Heterogeneous sensing in a multifunctional soft sensor for human-robot interfaces

Soft Materials and Devices Enabling Sensorimotor Functions in Soft Robots

Sensorimotor functions, the seamless integration of sensing, decision-making, and actuation, are fundamental for robots to interact with their environments. Inspired by biological systems, the incorporation of soft materials and devices into robotics holds significant promise for enhancing these functions. However, current robotics systems often lack the autonomy and intelligence observed in nature due to limited sensorimotor integration, particularly in flexible sensing and actuation. As the field progresses toward soft, flexible, and stretchable materials, developing such materials and devices becomes increasingly critical for advanced robotics. Despite rapid advancements individually in soft materials and flexible devices, their combined applications to enable sensorimotor capabilities in robots are emerging. This review addresses this emerging field by providing a comprehensive overview of soft materials and devices that enable sensorimotor functions in robots. We delve into the latest development in soft sensing technologies, actuation mechanism, structural designs, and fabrication techniques. Additionally, we explore strategies for sensorimotor control, the integration of artificial intelligence (AI), and practical application across various domains such as healthcare, augmented and virtual reality, and exploration. By drawing parallels with biological systems, this review aims to guide future research and development in soft robots, ultimately enhancing the autonomy and adaptability of robots in unstructured environments.

Plant-inspired decentralized controller for robust orientation control of soft robotic manipulators

Due to the complexity of deformations in soft manipulators, achieving precise control of their orientation is particularly challenging, especially in the presence of external disturbances and human interactions. Inspired by the decentralized growth mechanism of plant gravitropism, which enables plants' roots and stems to grow in the direction of gravity despite complex environmental interactions, this study proposes a decentralized control strategy for robust orientation control of multi-segment soft manipulators. This gravitropism-inspired decentralized controller was validated through simulations for convergence and robustness, and benchmarked against the traditional inverse Jacobian-based controller on a large-scale multi-segment soft manipulator. Experimental results demonstrate that the decentralized controller achieves comparable convergence and better control precision to the inverse Jacobian-based controller, while significantly outperforming it in disturbance rejection. Even in the presence of partial damage and human interaction, the decentralized controller provides robust control. This study provides a robust new approach for managing disturbances in complex environments, laying the foundation for further exploration of decentralized control strategies in soft robotics.

Biodegradable origami enables closed-loop sustainable robotic systems

Robots are increasingly integral across various sectors due to their efficiency and superior capabilities, which enable performance beyond human potential. However, the development of robotic systems often conflicts with the sustainable development goals set by the United Nations, as they generate considerable nondegradable waste and organic/inorganic pollutants throughout their life cycle. In this paper, we introduce a dual closed-loop robotic system that integrates biodegradable, sustainable materials such as plasticized cellulose films and NaCl-infused ionic conductive gelatin organogels. These materials undergo a closed-loop ecological cycle from processing to biodegradation, contributing to new growth, while the self-sensing, origami-based robot supports a seamless human-in-the-loop teleoperation system. This innovative approach represents a paradigm shift in the application of soft robotic systems, offering a path toward a more sustainable future by aligning advanced robotic functionalities with environmental stewardship.

Electrospun multifunctional nanofibers for advanced wearable sensors

The multifunctional extension of fiber-based wearable sensors determines their integration and sustainable development, with electrospinning technology providing reliable, efficient, and scalable support for fabricating these sensors. Despite numerous studies on electrospun fiber-based wearable sensors, further attention is needed to leverage composite structural engineering for functionalizing electrospun fibers. This paper systematically reviews the research progress on fiber-based multifunctional wearable sensors in terms of design concept, device fabrication, mechanism exploration, and application potential. Firstly, the basics of electrospinning are briefly introduced, including its development, principles, parameters, and material selection. Tactile sensors, as crucial components of wearable sensors, are discussed in detail, encompassing their performance parameters, transduction mechanisms, and preparation strategies for pressure, strain, temperature, humidity, and bioelectrical signal sensors. The main focus of the article is on the latest research progress in multifunctional sensing design concepts, multimodal decoupling mechanisms, sensing mechanisms, and functional extensions. These extensions include multimodal sensing, self-healing, energy harvesting, personal thermal management, EMI shielding, antimicrobial properties, and other capabilities. Furthermore, the review assesses existing challenges and outlines future developments for multifunctional wearable sensors, highlighting the need for continued research and innovation.

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.

Multimodal Flexible Sensor for the Detection of Pressing-Bending-Twisting Mechanical Deformations

Flexible sensors are increasingly significant in applications such as smart wearables and human-computer interactions. However, typical flexible sensors are spatially limited and can generally detect only one deformation mode. This study presents a novel multimodal flexible sensor that combines three sensing units: optoelectronics, ionic liquids, and conductive fabrics. It employs a sophisticated superposition and combination of the three sensing methods to achieve up to eight mechanical deformations, including pressing, bending, twisting, and combinations thereof, all within a very small sensor space. This sensor has excellent detection performance, high sensitivity (optoelectronics 4.312, ionic liquid 8.186, conductive fabric 2.438), a wide measurement range (pressing 0-75 kPa, bending 0-90°, and twisting 0-180°), and good consistency and repeatability. To address the signal coupling problem in multimode sensors, a deep learning method based on the Transformer is combined to provide precise decoupling of multimode signals and high-precision characterization of each mechanical deformation. Finally, the wrist joint experiments demonstrate the sensor's versatile uses in human-computer interaction.

Optimal Sensor Placement for Motion Tracking of Soft Wearables Using Bayesian Sampling

Soft sensors integrated or attached to robots or human bodies enable rapid and accurate estimation of the physical states of the target systems, including position, orientation, and force. While the use of a number of sensors enhances precision and reliability in estimation, it may constrain the movement of the target system or make the entire system complex and bulky. This article proposes a rapid, efficient framework for determining where to place the sensors on the system given the limited number of available sensors. In particular, given m candidates in location for sensor placement, the algorithm recommends m 0 locations that guarantee the maximal estimation performance, based on Bayesian sampling. The sampling and optimization method aims to maximize the log-likelihood in nonparametric regression between the measured values of the selected sensors and the target references. The proposed approach for the optimal sensor placement is validated through two scenarios: full-body motion sensing with a soft wearable sensor suit and fingertip position tracking with a motion-capture system. The proposed algorithm successfully determines the sensor locations close to the optimum within 20 min of learning for both cases.

A Soft Inductive Bimodal Sensor for Proprioception and Tactile Sensing of Soft Machines

The somatosensory system is crucial for living beings to survive and thrive in complex environments and to interact with their surroundings. Similarly, rapidly developed soft robots need to be aware of their own posture and detect external stimuli. Bending and force sensing are key for soft machines to achieve embodied intelligence. Here, we present a soft inductive bimodal sensor (SIBS) that uses the strain modulation of magnetic permeability and the eddy-current effect for simultaneous bidirectional bending and force sensing with only two wires. The SIBS is made of a flexible planar coil, a porous ferrite film, and a soft conductive film. By measuring the inductance at two different frequencies, the bending angle and force can be obtained and decoupled. Rigorous experiments revealed that the SIBS can achieve high resolution (0.44° bending and 1.09 mN force), rapid response, excellent repeatability, and high durability. A soft crawling robot embedded with one SIBS can sense its own shape and interact with and respond to external stimuli. Moreover, the SIBS is demonstrated as a wearable human-machine interaction to control a crawling robot via wrist bending and touching. This highlights that the SIBS can be readily implemented in diverse applications for reliable bimodal sensing.

Superflexible Carbon Nanofibers for Multidimensional Complex Deformation Sensing in Soft Robots

Soft robots can make complex motions or deformations due to their infinite freedom, which poses great challenges for monitoring their motion and position. While previous investigations of flexible sensing either focused on stretchable or compression deformations in one or two directions, the complex multidimensional deformations that occur on the surfaces of soft robots have been frequently overlooked. In this work, inspired by spider silk, superflexible carbon nanofibers with a bundled structure were biomimetically designed and fabricated using electrospinning technology and carbonization treatment. The fabricated fibers can be microscopically folded at 180° and can sustain multidimensional shrinkage deformation without microstructural damage during 200,000 times of repeated folding. In addition, the fibers process ultrasmall bending resistance that is two orders of magnitude lower than that of A4 paper and commercial conductive fibers, demonstrating excellent flexibility that is ideal for fabricating sensors in soft robots. Combining the study of origami techniques and mechanical simulations, the bending resistance of the fibers was found to have a step change in response to different deformation angles and radii. As a demonstration, a sensor based on this flexible carbon nanofiber successfully monitors the irregular shrinkage deformation of soft parts, showing great potential in applications of grasping, recognition, and perception. This work sheds light on the design of ultraflexible conductive carbon materials and provides an avenue for the extreme shape-morphing monitoring of soft robots.

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.

Ultrastretchable Segmented Sensors for Functional Human-Machine Interfaces

The key feature that enables soft sensors to shorten the performance gap between robots and biological structures is their deformability, coupled with their capability to measure physical changes. Robots equipped with these sensors can interact safely and proprioceptively with their environments. This has sparked interest in developing novel sensors with high stretchability for application in human-robot interactions. This study presents a novel ultrasoft optoelectronic segmented sensor design capable of measuring strains exceeding 500%. The sensor features an ultrastretchable segment physically joined with an asymmetrically configured soft proprioceptive segment. This configuration enables it to measure high strain and to detect both the magnitude and direction of bending. Although the sensor cannot decouple these types of deformations, it can sense prescribed motions that combine stretching and bending. The proposed sensor was applied to a highly deformable scissor mechanism and a human-robot interface (HRI) device for a robotic arm, capable of quantifying parameters in complex interactions. The results from the experiments also demonstrate the potential of the proposed segmented sensor concept when used in tandem with machine learning, affording new dimensions of proprioception to robots during multilayered interactions with humans.

Age of Flexible Electronics: Emerging Trends in Soft Multifunctional Sensors

With the commercialization of first-generation flexible mobiles and displays in the late 2010s, humanity has stepped into the age of flexible electronics. Inevitably, soft multifunctional sensors, as essential components of next-generation flexible electronics, have attracted tremendous research interest like never before. This review is dedicated to offering an overview of the latest emerging trends in soft multifunctional sensors and their accordant future research and development (R&D) directions for the coming decade. First, key characteristics and the predominant target stimuli for soft multifunctional sensors are highlighted. Second, important selection criteria for soft multifunctional sensors are introduced. Next, emerging materials/structures and trends for soft multifunctional sensors are identified. Specifically, the future R&D directions of these sensors are envisaged based on their emerging trends, namely i) decoupling of multiple stimuli, ii) data processing, iii) skin conformability, and iv) energy sources. Finally, the challenges and potential opportunities for these sensors in future are discussed, offering new insights into prospects in the fast-emerging technology.

Human-Inspired Tactile Perception System for Real-Time and Multimodal Detection of Tactile Stimuli

A human can intuitively perceive and comprehend complicated tactile information because the cutaneous receptors distributed in the fingertip skin receive different tactile stimuli simultaneously and the tactile signals are immediately transmitted to the brain. Although many research groups have attempted to mimic the structure and function of human skin, it remains a challenge to implement human-like tactile perception process inside one system. In this study, we developed a real-time and multimodal tactile system that mimics the function of cutaneous receptors and the transduction of tactile stimuli from receptors to the brain, by using multiple sensors, a signal processing and transmission circuit module, and a signal analysis module. The proposed system is capable of simultaneously acquiring four types of decoupled tactile information with a compact system, thereby enabling differentiation between various tactile stimuli, texture characteristics, and consecutive complex motions. This skin-like three-dimensional integrated design provides further opportunities in multimodal tactile sensing systems.

Integrating In-Plane Thermoelectricity and Out-Plane Piezoresistivity for Fully Decoupled Temperature-Pressure Sensing

A flexible sensor that simultaneously senses temperature and pressure is crucial in various fields, such as human-machine interaction, artificial intelligence, and biomedical applications. Previous research has mainly focused on single-function flexible sensors for e-skins or smart devices, and integrated bimodal sensing of temperature and pressure without complex crosstalk decoupling algorithms remains challenging. In this work, a flexible bimodal sensor is proposed that utilizes spatial orthogonality between in-plane thermoelectricity and out-plane piezoresistivity, which enables fully decoupled temperature-pressure sensing. The proposed bimodal sensor exhibits a high sensitivity of 281.46 µV K-1 for temperature sensing and 2.181 kPa-1 for pressure sensing. In the bimodal sensing mode, the sensor exhibits negligible mutual interference, providing a measurement error of ± 7% and ± 8% for temperature and pressure, respectively, within a 120 kPa pressure range and a 40 K temperature variation. Additionally, simultaneous spatial mapping of temperature and pressure with a bimodal sensor array enables contact shape identification with enhanced accuracy beyond the limit imposed by the number of sensing units. The proposed integrated bimodal sensing strategy does not require complex crosstalk decoupling algorithms, which represents a significant advancement in flexible sensors for applications that necessitate simultaneous sensing of temperature and pressure.

Multifunctional, Degradable Wearable Sensors Prepared with an Initiator and Crosslinker-Free Method

The present zwitterionic hydrogel-based wearable sensor exhibits various limitations, such as limited degradation capacity, unavoidable toxicity resulting from initiators, and poor mechanical properties that cannot satisfy practical demands. Herein, we present an initiator and crosslinker-free approach to prepare polyethylene glycol (PEG)@poly[2-(methacryloyloxy)ethyl] dimethyl-(3-sulfopropyl) (PSBMA) interpenetrating polymer network (IPN) hydrogels that are self-polymerized via sunlight-induced and non-covalent crosslinking through electrostatic interaction and hydrogen bonding among polymer chains. The PEG@PSBMA IPN hydrogel possesses tissue-like softness, superior stretchability (∼2344.6% elongation), enhanced fracture strength (∼39.5 kPa), excellent biocompatibility, antibacterial property, reliable adhesion, and ionic conductivity. Furthermore, the sensor based on the IPN hydrogel demonstrates good sensitivity and cyclic stability, enabling effective real-time monitoring of human body activities. Moreover, it is worth noting that the excellent degradability in the saline solution within 8 h makes the prepared hydrogel-based wearable sensor free from the electronic device contamination. We believe that the proposed strategy for preparing physical zwitterionic hydrogels will pave the way for fabricating eco-friendly wearable devices.

A study on a broadband photodetector based on hybrid 2D copper oxide/reduced graphene oxide

These days, photodetectors are a crucial part of optoelectronic devices, ranging from environmental monitoring to international communication systems. Therefore, fabricating these devices at a low cost but obtaining high sensitivity in a wide range of wavelengths is of great interest. This report introduces a simple solution-processed hybrid 2D structure of CuO and rGO for broadband photodetector applications. Particularly, 2D CuO acts as the active material, absorbing light to generate electron-hole pairs, while 2D rGO plays the role of a transport layer, driving charge carriers between two electrodes. Our device exhibits remarkable sensitivity to a wide wavelength range from 395 nm to 945 nm (vis-NIR region). Interestingly, our devices' responsivity and photoconductive gain were calculated (under 395 nm wavelength excitation) to be up to 8 mA W-1 and 28 fold, respectively, which are comparable values with previous publications. Our hybrid 2D structure between rGO and CuO enables a potential approach for developing low-cost but high-performance optoelectronic devices, especially photodetectors, in the future.

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human-machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

Optical hyperdimensional soft sensing: speckle-based touch interface and tactile sensor

Hyperdimensional computing (HDC) is an emerging computing paradigm that exploits the distributed representation of input data in a hyperdimensional space, the dimensions of which are typically between 1,000-10,000. The hyperdimensional distributed representation enables energy-efficient, low-latency, and noise-robust computations with low-precision and basic arithmetic operations. In this study, we propose optical hyperdimensional distributed representations based on laser speckles for adaptive, efficient, and low-latency optical sensor processing. In the proposed approach, sensory information is optically mapped into a hyperdimensional space with >250,000 dimensions, enabling HDC-based cognitive processing. We use this approach for the processing of a soft-touch interface and a tactile sensor and demonstrate to achieve high accuracy of touch or tactile recognition while significantly reducing training data amount and computational burdens, compared with previous machine-learning-based sensing approaches. Furthermore, we show that this approach enables adaptive recalibration to keep high accuracy even under different conditions.

Flexible Sensors with a Multilayer Interlaced Tunnel Architecture for Distinguishing Different Strains

The diversity of body joints and the complexity of joint motions cause flexible strain sensors to undergo complex strains such as stretching, compression, bending, and extrusion, which results in sensors that do not recognize different strains, facing great challenges in detecting the true motion characteristics of joints. Here, the monitoring of body joints' real motion characteristics has been realized by the sensor that can output response signals with different resistance trends for different strains. The sensor prepared by the sacrificial template method is characterized by a multilayered interlaced tunnel architecture and carbon black embedded in the inner wall of the tunnel. Stretching, compressive, and bending strains result in increasing, decreasing, and increasing resistance, followed by a decrease in resistance of the sensor, respectively. The sensor can still output distinguishable response signals, even in the presence of complex strains induced by squeezing. Low strain detection limits (0.03%) and wide detection ranges (>600%) are achieved due to the localized strain enhancement caused by the unique structure. The sensor can detect the motion characteristics of different joints in flexion-extension, abduction-adduction, and internal-external rotation, which, in turn, can be used for real-time monitoring of complex joint motions involved in limb rehabilitation. In addition, the sensor recognizes the 26 letters of the alphabet represented by sign language gestures. The above studies demonstrate the potential application of our prepared sensors in flexible, wearable devices.

Purposive Design of Stretchable Composite Electrodes for Strain-Negative, Strain-Neutral, and Strain-Positive Ionic Sensors

Soft ionic sensors have emerged as a promising device form to accommodate various future electronic applications. One of the hurdles in ionic sensors is that the sensing signals by mechanical deformation and other stimuli are mixed up. Although the performance of the ionic sensors is highly dependent on the structure of electrodes, systematic investigation of purposive electrode design has been rarely explored. This study proposes a simple strategy for designing stretchable composite electrodes which make the ionic sensor strain-negative, strain-neutral, and strain-positive. This study reveals that such strain-responses can be obtained by adjusting the surface coverage of the electrically-effective conductive fillers. On the basis of the concept, deposition of a Au film on an elastomer composite and crack formation of the Au film are presented for the practical fabrication of a highly reproducible strain-neutral ionic sensor. A completely strain-independent temperature sensor is demonstrated by using the Au crack-based ionic sensor. In addition, this study demonstrates a two-terminal shear sensor capable of recognizing shear directions by combining the strain-positive and strain-negative electrodes.

Soft Biomimetic Fiber-Optic Tactile Sensors Capable of Discriminating Temperature and Pressure

Tactile sensors with high softness and multisensory functions are highly desirable for applications in humanoid robotics, smart prosthetics, and human-machine interfaces. Here, we report a soft biomimetic fiber-optic tactile (SBFT) sensor that offers skin-like tactile sensing abilities to perceive and discriminate temperature and pressure. The SBFT sensor is fabricated by encapsulating a macrobent fiber Bragg grating (FBG) in an elastomeric droplet-shaped structure that results in two optical resonances associated with the FBG and excited whispering gallery modes (WGMs) propagating along the bent region. Benefiting from the different thermo-optic and stress-optic effects of FBG and WGM resonances, the pressure and temperature can be fully decoupled with a high precision of 0.2 °C and 0.8 mN, respectively. To achieve a compact system for signal demodulation, a single-cavity dual-comb fiber laser is developed to interrogate the SBFT sensor based on dual-comb spectroscopy, which enables fast spectral sampling with a single photodiode. We show that the SBFT sensor is capable of perceiving pressure, temperature, and hardness in touching soft tissues and human skins, demonstrating great promise for soft tissue palpation and human-like robotic perception.

Multi-modal deformation and temperature sensing for context-sensitive machines

Owing to the remarkable properties of the somatosensory system, human skin compactly perceives myriad forms of physical stimuli with high precision. Machines, conversely, are often equipped with sensory suites constituted of dozens of unique sensors, each made for detecting limited stimuli. Emerging high degree-of-freedom human-robot interfaces and soft robot applications are delimited by the lack of simple, cohesive, and information-dense sensing technologies. Stepping toward biological levels of proprioception, we present a sensing technology capable of decoding omnidirectional bending, compression, stretch, binary changes in temperature, and combinations thereof. This multi-modal deformation and temperature sensor harnesses chromaticity and intensity of light as it travels through patterned elastomer doped with functional dyes. Deformations and temperature shifts augment the light chromaticity and intensity, resulting in a one-to-one mapping between stimulus modes that are sequentially combined and the sensor output. We study the working principle of the sensor via a comprehensive opto-thermo-mechanical assay, and find that the information density provided by a single sensing element permits deciphering rich and diverse human-robot and robot-environmental interactions.

Ultrasensitive and robust mechanoluminescent living composites

Mechanosensing, the transduction of extracellular mechanical stimuli into intracellular biochemical signals, is a fundamental property of living cells. However, endowing synthetic materials with mechanosensing capabilities comparable to biological levels is challenging. Here, we developed ultrasensitive and robust mechanoluminescent living composites using hydrogels embedded with dinoflagellates, unicellular microalgae with a near-instantaneous and ultrasensitive bioluminescent response to mechanical stress. Not only did embedded dinoflagellates retain their intrinsic mechanoluminescence, but with hydrophobic coatings, living composites had a lifetime of ~5 months under harsh conditions with minimal maintenance. We 3D-printed living composites into large-scale mechanoluminescent structures with high spatial resolution, and we also enhanced their mechanical properties with double-network hydrogels. We propose a counterpart mathematical model that captured experimental mechanoluminescent observations to predict mechanoluminescence based on deformation and applied stress. We also demonstrated the use of the mechanosensing composites for biomimetic soft actuators that emitted colored light upon magnetic actuation. These mechanosensing composites have substantial potential in biohybrid sensors and robotics.

Soft Electromagnetic Motor and Soft Magnetic Sensors for Synchronous Rotary Motion

To create fully-soft robots, fully-soft actuators are needed. Currently, soft rotary actuator topologies described in the literature exhibit low rotational speeds, which limit their applicability. In this work, we describe a novel, fully-soft synchronous rotary electromagnetic actuator and soft magnetic contact switch sensor concept. In this study, the actuator is constructed using gallium indium liquid metal conductors, compliant permanent magnetic composites, carbon black powders, and flexible polymers. The actuator also operates using low voltages (<20 V, ≤10 A), has a bandwidth of 10 Hz, a stall torque of 2.5-3 mN·m, and no-load speed of up to 4000 rpm. These values show that the actuator rotates at over two orders-of-magnitude higher speed with at least one order-of-magnitude higher output power than previously developed soft rotary actuators. This unique soft rotary motor is operated in a manner similar to traditional hard motors, but is also able to stretch and deform to enable new soft robot functions. To demonstrate fully-soft actuator application concepts, the motor is incorporated into a fully-soft air blower, fully-soft underwater propulsion system, fully-soft water pump, and squeeze-based sensor for a fully-soft fan. Hybrid hard and soft applications were also tested, including a geared robotic car, pneumatic actuator, and hydraulic pump. Overall, this work demonstrates how the fully-soft rotary electromagnetic actuator can bridge the gap between the capabilities of traditional hard motors and novel soft actuator concepts.

Low-Cost Distributed Optical Waveguide Shape Sensor Based on WTDM Applied in Bionics

Bionic robotics, driven by advancements in artificial intelligence, new materials, and manufacturing technologies, is attracting significant attention from research and industry communities seeking breakthroughs. One of the key technologies for achieving a breakthrough in robotics is flexible sensors. This paper presents a novel approach based on wavelength and time division multiplexing (WTDM) for distributed optical waveguide shape sensing. Structurally designed optical waveguides based on color filter blocks validate the proposed approach through a cost-effective experimental setup. During data collection, it combines optical waveguide transmission loss and the way of controlling the color and intensity of the light source and detecting color and intensity variations for modeling. An artificial neural network is employed to model and demodulate a data-driven optical waveguide shape sensor. As a result, the correlation coefficient between the predicted and real bending angles reaches 0.9134 within 100 s. To show the parsing performance of the model more intuitively, a confidence accuracy curve is introduced to describe the accuracy of the data-driven model at last.

Single-Line Multi-Channel Flexible Stress Sensor Arrays

Flexible stress sensor arrays, comprising multiple flexible stress sensor units, enable accurate quantification and analysis of spatial stress distribution. Nevertheless, the current implementation of flexible stress sensor arrays faces the challenge of excessive signal wires, resulting in reduced deformability, stability, reliability, and increased costs. The primary obstacle lies in the electric amplitude modulation nature of the sensor unit's signal (e.g., resistance and capacitance), allowing only one signal per wire. To overcome this challenge, the single-line multi-channel signal (SLMC) measurement has been developed, enabling simultaneous detection of multiple sensor signals through one or two signal wires, which effectively reduces the number of signal wires, thereby enhancing stability, deformability, and reliability. This review offers a general knowledge of SLMC measurement beginning with flexible stress sensors and their piezoresistive, capacitive, piezoelectric, and triboelectric sensing mechanisms. A further discussion is given on different arraying methods and their corresponding advantages and disadvantages. Finally, this review categorizes existing SLMC measurement methods into RLC series resonant sensing, transmission line sensing, ionic conductor sensing, triboelectric sensing, piezoresistive sensing, and distributed fiber optic sensing based on their mechanisms, describes the mechanisms and characteristics of each method and summarizes the research status of SLMC measurement.

Multi-Dimensional Mechanical Mapping Sensor Based on Flexoelectric-Like and Optical Signals

Mechanical sensors execute multi-mode response to external force, which are cornerstones for applications in human-machine interactions and smart wearable equipments. Nevertheless, an integrated sensor responding to mechanical stimulation variables and providing the information of the corresponding signals, as velocity, direction, and stress distribution, remains a challenge. Herein, a Nafion@Ag@ZnS/polydimethylsiloxanes (PDMS) composite sensor is explored, which realizes the description of mechanical action via optics and electronics signals simultaneously. Combined with the mechano-luminescence (ML) originated from ZnS/PDMS and the flexoelectric-like effect of Nafion@Ag, the corresponding explored sensor achieves the detection of magnitude, direction, velocity, mode of mechanical stimulation, and the visualization of the stress distribution. Moreover, the outstanding cyclic stability, linearity response character, and rapid response time are demonstrated. Accordingly, the intelligent recognition and manipulation of a target are realized, which indicate a smarter human-machine interface sensing applied for wearable devices and mechanical arms can be expected.

Soft Robotics Enables Neuroprosthetic Hand Design

Development and implementation of neuroprosthetic hands is a multidisciplinary field at the interface between humans and artificial robotic systems, which aims at replacing the sensorimotor function of the upper-limb amputees as their own. Although prosthetic hand devices with myoelectric control can be dated back to more than 70 years ago, their applications with anthropomorphic robotic mechanisms and sensory feedback functions are still at a relatively preliminary and laboratory stage. Nevertheless, a recent series of proof-of-concept studies suggest that soft robotics technology may be promising and useful in alleviating the design complexity of the dexterous mechanism and integration difficulty of multifunctional artificial skins, in particular, in the context of personalized applications. Here, we review the evolution of neuroprosthetic hands with the emerging and cutting-edge soft robotics, covering the soft and anthropomorphic prosthetic hand design and relating bidirectional neural interactions with myoelectric control and sensory feedback. We further discuss future opportunities on revolutionized mechanisms, high-performance soft sensors, and compliant neural-interaction interfaces for the next generation of neuroprosthetic hands.

Mechano-luminescence Behavior of Lanthanide-Doped Fluoride Nanocrystals for Three-Dimensional Stress Imaging

Pervasive mechanical force in nature and human activities is closely related to intriguing physics and widespread applications. However, describing stress distribution timely and precisely in three dimensions to avoid "groping in the dark" is still a formidable challenge, especially for nonplanar structures. Herein, we realize three-dimensional (3D) stress imaging for sharp arbitrary targets via advanced 3D printing, owing to the use of fluoride nanocrystal(NC)-based ink. Notably, a fascinating mechano-luminescence (ML) is observed for the homogeneously dispersed NaLuF₄:Tb³⁺ NCs (∼25 nm) with rationally designed deep traps (at 0.88 and 1.02 eV) via incorporating Cs⁺ ions and using X-ray irradiation. Carriers captured in the corresponding traps are steadily released under mechanical stimulations, which enables a ratio metric luminescence intensity based on the applied force. As a result, a significant mechano-optical conversion and superior optical waveguide of the corresponding transparent printed targets demonstrate stress in 3D with a high spatial and temporal resolution based on stereovision. These results highlight the optical function of the 3D-printed fluoride NCs, which cast light into the black boxes of stress described in space, benefiting us in understanding the ubiquitous force relevant to most natural and engineering processes.

Highly Stable Liquid Metal Conductors with Superior Electrical Stability and Tough Interface Bonding for Stretchable Electronics

Ga-based liquid metal stretchable conductors have recently gained interest in flexible electronic devices such as electrodes, antennas, and sensors. It is essential to maintain electrical stability under strain or cyclic strain for reliable data acquisition and exhibit tough interfacial bonding between liquid metal and polymers to prevent performance loss and device failure. Herein, a highly stable conductor with superior electrical stability and tough interface bonding is introduced by casting curable polymers and a peeling-activated process from liquid metal particles. Based on the compensating effect of liquid metal, similar to the recharge relationship of water between rivers and lakes in nature, the conductor is not only strain-insensitive (ΔR/R₀ < 10% for 100% strain) but also immune to cyclic deformation (ΔR/R₀ < 7% with 5000 stretching cycles at 50% strain). Embedding liquid metal within the elastomer to create stretchable conductors effectively improves interfacial adhesion properties (the fluid-solid interfacial adhesion force increases from 0.48 to 0.62 mN/mm²). The constructed tough interface could even withstand sonication treatment. Finally, by combining strategies in material design and fabrication, an integrated array composed of vertical interconnect access and robust electrodes is fabricated, which simultaneously holds tough interfacial bonding with the upper and lower layers.

The Critical Biomechanics of Aortomitral Angle and Systolic Anterior Motion: Engineering Native Ex Vivo Simulation

Systolic anterior motion (SAM) of the mitral valve (MV) is a complex pathological phenomenon often occurring as an iatrogenic effect of surgical and transcatheter intervention. While the aortomitral angle has long been linked to SAM, the mechanistic relationship is not well understood. We developed the first ex vivo heart simulator capable of recreating native aortomitral biomechanics, and to generate models of SAM, we performed anterior leaflet augmentation and sequential undersized annuloplasty procedures on porcine aortomitral junctions (n = 6). Hemodynamics and echocardiograms were recorded, and echocardiographic analysis revealed significantly reduced coaptation-septal distances confirming SAM (p = 0.003) and effective manipulation of the aortomitral angle (p < 0.001). Upon increasing the angle in our pathological models, we recorded significant increases (p < 0.05) in both coaptation-septal distance and multiple hemodynamic metrics, such as aortic peak flow and effective orifice area. These results indicate that an increased aortomitral angle is correlated with more efficient hemodynamic performance of the valvular system, presenting a potential, clinically translatable treatment opportunity for reducing the risk and adverse effects of SAM. As the standard of care shifts towards surgical and transcatheter interventions, it is increasingly important to better understand SAM biomechanics, and our advances represent a significant step towards that goal.

Soft Electronics for Health Monitoring Assisted by Machine Learning

Due to the development of the novel materials, the past two decades have witnessed the rapid advances of soft electronics. The soft electronics have huge potential in the physical sign monitoring and health care. One of the important advantages of soft electronics is forming good interface with skin, which can increase the user scale and improve the signal quality. Therefore, it is easy to build the specific dataset, which is important to improve the performance of machine learning algorithm. At the same time, with the assistance of machine learning algorithm, the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis. The soft electronics and machining learning algorithms complement each other very well. It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future. Therefore, in this review, we will give a careful introduction about the new soft material, physiological signal detected by soft devices, and the soft devices assisted by machine learning algorithm. Some soft materials will be discussed such as two-dimensional material, carbon nanotube, nanowire, nanomesh, and hydrogel. Then, soft sensors will be discussed according to the physiological signal types (pulse, respiration, human motion, intraocular pressure, phonation, etc.). After that, the soft electronics assisted by various algorithms will be reviewed, including some classical algorithms and powerful neural network algorithms. Especially, the soft device assisted by neural network will be introduced carefully. Finally, the outlook, challenge, and conclusion of soft system powered by machine learning algorithm will be discussed.

Recent Progress of Tactile and Force Sensors for Human-Machine Interaction

Human-Machine Interface (HMI) plays a key role in the interaction between people and machines, which allows people to easily and intuitively control the machine and immersively experience the virtual world of the meta-universe by virtual reality/augmented reality (VR/AR) technology. Currently, wearable skin-integrated tactile and force sensors are widely used in immersive human-machine interactions due to their ultra-thin, ultra-soft, conformal characteristics. In this paper, the recent progress of tactile and force sensors used in HMI are reviewed, including piezoresistive, capacitive, piezoelectric, triboelectric, and other sensors. Then, this paper discusses how to improve the performance of tactile and force sensors for HMI. Next, this paper summarizes the HMI for dexterous robotic manipulation and VR/AR applications. Finally, this paper summarizes and proposes the future development trend of HMI.

Intentional Blocking Based Photoelectric Soft Pressure Sensor with High Sensitivity and Stability

Soft pressure sensors have recently attracted considerable attention because of their applications in human-machine interface, soft robotics, and prosthetics. However, there remain some challenges in achieving satisfactory performance (e.g., high sensitivity, wide sensing range, high stability) for soft pressure sensors. This article reports an intentional blocking based photoelectric pressure sensor. Two different blocking methods are investigated: the single-row-pyramid blocking and the double-row-pyramid blocking. The sensor has a simple structure, which is made of a light-emitting diode, photosensitive element, and silicone sensor shell. Experiments demonstrate that the sensor has a high sensitivity (the maximum sensitivity is 48.07 kPa^-1, and the minimum measurement pressure is 0.8 Pa), large pressure-sensing range (the sensing range is up to 120 kPa), superior stability (a drift about 0.4% over 12,130 repetitive cycles at 0-80 kPa), low drift (< ±0.2% in different 3-day testing), negligible hysteresis, and high signal-to-noise ratio (over 55 dB). By mounting the pressure sensor at the end of a robotic arm, the robot can detect subtle collisions (such as touching a balloon through a pinpoint). In addition, this article fabricates a tactile glove based on the proposed pressure sensor and shows the application of this glove for music playing and object weighing. This study provides a new structure for photoelectric sensors to increase sensitivity and also provides a more convenient way to fabricate photoelectric pressure sensors.

Data-driven multi-joint waveguide bending sensor based on time series neural network

Due to the bulky interrogation devices, traditional fiber optic sensing system is mainly connected by wire or equipped only for large facilities. However, the advancement in neural network algorithms and flexible materials has broadened its application scenarios to bionics. In this paper, a multi-joint waveguide bending sensor based on color dyed filters is designed to detect bending angles, directions and positions. The sensors are fabricated by casting method using soft silicone rubber. Besides, required optical properties of sensor materials are characterized to better understand principles of the sensor design. Time series neural networks are utilized to predict bending position and angle quantitatively. The results confirm that the waveguide sensor demodulated by the data-driven neural network algorithm performs well and can be used for engineering applications.

Bioinspired Amphibious Origami Robot with Body Sensing for Multimodal Locomotion

Animals have long captured the inspirations of researchers in robotics with their unrivaled capabilities of multimodal locomotion on land and in water, achieved by functionally versatile limbs. Conventional soft robots show infinite degrees-of-freedom (DOFs), making it hard to be actuated and conduct multiple movements especially for multimodal locomotion in different environments. An origami robot, which is capable of reversibly transforming the robotic shape by simple creases folding/unfolding, reveals advantages for imitating flexible movements of animals, thus drawing more and more attention. However, it poses substantial technological challenges for bioinspired design, sensing, and actuation of origami robots that can generate multimodal locomotion through performing complex morphologic deformation in different scenarios such as land and water. To relieve this issue, we propose a novel bioinspired amphibious origami machine with body sensing for multimodal locomotion. In this work, inspired by the peristalsis of inchworm and human swimming behaviors, a unique origami body with legs and origami arms is developed to enable the integrated robot to move both on land and in water. Instead of traditional electronic sensors, we design highly stretchable and foldable layer resistive sensor with conductive polymers coated onto the origami body to achieve robotic sensing such as obstacle detection. In addition, with detailed analysis, a self-designed pneumatic system of time division, multiplexing, and serialization is adopted to efficiently control the robot with high DOF. We eventually demonstrate that the fabricated origami robot successfully moves in amphibious environments, which is capable of crawling forward, turning right/left, and swimming. We expect that this work indicates contributions to advanced origami design, actuation control, and body sensor of the bioinspired robot with multimodal locomotion for broadly practical applications.

Soft Robots' Dynamic Posture Perception Using Kirigami-Inspired Flexible Sensors with Porous Structures and Long Short-Term Memory (LSTM) Neural Networks

Soft robots can create complicated structures and functions for rehabilitation. The posture perception of soft actuators is critical for performing closed-loop control for a precise location. It is essential to have a sensor with both soft and flexible characteristics that does not affect the movement of a soft actuator. This paper presents a novel end-to-end posture perception method that employs flexible sensors with kirigami-inspired structures and long short-term memory (LSTM) neural networks. The sensors were developed with conductive sponge materials. With one-step calibration from the sensor output, the posture of the soft actuator could be calculated by the LSTM network. The method was validated by attaching the developed sensors to a soft fiber-reinforced bending actuator. The results showed the accuracy of posture prediction of sponge sensors with three kirigami-inspired structures ranged from 0.91 to 0.97 in terms of R2. The sponge sensors only generated a resistive torque value of 0.96 mNm at the maximum bending position when attached to a soft actuator, which would minimize the effect on actuator movement. The kirigami-inspired flexible sponge sensor could in future enhance soft robotic development.

Machine Learning-Assisted Multifunctional Environmental Sensing Based on a Piezoelectric Cantilever

Multifunctional environmental sensing is crucial for various applications in agriculture, pollution monitoring, and disease diagnosis. However, most of these sensing systems consist of multiple sensors, leading to significantly increased dimensions, energy consumption, and structural complexity. They also often suffer from signal interferences among multiple sensing elements. Herein, we report a multifunctional environmental sensor based on one single sensing element. A MoS₂ film was deposited on the surface of a piezoelectric microcantilever (300 × 1000 μm²) and used as both a sensing layer and top electrode to make full use of the changes in multiple properties of MoS₂ after its exposure to various environments. The proposed sensor has been demonstrated for humidity detection and achieved high resolution (0.3% RH), low hysteresis (5.6%), and fast response (1 s) and recovery (2.8 s). Based on the analysis of the magnitude spectra for transmission using machine learning algorithms, the sensor accurately quantifies temperatures and CO₂ concentrations in the interference of humidity with accuracies of 91.9 and 92.1%, respectively. Furthermore, the sensor has been successfully demonstrated for real-time detection of humidity and temperature or CO₂ concentrations for various applications, revealing its great potential in human-machine interactions and health monitoring of plants and human beings.

Low-Powered and Resilient IR-Based Pigmented Soft Optoelectronic Sensors

Soft optoelectronic sensors capable of multimodal sensing have high repeatability, which makes them an attractive choice for applications requiring deformable sensors. A weakness of these sensors is the constant supply of electrical power input required to pass the light signal through their core, which can lead to excessive power requirements for portable devices. Using an infrared (IR) spectrum signal that requires very low power for signal propagation should help alleviate this issue. However, soft optoelectronic sensors can be easily disturbed by external light sources or even suffer from cross-interference, and IR-based sensors are more susceptible to such interferences since IR wavelengths can penetrate the cladding material generally used in soft optical waveguides. This paper presents a highly stretchable low-powered IR-based soft optoelectronic stretchable sensor with pigmented cladding capable of multimodal sensing. The use of an IR-spectrum signal makes it consume a fraction of the power of what a visible spectrum-based optoelectronic sensor would consume. Pigmented elastomers are used as the cladding of the waveguides of these sensors, which makes them highly resilient. These sensors are embedded in a resilient soft robotic gripper capable of controlling its contact force even with significant external disturbances.

Shielded soft force sensors

Force and strain sensors made of soft materials enable robots to interact intelligently with their surroundings. Capacitive sensing is widely adopted thanks to its low power consumption, fast response, and facile fabrication. Capacitive sensors are, however, susceptible to electromagnetic interference and proximity effects and thus require electrical shielding. Shielding has not been previously implemented in soft capacitive sensors due to the parasitic capacitance between the shield and sensing electrodes, which changes when the sensor is deformed. We address this crucial challenge by patterning the central sensing elastomer layer to control its compressibility. One design uses an ultrasoft silicone foam, and the other includes microchannels filled with liquid metal and air. The force resolution is sub-mN both in normal and shear directions, yet the sensor withstands large forces (>20 N), demonstrating a wide dynamic range. Performance is unaffected by nearby high DC and AC electric fields and even electric sparks.

Capacitive soft force sensors require electrical shielding from electromagnetic interference, but this shielding can mess with the effectiveness of the sensing electrodes. Here, Aksoy et al. solve this problem by patterning the central sensing elastomer layer to control its compressibility.

High-Stretchability, Ultralow-Hysteresis ConductingPolymer Hydrogel Strain Sensors for Soft Machines

Highly stretchable strain sensors based on conducting polymer hydrogel are rapidly emerging as a promising candidate toward diverse wearable skins and sensing devices for soft machines. However, due to the intrinsic limitations of low stretchability and large hysteresis, existing strain sensors cannot fully exploit their potential when used in wearable or robotic systems. Here, a conducting polymer hydrogel strain sensor exhibiting both ultimate strain (300%) and negligible hysteresis (<1.5%) is presented. This is achieved through a unique microphase semiseparated network design by compositing poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) nanofibers with poly(vinyl alcohol) (PVA) and facile fabrication by combining 3D printing and successive freeze-thawing. The overall superior performances of the strain sensor including stretchability, linearity, cyclic stability, and robustness against mechanical twisting and pressing are systematically characterized. The integration and application of such strain sensor with electronic skins are further demonstrated to measure various physiological signals, identify hand gestures, enable a soft gripper for objection recognition, and remote control of an industrial robot. This work may offer both promising conducting polymer hydrogels with enhanced sensing functionalities and technical platforms toward stretchable electronic skins and intelligent robotic systems.

Speckle-based high-resolution multimodal soft sensing

Skin-like soft sensors are key components for human–machine interfaces; however, the simultaneous sensing of several types of stimuli remains challenging because large-scale sensor integration is required with numerous wire connections. We propose an optical high-resolution multimodal sensing approach, which does not require integrating multiple sensors. This approach is based on the combination of an optical scattering phenomenon, which can encode the information of various stimuli as a speckle pattern, and a decoding technique using deep learning. We demonstrate the simultaneous sensing of three different physical quantities—contact force, contact location, and temperature—with a single soft material. Another unique capability of the proposed approach is spatially continuous sensing with an ultrahigh resolution of few tens of micrometers, in contrast to previous multimodal sensing approaches. Furthermore, a haptic soft device is presented for a human–machine interface. Our approach encourages the development of high-performance smart skin-like sensors.

Laser-Sculptured Hierarchical Spinous Structures for Ultra-High-Sensitivity Iontronic Sensors with a Broad Operation Range

Tactile pressure sensing over a wide operation range (>1 MPa) is challenging for a variety of applications in fields such as aviation, oceanography, and biomedicine. Recently, innovative strategies have been utilized to improve the performances of tactile sensors using specially designed structures, dielectric layers, and electrodes. Here, a hierarchical structural design based on ionic gel films has been utilized to build iontronic pressure sensors with ultrahigh sensitivities and broad operation ranges. Sculptured patterns made by a controlled CO₂ laser scanning process have been produced on polyimide films to achieve two kinds of protrusion structures for high specific surface areas and strength to withstand high pressure. The iontronic sensor has been constructed by adding two screen-printed electrodes of high surface areas to achieve an ultrahigh sensitivity of 2593 kPa^-1 and a wide pressure range from 0 Pa to 3.36 MPa. The prototype device also has a fast response and recovery time of 26 and 13 ms, respectively, and an excellent mechanical durability in the endurance test of over 2700 repeated loading and unloading cycles under a pressure of 1 MPa. Several application examples have been demonstrated, including the detection of physiological signals on human volunteers, the feedback control of intelligent robots, the grasping operation of underwater soft grippers, and the environmental wind-speed monitoring. As such, this work demonstrates a versatile and economical methodology to produce high-performance flexible sensors for various potential applications.

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.

Undulatory Swimming Performance Explored With a Biorobotic Fish and Measured by Soft Sensors and Particle Image Velocimetry

Due to the difficulty of manipulating muscle activation in live, freely swimming fish, a thorough examination of the body kinematics, propulsive performance, and muscle activity patterns in fish during undulatory swimming motion has not been conducted. We propose to use soft robotic model animals as experimental platforms to address biomechanics questions and acquire understanding into subcarangiform fish swimming behavior. We extend previous research on a bio-inspired soft robotic fish equipped with two pneumatic actuators and soft strain sensors to investigate swimming performance in undulation frequencies between 0.3 and 0.7 Hz and flow rates ranging from 0 to 20c m s in a recirculating flow tank. We demonstrate the potential of eutectic gallium-indium (eGaIn) sensors to measure the lateral deflection of a robotic fish in real time, a controller that is able to keep a constant undulatory amplitude in varying flow conditions, as well as using Particle Image Velocimetry (PIV) to characterizing swimming performance across a range of flow speeds and give a qualitative measurement of thrust force exerted by the physical platform without the need of externally attached force sensors. A detailed wake structure was then analyzed with Dynamic Mode Decomposition (DMD) to highlight different wave modes present in the robot's swimming motion and provide insights into the efficiency of the robotic swimmer. In the future, we anticipate 3D-PIV with DMD serving as a global framework for comparing the performance of diverse bio-inspired swimming robots against a variety of swimming animals.

Challenges and solutions for application and wider adoption of wearable robots

The science and technology of wearable robots are steadily advancing, and the use of such robots in our everyday life appears to be within reach. Nevertheless, widespread adoption of wearable robots should not be taken for granted, especially since many recent attempts to bring them to real-life applications resulted in mixed outcomes. The aim of this article is to address the current challenges that are limiting the application and wider adoption of wearable robots that are typically worn over the human body. We categorized the challenges into mechanical layout, actuation, sensing, body interface, control, human-robot interfacing and coadaptation, and benchmarking. For each category, we discuss specific challenges and the rationale for why solving them is important, followed by an overview of relevant recent works. We conclude with an opinion that summarizes possible solutions that could contribute to the wider adoption of wearable robots.