that applying machine learning to soft actuators eliminates the need to implant a sensing element to achieve self-sensing. Fabrication is done using 3D printing, thus ensuring that sensing performance is consistent across the actuators. In addition, our proposed technique is able to estimate the bending curvature of a soft continuum actuator and the external forces applied to the tip of the actuator in real time. Our methodology is generalizable and aims to provide a novel way of multimodal sensing for soft robots across a variety of applications.

Fabrication and Functionality Integration Technologies for Small-Scale Soft Robots

Small-scale soft robots are attracting increasing interest for visible and potential applications owing to their safety and tolerance resulting from their intrinsic soft bodies or compliant structures. However, it is not sufficient that the soft bodies merely provide support or system protection. More importantly, to meet the increasing demands of controllable operation and real-time feedback in unstructured/complicated scenarios, these robots are required to perform simplex and multimodal functionalities for sensing, communicating, and interacting with external environments during large or dynamic deformation with the risk of mismatch or delamination. Challenges are encountered during fabrication and integration, including the selection and fabrication of composite/materials and structures, integration of active/passive functional modules with robust interfaces, particularly with highly deformable soft/stretchable bodies. Here, methods and strategies of fabricating structural soft bodies and integrating them with functional modules for developing small-scale soft robots are investigated. Utilizing templating, 3D printing, transfer printing, and swelling, small-scale soft robots can be endowed with several perceptual capabilities corresponding to diverse stimulus, such as light, heat, magnetism, and force. The integration of sensing and functionalities effectively enhances the agility, adaptability, and universality of soft robots when applied in various fields, including smart manufacturing, medical surgery, biomimetics, and other interdisciplinary sciences.

A Sea-Anemone-Inspired, Multifunctional, Bistable Gripper

The growing need for soft robots with secure, adaptive, and autonomous functioning in unforeseen environments favors designs with multiple functionalities. This has driven soft robotic grippers to be explored to integrate perceptual capability for augmented multifunctionality. In nature, sea anemones can detect and catch preys of various shapes and sizes effectively with extremely simple bodies because of the efficient coupling of sensing and actuation capability. Inspired by their body structures, we present a bistable gripper with multifunctionality that includes sensing (proprioceptive and exteroceptive) and multimodal gripping (grasping and pinching). The gripper exploits an array of tapered pins on the external surface of a dome membrane for gripping and a set of cylindrical markers on the internal surface of the membrane for optical sensing. The membrane is bistable and can settle in either of two equilibrium states "natural" and "retracted." Gripping functionality is achieved by the centripetal enveloping movement of the pins, along with the passive snap-through process of the membrane. By analyzing the distribution of markers within the view of an embedded camera, sophisticated sensing functionality can be achieved. We first characterized each function separately and then implemented an object handling system, combining the sensing and gripping functionality, to demonstrate the potential for more advanced robotic applications. This work delivers a compact universal gripper design with an efficient and elegant integration of multifunctionality.

Multicomponent and multifunctional integrated miniature soft robots

Miniature soft robots with elaborate structures and programmable physical properties could conduct micromanipulation with high precision as well as access confined and tortuous spaces, which promise benefits in medical tasks and environmental monitoring. To improve the functionalities and adaptability of miniature soft robots, a variety of integrated design and fabrication strategies have been proposed for the development of miniaturized soft robotic systems integrated with multicomponents and multifunctionalities. Combining the latest advancement in fabrication technologies, intelligent materials and active control methods enable these integrated robotic systems to adapt to increasingly complex application scenarios including precision medicine, intelligent electronics, and environmental and proprioceptive sensing. Herein, this review delivers an overview of various integration strategies applicable for miniature soft robotic systems, including semiconductor and microelectronic techniques, modular assembly based on self-healing and welding, modular assembly based on bonding agents, laser machining techniques, template assisted methods with modular material design, and 3D printing techniques. Emerging applications of the integrated miniature soft robots and perspectives for the future design of small-scale intelligent robots are discussed.

Learning to sense three-dimensional shape deformation of a single multimode fiber

Optical fiber bending, deformation or shape sensing are important measurement technologies and have been widely deployed in various applications including healthcare, structural monitoring and robotics. However, existing optical fiber bending sensors require complex sensor structures and interrogation systems. Here, inspired by the recent renewed interest in information-rich multimode optical fibers, we show that the multimode fiber (MMF) output speckles contain the three-dimensional (3D) geometric shape information of the MMF itself. We demonstrate proof-of-concept 3D multi-point deformation sensing via a single multimode fiber by using k-nearest neighbor (KNN) machine learning algorithm, and achieve a classification accuracy close to 100%. Our results show that a single MMF based deformation sensor is excellent in terms of system simplicity, resolution and sensitivity, and can be a promising candidate in deformation monitoring or shape-sensing applications.

Bending Sensor Based on Controlled Microcracking Regions for Application toward Wearable Electronics and Robotics

A soft bending sensor based on the inverse pyramid structure is demonstrated, revealing that it can effectively suppress microcrack formation in designated regions, thus allowing the cracks to open gradually with bending in a controlled manner. Such a feature enabled the bending sensor to simultaneously have a wide dynamic range of bending strain (0.025-5.4%), high gauge factor (∼74), and high linearity (R² ∼ 0.99). Furthermore, the bending sensor can capture repeated instantaneous changes in strain and various types of vibrations, owing to its fast response time. Moreover, the bending direction can be differentiated with a single layer of the sensor, and using an array of sensors integrated on a glove, object recognition was demonstrated via machine learning. Finally, a self-monitoring proprioceptive ionic electroactive polymer (IEAP) actuator capable of operating in liquid was demonstrated. Such features of our bending sensor will enable a simple and effective way of detecting sophisticated motion, thus potentially advancing wearable healthcare monitoring electronics and enabling proprioceptive soft robotics.

Integrated stretchable pneumatic strain gauges for electronics-free soft robots

In soft robotics, actuators, logic and power systems can be entirely fluidic and electronics-free. However, sensors still typically rely on electric or optical principles. This adds complexity to fluidic soft robots because transducers are needed, and electrical materials have to be incorporated. Herein, we show a highly-stretchable pneumatic strain gauge based on a meandering microchannel in a soft elastomer material thus eliminating the need for an electrical signal in soft robots. Using such pneumatic sensors, we demonstrate an all-pneumatic gripper with integrated pneumatic strain gauges that is capable of autonomous closure and object recognition. The gauges can measure at least up to 300% engineering strains. The sensor exhibits a very stable signal over a 12-hour measurement period with no hysteresis. Using pneumatic sensors, all four major components of robots—actuators, logic, power, and sensors—can be pneumatic, enabling all-fluidic soft robots with proprioception and exteroception.

Anastasia Koivikko and colleagues report pneumatic strain gauges integrated into a soft robotic gripper which can recognize objects and close autonomously. The approach enables entirely fluidic robotic systems with no need for electrical or optical sensors.

Haptic perception using optoelectronic robotic flesh for embodied artificially intelligent agents

Flesh encodes a variety of haptic information including deformation, temperature, vibration, and damage stimuli using a multisensory array of mechanoreceptors distributed on the surface of the human body. Currently, soft sensors are capable of detecting some haptic stimuli, but whole-body multimodal perception at scales similar to a human adult (surface area ~17,000 square centimeters) is still a challenge in artificially intelligent agents due to the lack of encoding. This encoding is needed to reduce the wiring required to send the vast amount of information transmitted to the processor. We created a robotic flesh that could be further developed for use in these agents. This engineered flesh is an optical, elastomeric matrix "innervated" with stretchable lightguides that encodes haptic stimuli into light: temperature into wavelength due to thermochromic dyes and forces into intensity due to mechanical deformation. By exploiting the optical properties of the constitutive materials and using machine learning, we infer spatiotemporal, haptic information from light that is read by an image sensor. We demonstrate the capabilities of our system in various assemblies to estimate temperature, contact location, normal and shear force, gestures, and damage from temporal snapshots of light coming from the entire haptic sensor with errors <5%.

A biomimetic elastomeric robot skin using electrical impedance and acoustic tomography for tactile sensing

Human skin perceives physical stimuli applied to the body and mitigates the risk of physical interaction through its soft and resilient mechanical properties. Social robots would benefit from whole-body robotic skin (or tactile sensors) resembling human skin in realizing a safe, intuitive, and contact-rich human-robot interaction. However, existing soft tactile sensors show several drawbacks (complex structure, poor scalability, and fragility), which limit their application in whole-body robotic skin. Here, we introduce biomimetic robotic skin based on hydrogel-elastomer hybrids and tomographic imaging. The developed skin consists of a tough hydrogel and a silicone elastomer forming a skin-inspired multilayer structure, achieving sufficient softness and resilience for protection. The sensor structure can also be easily repaired with adhesives even after severe damage (incision). For multimodal tactile sensation, electrodes and microphones are deployed in the sensor structure to measure local resistance changes and vibration due to touch. The ionic hydrogel layer is deformed owing to an external force, and the resulting local conductivity changes are measured via electrodes. The microphones also detect the vibration generated from touch to determine the location and type of dynamic tactile stimuli. The measurement data are then converted into multimodal tactile information through tomographic imaging and deep neural networks. We further implement a sensorized cosmetic prosthesis, demonstrating that our design could be used to implement deformable or complex-shaped robotic skin.

Shape-programmable, deformation-locking, and self-sensing artificial muscle based on liquid crystal elastomer and low-melting point alloy

An artificial muscle capable of shape programmability, deformation-locking capacity without needing continuous external energy, and self-sensing capability is highly desirable yet challenging in applications of reconfigurable antenna, deployable space structures, etc. Inspired by coupled behavior of the muscles, bones, and nerve system of mammals, a multifunctional artificial muscle based on polydopamine-coated liquid crystal elastomer (LCE) and low-melting point alloy (LMPA) in the form of a concentric tube/rod is proposed. Thereinto, the outer LCE is used for reversible contraction and recovery (i.e., muscle function); the inner LMPA in the resolidification state is adopted for deformation locking, and that in the melt state is adopted for angle variation monitoring by detecting resistance change (i.e., bones and nerve functions, respectively). The proposed artificial muscle demonstrates multiple performances, including controllable bending angle, position, and direction; deformation locking for supporting heavy objects; and real-time monitoring of angle variation, which also provides a straightforward and effective approach for designing soft devices.

A Scientometric Review of Soft Robotics: Intellectual Structures and Emerging Trends Analysis (2010–2021)

Within the last decade, soft robotics has attracted an increasing attention from both academia and industry. Although multiple literature reviews of the whole soft robotics field have been conducted, there still appears to be a lack of systematic investigation of the intellectual structure and evolution of this field considering the increasing amount of publications. This paper conducts a scientometric review of the progressively synthesized network derived from 10,504 bibliographic records using a topic search on soft robotics from 2010 to 2021 based on the Web of Science (WoS) core database. The results are presented from both the general data analysis of included papers (e.g., relevant journals, citation, h-index, year, institution, country, disciplines) and the specific data analysis corresponding to main disciplines and topics, and more importantly, emerging trends. CiteSpace, a data visualization software, which can construct the co-citation network maps and provide citation bursts, is used to explore the intellectual structures and emerging trends of the soft robotics field. In addition, this paper offers a demonstration of an effective analytical method for evaluating enormous publication citation and co-citation data. Findings of this review can be used as a reference for future research in soft robotics and relevant topics.

Bio-Inspired Mechano-Sensor Based on the Deformation of Slit Wake

Internal mechano-sensors, as an indispensable part of the proprioceptive system of intelligent equipment, have attracted enormous research interest because of their extremely crucial role in monitoring machining processes, real-time diagnosis of equipment faults, adaptive motor control and so on. The mechano-sensory structure with signal-transduction function is an important factor in determining the sensing performance of a mechano-sensor. However, contrary to the wide application of the cantilever beam as the sensory structure of external mechano-sensors in order to guarantee their exteroceptive ability, there is still a lack of an effective and widely used sensory structure to significantly improve the sensing performance of internal mechano-sensors. Here, inspired by the scorpion using the specialized slit as the sensory structure of internal mechano-sensilla, the slit is ingeniously used in the design of the engineered internal mechano-sensor. In order to improve the deformability of the slit wake, the hollowed-out design around the slit tail of biological mechano-sensilla is researched. Meanwhile, to mimic the easily deformed flexible cuticular membrane covering the slit, the ultrathin, flexible, crack-based strain sensor is used as the sensing element to cover the controllable slit wake. Based on the coupling deformation of the slit wake, as well as the flexible strain sensor, the slit-based mechano-sensor shows excellent sensing performance to various mechanical signals such as displacement and vibration signals.

Framework for Armature-Based 3D Shape Reconstruction of Sensorized Soft Robots in eXtended Reality

Soft robots are typically intended to operate in highly unpredictable and unstructured environments. Although their soft bodies help them to passively conform to their environment, the execution of specific tasks within such environments often requires the help of an operator that supervises the interaction between the robot and its environment and adjusts the actuation inputs in order to successfully execute the task. However, direct observation of the soft robot is often impeded by the environment in which it operates. Therefore, the operator has to depend on a real-time simulation of the soft robot based on the signals from proprioceptive sensors. However, the complicated three-dimensional (3D) configurations of the soft robot can be difficult to interpret using traditional visualization techniques. In this work, we present an open-source framework for real-time 3D reconstruction of soft robots in eXtended Reality (Augmented and Virtual Reality), based on signals from their proprioceptive sensors. This framework has a Robot Operating System (ROS) backbone, allowing for easy integration with existing soft robot control algorithms for intuitive and real-time teleoperation. This approach is demonstrated in Augmented Reality using a Microsoft Hololens device and runs at up to 60 FPS. We explore the influence that system parameters such as mesh density and armature complexity have on the reconstruction's key performance metrics (i.e., speed, scalability). The open-source framework is expected to function as a platform for future research and developments on real-time remote control of soft robots operating in environments that impede direct observation of the robot.

Flexible Electronics and Devices as Human-Machine Interfaces for Medical Robotics

Medical robots are invaluable players in non-pharmaceutical treatment of disabilities. Particularly, using prosthetic and rehabilitation devices with human-machine interfaces can greatly improve the quality of life for impaired patients. In recent years, flexible electronic interfaces and soft robotics have attracted tremendous attention in this field due to their high biocompatibility, functionality, conformability, and low-cost. Flexible human-machine interfaces on soft robotics will make a promising alternative to conventional rigid devices, which can potentially revolutionize the paradigm and future direction of medical robotics in terms of rehabilitation feedback and user experience. In this review, the fundamental components of the materials, structures, and mechanisms in flexible human-machine interfaces are summarized by recent and renowned applications in five primary areas: physical and chemical sensing, physiological recording, information processing and communication, soft robotic actuation, and feedback stimulation. This review further concludes by discussing the outlook and current challenges of these technologies as a human-machine interface in medical robotics.

Soft actuators for real-world applications

Inspired by physically adaptive, agile, reconfigurable and multifunctional soft-bodied animals and human muscles, soft actuators have been developed for a variety of applications, including soft grippers, artificial muscles, wearables, haptic devices and medical devices. However, the complex performance of biological systems cannot yet be fully replicated in synthetic designs. In this Review, we discuss new materials and structural designs for the engineering of soft actuators with physical intelligence and advanced properties, such as adaptability, multimodal locomotion, self-healing and multi-responsiveness. We examine how performance can be improved and multifunctionality implemented by using programmable soft materials, and highlight important real-world applications of soft actuators. Finally, we discuss the challenges and opportunities for next-generation soft actuators, including physical intelligence, adaptability, manufacturing scalability and reproducibility, extended lifetime and end-of-life strategies.

A Shift from Efficiency to Adaptability: Recent Progress in Biomimetic Interactive Soft Robotics in Wet Environments

Research field of soft robotics develops exponentially since it opens up many imaginations, such as human-interactive robot, wearable robots, and transformable robots in unpredictable environments. Wet environments such as sea and in vivo represent dynamic and unstructured environments that adaptive soft robots can reach their potentials. Recent progresses in soft hybridized robotics performing tasks underwater herald a diversity of interactive soft robotics in wet environments. Here, the development of soft robots in wet environments is reviewed. The authors recapitulate biomimetic inspirations, recent advances in soft matter materials, representative fabrication techniques, system integration, and exemplary functions for underwater soft robots. The authors consider the key challenges the field faces in engineering material, software, and hardware that can bring highly intelligent soft robots into real world.

Towards enduring autonomous robots via embodied energy

Autonomous robots comprise actuation, energy, sensory and control systems built from materials and structures that are not necessarily designed and integrated for multifunctionality. Yet, animals and other organisms that robots strive to emulate contain highly sophisticated and interconnected systems at all organizational levels, which allow multiple functions to be performed simultaneously. Herein, we examine how system integration and multifunctionality in nature inspires a new paradigm for autonomous robots that we call Embodied Energy. Whereas most untethered robots use batteries to store energy and power their operation, recent advancements in energy-storage techniques enable chemical or electrical energy sources to be embodied directly within the structures and materials used to create robots, rather than requiring separate battery packs. This perspective highlights emerging examples of Embodied Energy in the context of developing autonomous robots.

Fusing Dexterity and Perception for Soft Robot-Assisted Minimally Invasive Surgery: What We Learnt from STIFF-FLOP

In recent years we have seen tremendous progress in the development of robotic solutions for minimally invasive surgery (MIS). Indeed, a number of robot-assisted MIS systems have been developed to product level and are now well-established clinical tools; Intuitive Surgical’s very successful da Vinci Surgical System a prime example. The majority of these surgical systems are based on the traditional rigid-component robot design that was instrumental in the third industrial revolution—especially within the manufacturing sector. However, the use of this approach for surgical procedures on or around soft tissue has come under increasing criticism. The dangers of operating with a robot made from rigid components both near and within a patient are considerable. The EU project STIFF-FLOP, arguably the first large-scale research programme on soft robots for MIS, signalled the start of a concerted effort among researchers to investigate this area more comprehensively. While soft robots have many advantages over their rigid-component counterparts, among them high compliance and increased dexterity, they also bring their own specific challenges when interacting with the environment, such as the need to integrate sensors (which also need to be soft) that can determine the robot’s position and orientation (pose). In this study, the challenges of sensor integration are explored, while keeping the surgeon’s perspective at the forefront of ourdiscussion. The paper critically explores a range of methods, predominantly those developed during the EU project STIFF-FLOP, that facilitate the embedding of soft sensors into articulate soft robot structures using flexible, optics-based lightguides. We examine different optics-based approaches to pose perception in a minimally invasive surgery settings, and methods of integration are also discussed.

Artificial Intelligence of Things (AIoT) Enabled Virtual Shop Applications Using Self-Powered Sensor Enhanced Soft Robotic Manipulator

Rapid advancements of artificial intelligence of things (AIoT) technology pave the way for developing a digital-twin-based remote interactive system for advanced robotic-enabled industrial automation and virtual shopping. The embedded multifunctional perception system is urged for better interaction and user experience. To realize such a system, a smart soft robotic manipulator is presented that consists of a triboelectric nanogenerator tactile (T-TENG) and length (L-TENG) sensor, as well as a poly(vinylidene fluoride) (PVDF) pyroelectric temperature sensor. With the aid of machine learning (ML) for data processing, the fusion of the T-TENG and L-TENG sensors can realize the automatic recognition of the grasped objects with the accuracy of 97.143% for 28 different shapes of objects, while the temperature distribution can also be obtained through the pyroelectric sensor. By leveraging the IoT and artificial intelligence (AI) analytics, a digital-twin-based virtual shop is successfully implemented to provide the users with real-time feedback about the details of the product. In general, by offering a more immersive experience in human-machine interactions, the proposed remote interactive system shows the great potential of being the advanced human-machine interface for the applications of the unmanned working space.

Bioinspired Soft Microactuators

Soft actuators have the potential of revolutionizing the field of robotics. However, it has been a long-standing challenge to achieve simultaneously: i) miniaturization of soft actuators, ii) high contrast between materials properties at their "on" and "off" states, iii) significant actuation for high-payload mechanical work, and iv) ability to perform diverse shape transformations. This challenge is addressed by synergistically utilizing structural concepts found in the dermis of sea cucumbers and the tendrils of climbing plants, together with microfluidic fabrication to create diatomite-laden hygroscopically responsive fibers with a discontinuous ribbon of stiff, asymmetrically shaped, and hygroscopically inactive microparticles embedded inside. The microactuators can undergo various deformations and have very high property contrast ratios (20-850 for various mechanical characteristics of interest) between hydrated and dehydrated states. The resulting energy density, actuation strain, and actuation stress are shown to exceed those of natural muscle by ≈4, >2, and >30 times, respectively, and their weight-lifting ratio is 2-3 orders of magnitude higher than the value of recent hygroscopic actuators. This work offers a new and general way to design and fabricate next-generation soft microactuators, and thus advances the field of soft robotics by tailoring the structure and properties of deformable elements to suit a desired application.

Shape Changing Robots: Bioinspiration, Simulation, and Physical Realization

One of the key differentiators between biological and artificial systems is the dynamic plasticity of living tissues, enabling adaptation to different environmental conditions, tasks, or damage by reconfiguring physical structure and behavioral control policies. Lack of dynamic plasticity is a significant limitation for artificial systems that must robustly operate in the natural world. Recently, researchers have begun to leverage insights from regenerating and metamorphosing organisms, designing robots capable of editing their own structure to more efficiently perform tasks under changing demands and creating new algorithms to control these changing anatomies. Here, an overview of the literature related to robots that change shape to enhance and expand their functionality is presented. Related grand challenges, including shape sensing, finding, and changing, which rely on innovations in multifunctional materials, distributed actuation and sensing, and somatic control to enable next-generation shape changing robots are also discussed.

Exploiting Mechanical Instabilities in Soft Robotics: Control, Sensing, and Actuation

The rapidly expanding field of soft robotics has provided multiple examples of how entirely soft machines and actuators can outperform conventional rigid robots in terms of adaptability, maneuverability, and safety. Unfortunately, the soft and flexible materials used in their construction impose intrinsic limitations on soft robots, such as low actuation speeds and low output forces. Nature offers multiple examples where highly flexible organisms exploit mechanical instabilities to store and rapidly release energy. Guided by these examples, researchers have recently developed a variety of strategies to overcome speed and power limitations in soft robotics using mechanical instabilities. These mechanical instabilities provide, through rapid transitions from structurally stable states, a new route to achieve high output power amplification and attain impressive actuation speeds. Here, an overview of the literature related to the development of soft robots and actuators that exploit mechanical instabilities to expand their actuation speed, output power, and functionality is presented. Additionally, strategies using structural phase transitions to address current challenges in the area of soft robotic control, sensing, and actuation are discussed. Approaches using instabilities to create entirely soft logic modules to imbue soft robots with material intelligence and distributed computational capabilities are also reviewed.

Transparent Soft Actuators/Sensors and Camouflage Skins for Imperceptible Soft Robotics

The advent of soft robotics has led to great advancements in robots, wearables, and even manufacturing processes by employing entirely soft-bodied systems that interact safely with any random surfaces while providing great mechanical compliance. Moreover, recent developments in soft robotics involve advances in transparent soft actuators and sensors that have made it possible to construct robots that can function in a visually and mechanically unobstructed manner, assisting the operations of robots and creating more applications in various fields. In this aspect, imperceptible soft robotics that mainly consist of optically transparent imperceptible hardware components is expected to constitute a new research focus in the forthcoming era of soft robotics. Here, the recent progress regarding extended imperceptible soft robotics is provided, including imperceptible transparent soft robotics (transparent soft actuators/sensors) and imperceptible nontransparent camouflage skins. Their principles, materials selections, and working mechanisms are discussed so that key challenges and perspectives in imperceptible soft robotic systems can be explored.