1. Introduction
Over millions of years of evolutionary development, humans have developed remarkably dexterous hands that are unmatched by those of other animals. The complex structure of the human hand, encompassing an assemblage of bones, ligaments, and muscles, collaborates synergistically to combine robustness and delicate control, enabling both forceful and intricate maneuvers. In robotics, the advent of robots seeks to either replace or assist human efforts in executing specific tasks and operations. Robotic hands and end effectors are crucial components of robotic systems that directly interact with the external environment and are responsible for executing various actions, grasping, and manipulation tasks.
To enhance the flexibility of mechanical hands, traditional rigid robotic hands incorporate numerous motors, linkages, gears, and springs to achieve the desired functionality. However, this approach significantly increases the complexity of the structure. Although rigid humanoid hands are precise and responsive, they can cause irreparable damage when handling fragile objects [1,2,3]. Additionally, the potential weight of rigid structures can pose an injury risk during human–robot interactions. Contrarily, anthropomorphic soft hands leverage the inherent flexibility of their materials, offering remarkable advantages, such as compliance and high resistance to external impact, compression, torsion, and collisions [4,5,6]. These benefits stemming from their “soft” characteristics have prompted increased research in related studies.
Soft hands have received more attention due to their innate compliance, permitting interaction with the environment via techniques akin to those of natural organisms. The inherent compliance reduces the stringent requirements for complex and precise kinematic modeling and high-resolution sensor feedback, simplifying traditional grasping problems encountered by rigid robotic hands. The grasp of soft hands can be effectively controlled by adjusting the input pressure. The passive adaptability and compliance in their structure significantly simplify grasp planning problems [7,8]. Therefore, soft hands are a promising choice for human-centered machine grasping tasks, offering performance benefits such as interaction safety, grasp reliability, and cost-efficacy.
The recreation of humanoid dexterous hands is an ongoing pursuit in the field of robotics. By imitating the shape, structure, and functionality of human hands, soft humanoid dexterous hands can partially achieve human-like movements, providing broad adaptability and operability in grasping and hand manipulation [9,10]. It is foreseeable that soft humanoid dexterous hands will become a focal point in the development of robotic hands, finding increasingly widespread applications and market prospects in areas such as flexible grasping solutions in industrial production [11,12], medical rehabilitation [13,14,15,16], and home services [17,18].
However, there are several differences between human and soft hands. First, soft hands are still unable to match the flexibility and adaptability of human hands. Human hands possess exceptional perception and adjustment abilities, adapting to various shapes and environments, while soft hands face challenges when dealing with objects in complex three-dimensional (3D) spaces [19,20]. Second, the control systems of soft hands are relatively complex, potentially requiring advanced algorithms and sensing technologies to achieve fine manipulation similar to human hands [21,22]. The high coordination and flexibility exhibited by human hands during various tasks may necessitate further research and technological innovation for soft hands, while durability and stability present additional challenges [23,24], requiring attention.
This review aims to provide an overview of the main directions in soft hand development, address the challenges and current issues in the research of soft humanoid dexterous hands, and explore potential future trends. The crucial factors that determine whether a soft hand can match the capabilities of a human hand are its gripping/manipulation performance and perception capability. Therefore, this review primarily focuses on the evolution of gripping/manipulation performance and advancements in tactile perception of soft hands. Additionally, the inclusion of machine learning in soft robots is discussed, as it enables them to possess more sophisticated perception abilities and improved gripping/manipulation strategies, allowing them to intelligently adapt to various environments and tasks. Since the materials, manufacturing processes, and driving methods for soft hands have been widely investigated [25,26,27,28,29], these aspects are not extensively discussed in this review. Popular materials, manufacturing methodologies, driving force, and control policy used in soft hands are listed in Table 1.
2. Dexterity and Gripping/Manipulation Performance
The grasping and manipulation capabilities of the human hand are highly complex and remarkable, showcasing the unique adaptability and flexibility developed throughout human evolution. First, the five fingers of the human hand possess independent motion capabilities, each with its own joint system that allows for individual flexion, extension, abduction, and adduction. This multi-joint structure grants the human hand exceptional dexterity, enabling the fingers to adapt to objects of various shapes and sizes (Figure 1a). The human hand also exhibits coordinated movements, allowing the fingers to work together to accomplish intricate manipulation tasks. The synergy of the fingers allows the human hand to perform precise grasping actions, such as delicately picking up small objects or adjusting the posture of larger objects. This coordination enables efficient object manipulation in various tasks. The flexibility of the human hand is driven by its highly complex biological structure and the remarkable neural control system, enabling independent, precise movements of the joints in a 3D space. The motion of these joints is intricately regulated by neural control systems, achieving highly coordinated and flexible control over the fingers. This section focuses on the dexterity and gripping/manipulation performance of different types of soft hands. The discussion categorizes these soft hands into three types based on their structure and functionality: non-anthropomorphic grippers, underactuated anthropomorphic hands, and anthropomorphic dexterous hands.
2.1. Nonanthropomorphic Grippers
As the end effector, the complete functionality of a robot hand is essential in the field of automation, with grasping being the most easily achievable operation. Therefore, two-finger and multi-finger soft grippers are fundamental for simulating human hand movements.
Soft grippers can grasp and release objects while executing specific actions. Pneumatic grippers are the most commonly employed, deforming when inflated, which enables approaching and securely grasping an object. The goal of research on robots using two-finger grippers for grasping tasks is to build a grasping recognition system that is fast, accurate, and appropriate for use by robotic hands [50,51]. However, multi-finger grippers are often employed since two-finger grippers frequently find it difficult to successfully complete grasping tasks when dealing with objects of complex shapes. Due to their multi-finger design, they are capable of multi-contact grasping of target objects, improving the grasping success rate and reliability [59,60].
Two-finger and multi-finger grippers demonstrate efficiency and speed when repeatedly grasping the same type of object. Soft grippers have a lower design time and production cost than other actuators due to their comparatively simpler structure. Therefore, they are popular for simple tasks in industrial applications and include commercial products, such as Festo’s tentacle gripper and mGrip from Soft Robotics Inc. [61,62,63].
In this context, a significant emphasis has been placed on enhancing output force and actuation speed when developing novel grippers. To achieve these objectives, many studies have integrated bistable mechanisms into the soft gripper design [51,64], which are characterized by two stable equilibrium states, representing local minima in the total potential energy. The incorporation of bistable mechanisms reduces control complexity, enables fast motions, and promotes energy conservation.
However, the functionality of these grippers still suffers from single grasping mode, enveloped grasping, or pinch grasping, limiting their applications and reducing their reliability in grasping small objects. Even in dual grasping mode [50], these grippers are still limited to object grasping, lacking the capacity for intricate manipulation.
2.2. Underactuated Anthropomorphic Soft Hands
Two-finger and multi-finger grippers face challenges in adapting to situations where the objects to be grasped are constantly changing, necessitating the use of a dexterous hand. In this context, the term “dexterity” refers to the ability of the hand to exhibit a wide range of postures. A hand is considered more dexterous when it displays higher variability, increasing its grasping diversity and in-hand manipulation involving different grasping patterns. For instance, small objects can be precisely grasped with a few fingers, while larger objects may require an enveloping grip, and thin objects can be grasped via a thumb opposition grip [4,5,65].
To adapt to a wider range of object shapes and sizes, soft hands have been designed and developed, inspired by the shape of the human hand. The human hand can manipulate objects of various morphologies and materials and can tune the pose and position of objects (in-hand manipulation) with high dexterity in limited spaces. Constructing dexterous anthropomorphic hands capable of autonomously grasping and manipulating objects has been an important aim during robotic system design.
Represented by the RBO hand, these developed soft hands are primarily designed based on the principle of passive adaptation to the shape of the object [8,52]. Passive adaptability enables the hand to dynamically adjust its surface in response to contact forces, achieving a shape-matching effect with the object. Enhancing shape-matching improves the contact area between the hand and the object, eliminating the requirement for explicit sensing and control [15,66,67]. This not only increases resilience against uncertainties in hand position, finger control, and environmental factors, but also enables a passive adaptive hand to establish contact in any direction without sustaining damage [53]. Consequently, the compliant hand can effectively utilize the environment as a guide during grasping motions, further strengthening grip robustness. Therefore, passive adaptability is key in soft hand design to achieve successful grasping under uncertainty [5,68].
Traditionally, achieving dexterous grasping capabilities in robotic hands involved complex multi-joint structures and intricate actuation mechanisms. In addition to requiring sophisticated perception and control systems, these hands are expensive and challenging to design [69]. A significant direction in soft hand design is to simplify the system and improve robustness. Underactuated soft hands utilize reasonable structural designs to control hand movements with fewer degrees of freedom (DOFs) than finger joints, reducing the complexity of the entire hand system while improving reliability [70].
Unlike rigid dexterous hands, soft hands do not require additional actuators to achieve human-like bending motions. For instance, Feix et al. introduced a configuration featuring a single actuator per finger, enabling the soft hand to effectively accomplish 31 out of 33 grasp postures outlined in the widely recognized Feix taxonomy [71]. Similarly, the RBO 2 hand, with a mere 7 DOFs, successfully executes 31 out of 33 grasp postures from the Feix taxonomy [52]. Furthermore, Fras et al. proposed a biomimetic hand design where each finger possessed only one DOF yet was still capable of performing a diverse range of human-like gestures and effectively grasping various objects [67].
2.3. Dexterous Anthropomorphic Hands
Underactuated hands have shown robustness in grasping tasks, providing cost-effectiveness and system simplification. However, their operational capabilities are inherently limited due to their underactuated nature. This presents a trade-off between robustness and functionality in robotic hand design. A notable limitation of underactuated soft hands is their restricted dexterity, particularly in manipulating and repositioning objects [19,54]. Moreover, the majority of soft hands, limited by soft mechanical design and manufacturing technologies, typically only exhibit several DOFs [5,52,56]. Underactuated fingers constrain the robotic hand’s workspace, thus limiting its flexibility. This shortcoming hinders their broad application in complex, human-centric tasks. This is primarily attributed to the limited dexterity of soft actuators, which typically function as the fingers in soft hands. Most soft actuators have pre-defined motion trajectories, resulting in fixed trajectories for the fingers and overlapping motion limited to a singular point [72,73,74]. This paradox has spurred research into specific operational processes and the design of more dexterous hands, which are intelligent, multipurpose mechanical structures designed for a variety of tasks [4,6,20,75].
The common workspace between fingers is critical for in-hand manipulation. Designing multi-DOF soft fingers is a viable solution to address this, as demonstrated by integrating multiple actuators into a single finger [5,19,76,77]. Yet, this integration increases finger size and weight. Therefore, developing compact, lightweight multi-DOF soft fingers remains a valuable goal. Additionally, biomimetic dexterity, encompassing both appearance and kinematic functionality, is a key consideration in soft hand design, aiming for the effective handling of everyday objects.
Human-like designs with additional actuators can significantly enhance soft hand flexibility. Notable examples include the BCL-13 hand [20], the BCL-26 hand [5], the Blue hand [19], and particularly, the Blue hand with a total of 21 DOFs, able to perform all 33 grasp types in the Feix taxonomy and pass all Kapandji tests for thumb dexterity [19]. Another example is the dual-mode actuators, which enable fingers to execute both bending and twisting motions [66]. With ongoing advancements in soft robotics, soft humanoid dexterous hands are evolving towards more humanoid appearances and motion characteristics, as seen in the BCL-26 hand [5], the RBO Hand 3 [6], and others [30,75,78].
The thumb and thenar muscles play a vital role in hand dexterity (Figure 1b). The thumb is opposable, meaning it can move in opposition to the other fingers, allowing for precision grasping and manipulation of objects. This opposable movement is made possible by the coordinated action of the thenar muscles, which control the movements of the thumb. The thumb is the primary contributor to hand motion, achieving nearly 40% of overall hand movements [79]. The thenar muscles (Figure 1b) are significant for thumb movement and essential for various daily actions, such as grasping, gripping, pinching, clamping, twisting, and tying [4,80]. First, the thumb can oppose the other four fingers, which is a prerequisite for grasping objects [30]. Second, the thumb can simultaneously translate, rotate, and flex, which is an ability that other primate hands are incapable of [81]. During the object-grasping process, the thumb adapts its position based on the shape of the object. This necessitates the carpometacarpal (CMC) joint of the thumb to perform not only flexion and extension motions but also abduction and adduction movements. These various thumb motions enable different types of grasping and pinching actions [82].
Despite the importance of the thumb, its motion differs significantly from that of the other fingers, and research on the mechanical design of the thumb is limited [83]. Many modular designs still treat the thumb the same as the other fingers, merely placing it in an opposing configuration [84], severely limiting its functionality and affecting the grasping ability of the entire hand. Recently, studies have revealed a flexible thumb with an active thenar, improving the grasping ability of the soft hand [4]. However, practical considerations, such as space constraints for actuators weight and cost limitations, challenge the implementation of multiple DOFs in the thumb.
Previous research has often overlooked the functionality of the palm, focusing primarily on soft finger designs. The hand muscles facilitate palm flexibility, allowing it to bend and form a concave shape, which is essential for grasping objects. The three key arches, namely the longitudinal, distal transverse, and oblique, achieve dexterous palm motion (Figure 1b) [85]. Many soft, humanoid, dexterous hands replace the palm with rigid materials, lacking actuation and limiting their grasping and manipulation capabilities. To address this, researchers have incorporated flexible actuators into the palm, enabling active palm-like functions. For instance, Wang et al. proposed an active palm with pneumatic actuation to enhance hand dexterity [78,86], while the RBO Hand 2 and 3 feature activatable anthropomorphic palms [6,52]. Experimental results have demonstrated that the active palm in anthropomorphic hands is a key factor in improving the performance of thumb opposition and envelope grasping [75,87]. A comparison of the main features of various types of soft hands is presented in Table 1. Examples of nonanthropomorphic grippers, underactuated anthropomorphic hand, and anthropomorphic dexterous hand are presented in Figure 2.
2.4. Methods to Enhance Grasping and Manipulation Performance
To augment the functionality of soft humanoid hands, researchers have embarked on innovative design explorations aimed at expanding their application range while concurrently enhancing their flexibility. A predominant challenge in the realm of soft hands is their limited output force [92,93]. This constraint directly impacts their carrying capacity, narrowing the spectrum of objects they can effectively grasp and thereby curtailing their application breadth. To address this, the integration of variable stiffness elements into soft hand systems has been proposed [94,95]. Activating these elements increases the structural stiffness of the gripper, aiding in the lifting of heavier loads. These elements bolster the carrying capacity of soft hands without markedly compromising their compliance and adaptability when inactive. Particle jamming is the most popular approach for variable stiffness due to its safety and easy availability [96,97]. Other variable stiffness methodologies include interference-based methods [98], motor-based methods [99], variable modulus-based methods [100], electromagnetic field-based methods [101], and phase change material-based methods [102]. Examples of stiffness-based methods to adjust grasping and manipulation performance are presented in Figure 3.
Despite these improvements, the maximum carrying capacity of soft hands still lags behind that of conventional rigid grippers and human hands. For instance, the pneumatic soft hand integrated with shape memory polymers, as developed by Zhang et al., can lift a 1.5 kg dumbbell using three fingers—a notable achievement for a soft gripper, yet still trailing behind its rigid counterparts (Figure 3d) [103]. The incorporation of variable stiffness elements often introduces additional actuation methods and elongates response times.
In recent years, some researchers have explored the fusion of rigid and soft structures, employing collaborative mechanisms to synergize their respective advantages, thereby achieving superior overall performance [32]. These rigid–soft coupling designs have demonstrated significant promise. For example, the single–stable rigid–soft coupling gripper proposed by Tang et al. can securely grasp an egg and stably lift an 11.4 kg dumbbell [104]. Liu et al. developed flexible hybrid pneumatic actuators for soft hand fingers [32]. The soft humanoid hand exhibits satisfactory comprehensive performance, including fast response, substantial grasping force, affordability, lightweight construction, and ease of fabrication and repair. Nonetheless, these designs confront challenges, such as not fully capitalizing on the high output force of rigid structures and the flexibility of soft structures. Moreover, current rigid–soft coupling gripper designs often feature complex and bulky structures, which substantially limit their compliance. Furthermore, high stability is imperative for robotic grippers, as soft robotic grippers frequently undergo deformation or prolonged vibrations due to external forces like gravity or impact, potentially impairing their operational efficiency and precision.
Environments containing lubricants like water or oil can significantly impact the performance of soft humanoid hands. The frictional interaction in such scenarios is crucial for stable grasping. Human fingertip skin undergoes various degrees of stretching during object grasping, with ridge patterns bolstering grasping ability [105,106]. In wet environments, fingerprints enhance the grasping area, thereby improving the success rate [107,108]. This mechanism differs from the adhesive effect observed in tree frog toe pads or gecko claw setae, which operate at the nanoscale [109,110]. Human fingerprints are relatively macroscopic and primarily leverage frictional changes to augment grasping. Hao et al. indicated that fingerprint-like surface textures significantly enhanced the pinch-grasping ability of soft humanoid hands in water and oil lubrication conditions, surpassing the performance on smooth surfaces [111]. Additionally, applying a concentrated hyaluronic acid solution to the surfaces with fingerprint-like textures enabled the soft hand to grasp a variety of common medical instruments, marking a substantial improvement over smooth surfaces [112].
3. Tactile Perception
A dexterous anthropomorphic hand lacking a tactile sensing feedback mechanism falls short in comparison to the human hand, as it lacks the ability to autonomously interact with its surroundings. This deficiency hinders the ability to regulate contact forces accurately and execute precise manipulations [113]. Substantial efforts have been directed towards developing external perception capabilities for soft hands, with tactile sensing identified as crucial for safe, refined object grasping and manipulation [114,115].
Human interaction with the external world is largely mediated through tactile sensing via the skin, the body’s largest sensory organ, which plays a vital role in survival, exploration, and response. Similarly, tactile sensors are fundamental in robotics for achieving biomimetic sensing and intelligent interaction. These sensors convert tactile stimuli into electrical signals that computers can interpret, holding significant potential in human healthcare, biomimetic robotics, and human–machine interaction. Recent advancements in flexible electronics and nanotechnology have facilitated the development of tactile sensing technologies that emulate human skin characteristics [116,117]. Embedded sensors and electronic skin (e-skin) are the most applied strategies to provide tactile perception for soft hands. Several examples of soft hands with integrated perception devices are presented in Figure 4.
3.1. Embedded Sensors
A direct approach to equipping soft hands with perception capabilities involves the integration of sensors onto their surfaces. By incorporating sensors directly onto the soft hand’s exterior, it becomes possible to capture and interpret information about the surrounding environment. These sensors can be designed to detect various stimuli such as pressure, temperature, proximity, and even tactile feedback. For instance, tactile sensors have been integrated into soft actuators and hands for closed-loop control of grasping force [30,126], while novel soft sensors capable of detecting contact forces and object curvature during grasping have been proposed [127]. Stretchable optical waveguides were applied as embedded sensors in a soft hand system to detect curvature, elongation, and force during object interaction (Figure 4a) [118]. These optoelectronic strain sensors, characterized by ease of fabrication, chemical inertness, and low hysteresis, exhibited the ability to act as multi-modal sensors. Zhou et al. designed and developed a soft hand equipped with soft sensors using 3D printing for effective integration with myoelectric control systems (Figure 4d) [121].
Marasco et al. developed an artificial sense of touch in a prosthetic hand equipped with pressure sensors, providing sensory feedback via a force-tactile interface [128]. This interface stimulated sensory nerves redirected to the residual limb skin, applying proportional pressure. Additionally, e-skin can enhance prosthetics by providing sensory perception and interaction capabilities for amputees and individuals with nerve damage (Figure 4e) [21].
A novel dual-purpose design has been proposed for soft fingertips to enhance the performance of in-hand manipulation, presenting a unique approach for delicately manipulating soft objects without causing damage [129]. This innovative approach involves equipping robotic hands with soft fingertips that possess both tactile sensing and active shape-changing capabilities. By actively manipulating embedded air cavities, the fingertip demonstrates precise control over the in-hand manipulation of soft objects, aided by pressure feedback control. Additionally, a 3D-printed soft hand has been developed for prosthetic applications, incorporating flexible sensors that are compatible with advanced myoelectric control systems such as pattern recognition control and simultaneous proportional control [130]. By integrating flexible position sensors into the fingers, the hand can monitor finger positions to prevent self-collisions, enabling smoother and more intuitive transitions between gestures. The combination of various gestures empowers the hand to perform multi-stage grasping and manipulate multiple objects simultaneously.
For sophisticated manipulation, it is necessary to recognize the object pose estimation observed in human fingers, which remains a fundamental yet challenging task in robotics. Various methods for hand pose estimation (HPE) [131] and hand–object pose estimation (HOPE) [132,133] have been developed, mirroring human manipulation strategies. Inspired by proprioception, embedded cameras and deep learning architectures have been proposed for object recognition in soft fingers. Liu et al. presented a soft finger design that integrated inner vision and kinesthetic sensing to replicate the object pose estimation capabilities observed in human fingers [115]. The proposed framework offered a comprehensive solution that leveraged raw images captured by the soft fingers to estimate the pose of objects held in the hand, providing an end-to-end approach for accurate object pose estimation. The framework underwent testing with seven objects, achieving impressive results with a pose error of 2.02 mm, an angle error of 11.34 degrees, and a classification accuracy of 99.05%.
While these solutions demonstrate the advantages of tactile feedback in soft grippers or fingers, they primarily provide aggregated contact information. Distributed sensor networks can offer spatial information on contact events for a more comprehensive understanding. He et al. proposed a soft hand featuring an active palm and tactile perception enabled by distributed pneumatic sensors integrated into both the palm and fingers [30]. Leveraging multi-material 3D printing enabled the direct printing of tactile sensors on the hand, contrasting with traditional tactile methods that necessitated separate attachment as part of multiple fabrication steps. The presented hand successfully executed 32 out of 33 Feix taxonomic parameters for grasping and all 11 Kapandji tests.
3.2. Electronic Skin (E-Skin)
The concept of electronic skin (e-skin) draws inspiration from the sensory and mechanical properties of human skin, with the goal of developing electronic devices that can emulate these characteristics. E-skin is designed to replicate the functionalities of human skin, such as the ability to sense touch, temperature, pressure, and even humidity. The development of e-skin involves the integration of various components, including flexible and stretchable materials, sensors, and electronic circuits. These components work together to create a thin and flexible electronic layer that can be applied to the surface of objects or even directly onto the human body. E-skin, with capabilities comparable or superior to human skin, holds vast potential in fields such as intelligent robotics [134,135], wearable medical devices [136,137], and human–machine interaction [122,138]. Additionally, e-skin can enhance prosthetics by providing sensory perception and interaction capabilities for amputees and individuals with nerve damage [21].
Although various e-skins have been developed for monitoring pressure, recent innovations involve integrating individual tactile force sensor pixels into soft robots. Yamaguchi et al. developed a soft hand with integrated e-skin pressure sensors, focusing on incorporating strain effects into both the sensor and the overall mechanical system [116]. The e-skin demonstrated insensitivity to strain resulting from structural actuation bending and the ability to monitor tactile force distribution when the soft hand held an object.
Monitoring object sliding within the soft hand is helpful for grasping by providing feedback to adjust the gripping force and prevent slippage. While human hands naturally possess this capability, current robotic hands lack it because of the complexity of integrating various sensors. Successful integration can significantly improve the performance of soft hands in human–machine interaction applications. Two primary challenges arise: integrating sensors into high-strain areas without compromising dexterity and optimizing sensor sensitivity to match execution force and detectable threshold pressure during object contact. Soft hands integrated with e-skin can monitor tactile pressure, temperature, and sliding during grasping and manipulation without compromising robot softness [113]. In addition to force measurement, object sliding or slipping when held in a soft hand can be tracked by assessing the time delay of the tactile forces detected by the sensor array.
Advancements have produced e-skins capable of sensing temperature, pressure, vibration, and strain, similar to human skin [139]. Various biomimetic strategies have been employed, utilizing interlocked microstructures combined with resistive [140], piezoresistive [141], ferroelectric [142], triboelectric [143], or capacitive [144] sensor arrays. These multifunctional sensing systems improve sensitivity, reduce response time, and increase linearity to enhance the perception performance of soft hands. However, distinguishing different stimuli remains challenging since soft hands are exposed to various objects in different posture and loading conditions.
3.3. Multi-Dimensional Force Perception and Multi-Modal Sensing
Human skin can perceive tactile stimuli, distinguish between normal and shear forces, and discern the temperature, hardness, and roughness of touched objects [145]. Emulating this capability in humanoid dexterous hands is crucial for accurately identifying grasped objects using tactile feedback from sensors. Therefore, designing tactile sensors with 3D force perception and multi-modal sensing capabilities is significant for enhancing the intelligence of dexterous hands. Tactile sensing is essential for providing contact properties such as pressure, motion direction, location, and surface hardness/texture [146]. This is particularly important for anthropomorphic soft hands, where internal torque sensing may not always be feasible due to design or cost constraints. The real-time measurement and differentiation of force direction, typically normal and shear, are necessary for providing slip and surface property information, which cannot be obtained through traditional wrist force, torque, and proprioceptive sensors in current robots [147]. To achieve the advanced capability, array based and multi-layer based design strategies (Figure 5) are usually applied in conjunction with carefully functional materials.
Boutry et al. developed an e-skin capable of differentiating normal and shear forces by mimicking the mechanism of the ridges and mechanoreceptors in human skin (Figure 4b) [123]. The capacitor sensor array was sensitive to various stimuli, and the nature of unknown stimuli could be assessed by analyzing recorded signals against a known library of stimulus–response curves. This capability was realized via a 3D configuration that replicated the interconnected dermis–epidermis interface present in human skin. Nevertheless, these systems come at the expense of hand manipulation speed and controller performance. Qu et al. proposed the utilization of a flexible triboelectric tactile sensor (FTTS) designed in a shape inspired by human fingerprints (Figure 4i) [148]. This sensor incorporated eutectic gallium–indium (EGaIn) liquid metal and silicone materials, operating on the principle of a triboelectric nanogenerator. Using three independent fingerprint-like channels, the sensor could detect pressure intensity and position, simulate passwords, identify materials, and monitor pulses.
Recent popular strategies for fabricating multi-modal tactile sensors include integrating various sensing units [150,151], designing sensing arrays to decouple different types of signals [149,152], developing novel materials able to simultaneously sense multiple stimuli [153,154], and designing novel structures [143,148]. Multi-modal sensors with decoupled sensing mechanisms allow a robotic hand to acquire more precise tactile information about the target object, facilitating dexterous object manipulation. They also benefit accurate real-time health monitoring, allowing for the separate measurement of different physiological parameters, such as body temperature and movement, improving the accuracy and safety of the human–machine interface. Mechanism and output signals of flexible sensors with capabilities of multi-dimensional force perception or multi-modal sensing are summarized in Table 2. Future robots will require complex feedback regarding force, temperature, and touching surface properties for tasks trivial to humans, like grasping a glass or inserting a key into a lock, making the development of a multi-modal complex sensing system an inspiring and challenging research topic.
The advancement of flexible electronics and nanotechnology has driven the development of tactile sensing technology, enabling it to simulate the characteristics of human skin. Tactile sensors are crucial for robots to achieve biomimetic perception and intelligent interaction, as they convert tactile stimuli into signals that computers can interpret. This technology has the potential to play an important role in human healthcare, biomimetic robots, and human–computer interaction. Soft humanoid hands require tactile perception to ensure safe interaction with objects. By using integrated tactile sensing devices, soft hands can perceive and interpret tactile information of external objects to adjust contact forces and perform precise operations. This not only improves the interaction ability between the soft hand and the environment but also increases the flexibility and reliability of the robot in various tasks. The further development of tactile sensing technology will further enhance the application potential of soft humanoid hands in fields such as medical rehabilitation, home services, and industrial flexible grasping.
4. Machine Learning in Soft Hands
The remarkable adaptability of the human hand in performing various tasks is a testament to its biomechanical superiority, which is further enhanced by the learning and memory mechanisms of the nervous system. The goal of the next generation of soft hands is to achieve the capability of accurately perceiving their surroundings and making correct decisions in response to stimuli. The advancements in machine learning have proven beneficial in increasing the accuracy of tactile perception and improving the decision-making strategies of soft hands during their interactions with different environments. Similarly, soft hands can benefit from incorporating machine learning techniques to address their inherent limitations, such as nonlinearity and hysteresis, which arise from structural compliance, material viscoelasticity [166,167], inconsistent outputs during loading and unloading cycles, and higher complexity due to factors like drift and high DOFs, challenging the mathematical modeling and calibration of soft hands and grippers.
Machine learning, renowned for its efficacy in solving nonlinear problems across various fields [168,169], has been increasingly applied to soft robotics. Its applications extend to soft sensor calibration [170,171], soft actuator position control [172,173], and more intricate tasks like grasping and in-hand manipulation [69]. Research indicates that machine learning-based methods have successfully mitigated many of the current challenges faced by soft robotic hands. A schematic of an anthropomorphic soft hand interacting with the environment with machine learning models is presented in Figure 6.
Sensor calibration aims to accurately estimate physical stimuli, such as strain and stress, from soft sensor inputs, such as resistance and capacitance, using the Artificial Neural Network (ANN), k-nearest Neighbors (kNN), Recurrent Neural Network (RNN) [167,174], Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) models. These models are widely applied to evaluate grasping information, such as object recognition, grasping stability, and hand pose [175]. To address the hysteresis characteristics in output signals, both mathematical models and data-based methods have been used for optimization [176]. Data-based approaches often employ machine learning algorithms and convex optimization to fine-tune hysteresis model parameters [167]. Luo et al. designed a bioinspired soft sensor array (BOSSA) via theoretical and experimental investigations into the triboelectric effect and cascaded electrodes [177]. Using a data-driven algorithm, multilayer perceptron, the BOSSA was capable of environmental self-awareness, with pressure- and material-sensing abilities. Deep learning, well-established in fields like computer vision and natural language processing [178], has also been applied to soft sensor research. For instance, Navarro et al. utilized feed-forward neural networks and transfer learning to calibrate soft pneumatic mechanosensors, comparing results with those from numerical methods like the finite element method [179].
Inputs from sensors are processed by machine learning models, usually the feed-forward neural network (FNN), RNN, and CNN, to estimate pose/position and to generate control strategies for the proprioception and motion control of soft grippers and hands [22,180]. Machine learning has also been useful in soft robotics for generating grasping poses for unknown objects [181,182]. Demonstration learning has been combined with reinforcement learning to transfer grasping capabilities from human operators to robotic systems [183]. Dexterous robotic hands with multiple fingers are capable of an extensive range of actions, and their morphological resemblance to the human hand presents significant potential for expediting robot learning. Mandikal et al. introduced an innovative method for acquiring proficiency in robotic grasping by leveraging human–object interactions available in openly accessible videos [184]. Using deep reinforcement learning, it can easily scale to new objects without collecting human demonstrations. Learning-based techniques have also facilitated the transfer of human-operated soft hand response strategies to robots for grasping new items in human–robot handover scenarios [185]. These advancements highlight the synergy between machine learning methods and the inherent adaptability of soft hands to unknown environments.
A deep learning-based method has been developed to address the challenge of predicting whether the grasp will be successful in soft hands [186]. This framework utilizes two neural architectures: a classifier for the a-posteriori detection of failure events and a predictor that uses readings from Inertial Measurement Units (IMUs) to estimate object sliding. By leveraging these neural architectures, the framework can effectively anticipate grasp failures in soft hands before they occur. Santina et al. proposed a strategy that mimics human motion to achieve autonomous soft hand grasping [182]. This approach employs a deep neural network classifier to analyze the visual information of the object to be grasped. Based on this analysis, the classifier predicts the action that a human would likely perform to achieve the desired goal. The predicted action is then used to select one of several human-inspired primitives, combining anticipatory actions with touch-based reactive grasping. Research on machine learning related to anthropomorphic soft hands is shown in Table 3.
This section demonstrates how soft hands can partially replicate human-like movements in grasping tasks by imitating the shape, structure, and function of human hands, providing flexibility and operability. The article predicts that these soft hands, designed to mimic human dexterity, will become the focus of robot hand development and have broad market prospects in industrial flexible grip solutions, medical rehabilitation, home services, and other fields. Future advancements may stem from further sensor and actuator integration, aiming to develop soft hands that can rival the capabilities of biological organisms.
5. Outlook
5.1. Potential Applicaitons
In the field of healthcare, the application of the anthropomorphic soft hand is very extensive. For example, in surgery, soft robotic arms can cooperate with doctors to perform minimally invasive surgery, allowing for more precise operations and reducing harm to patients. In addition, soft robotic arms can also be applied in rehabilitation assistive devices to assist rehabilitation patients in self-care training or provide more intelligent assistive tools for disabled individuals to improve their quality of life.
In industrial production, anthropologic soft hands can collaborate with human workers to complete the assembly work of small and vulnerable parts. This type of soft robotic arm has a strong adaptability to meticulous operations, which will be of great help in the production of electronic components, medical devices, and other products that require careful operation. In addition to the production field, soft robotic arms can also be applied in hazardous environments, such as radioactive areas or chemical processing, to avoid direct human operations in high-risk environments.
In the field of personal assistance, the anthropomorphic soft hand also has broad application prospects. For example, it can be applied to assist elderly people in their daily lives, performing simple and common actions such as picking up items, wiping the table, etc. In addition, soft robotic arms can also be designed to take care of people with disabilities, such as helping them complete self-care activities, and even engaging in intelligent communication and interaction.
Overall, the application of the anthropomorphic soft hand in fields such as healthcare, industry, and personal assistance will have a profound impact in practical scenarios. It will improve work efficiency, reduce human errors, promote a safer working environment, and improve quality of life in the fields of healthcare and personal assistance. The development of soft robotic arms will strongly promote the advancement of robotics technology and become one of the key technologies in the future.
5.2. Challenges and Future Directions
As the end effector, robotic hands determine essential functions for the robotic system, like grasping and in-hand manipulation. Soft, dexterous hands adapt actively or passively to environmental changes, compensating for rigid hand limitations. These grippers and hands excel in adaptability and interaction, executing tasks gently and securely, even with delicate items, in unstructured settings. The goal of robotic gripper design is to replicate human-inspired dexterity for autonomous object manipulation. Yet, despite the advantages of soft, human-like, dexterous hands, they still present significant disparities and limitations compared to their human counterparts. Therefore, addressing these challenges during continuous soft-hand development is a priority.
Most underactuated soft grippers and hands typically exhibit a single grasping mode when handling objects of varying sizes. The dependability of grasping diverse types of objects, particularly small ones, is frequently limited by the mismatched or insufficient contact area. In addition, underactuated hands often lack sensing capabilities, which means their anti-interference ability during the grasping process is a concern.
Dexterous soft hands with multiple joints and 20 or more DOFs require complex mechanical structures, presenting a significant challenge for design because of the compact, narrow digit space, especially the thumb. This scenario is exacerbated by integrating a considerable number of sensing units into the main configuration. Researchers face challenges in the industrialization and commercialization of dexterous soft hands due to the trade-off between their overall performance and the time and cost of design.
Compared to rigid and human hands, the load capacity of current soft hands is relatively low, hindering more extensive application. Although development in material science provides potential solutions to this problem, further investigation into improved variable stiffness methodologies to enhance grasping performance is necessary. In addition, adding friction layers with specially designed microstructures may also contribute to stable grasping in various environments.
Environmental perception plays a pivotal role in manipulation tasks. Several tactile sensors have been developed for specific parameters, demonstrating high performance comparable to or even surpassing that of human skin. Nevertheless, creating tactile sensors that encompass all the properties of human skin remains a formidable challenge. The development of enhanced decoupling mechanisms and methods is vital for multi-module sensors to ensure the production of unaltered signals and the accurate restoration of stimuli.
Machine learning has been employed to capture human gestures/poses and control soft hands to achieve object grasping and in-hand manipulation. While numerous promising results have been reported, several challenges persist, including the proper training of general machine learning models and addressing the nonlinearities inherent in soft systems. Overcoming hurdles, such as the need for a substantial amount of data, unexpected error sources, and the necessity for real-time measurements and controls, is crucial for further advancements in this field. To complete tasks in unknown or complex environments, a soft robotic arm requires a high level of perception ability and intelligent decision-making systems. However, embedding highly integrated sensing systems and intelligent algorithms into software materials to achieve the environmental perception, data processing, and decision execution functions of robotic arms is a complex engineering task. Among them, the ability to process a large amount of sensor data in real-time and make rapid and accurate responses is currently the bottleneck of technological development.
Furthermore, a commonly overlooked issue is the fabrication errors associated with soft hands. During soft hand fabrication, 3D technology is commonly used for casting. However, the fabrication accuracy falls short when compared to rigid counterparts. These manufacturing flaws significantly reduce the deformation, bending, and output force stability of anthropomorphic soft hands. Due to the close collaboration between soft robotic arms and human workers, they must have extremely high safety performance to avoid harm to people or objects during operation. However, ensuring the reliability and low failure rate of soft robotic arms, especially when experiencing frequent deformation or contact with different objects and surfaces, remains a challenge in technological development. The wear and tear, aging, and maintenance strategies of soft robotic arms are also current issues that need to be overcome.
The activities of soft robotic arms typically require external energy sources, such as pneumatic or hydraulic systems, which are often bulky and inconvenient to carry. This greatly limits the application of soft robotic arms in situations where there is no fixed energy supply point. Meanwhile, the energy conversion efficiency of soft robotic arms is low when undergoing multiple bending and stretching movements, which may lead to excessive energy consumption, thereby limiting their continuous working time and practicality.
With the continuous progress of robot technology, it is expected that future robot soft hands will integrate more advanced multimodal perception systems and incorporate innovative materials and intelligent structures to improve load capacity and adaptability to variable stiffness. In addition, new design and manufacturing technologies will greatly improve the performance of soft hands and reduce production errors. By utilizing advanced machine learning and adaptive control algorithms, soft hands will be able to simulate human hand movements more naturally and be applied in increasingly expanding fields such as advanced manufacturing, service robots, medical assistance, and even disaster response scenarios. At the same time, the sustainability and maintenance issues of soft hand design will also be given attention, and improvements in user interface and interaction performance will make soft hands more user-friendly. These innovations will not only greatly expand the application scope of robotic hands, but also have the potential to change the way humans and robots interact, improving the efficiency and safety of human–machine cooperation. In the future, robot soft hands are expected to achieve more precise operations, unlock new application prospects, and become indispensable assistants in human life and work.
Conceptualization, H.X.; writing—original draft preparation, Y.W. and Y.L.; writing—review and editing, T.H., H.X., S.L. and H.Z. All authors have read and agreed to the published version of the manuscript.
No new data were created or analyzed in this study. Data sharing is not applicable to this article.
The authors declare no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 1. Schematic expression of (a) human hand joints; (b) and muscle groups and palm arches.
![View Image - Figure 2. Development of various types of soft hands. Top: nonanthropomorphic grippers, (a) starfish-like grippers [2], (b), soft gripper for both safe grasping and non-destructive firmness evaluation [88] (c), middle, underactuated anthropomorphic hand, (d), UC soft hand with compact twisted string actuation mechanism [89], (e), soft hand with magnets to robustify the performance [90] (f), soft glove with four kinds of rehabilitation postures [91], and bottom: anthropomorphic dexterous hand (g), BCL-26 soft hand with 16 DOFs [5], (h), RBO Hand 3 with a dexterous opposable thumb [6] (i), soft hand with active palm [75].](https://media.proquest.com/media/hms/THUM/4/JRFiX?_a=ChgyMDI2MDQxOTEyNTk1OTQyMTozNDAwNTESBzEzODMzMjMaCk9ORV9TRUFSQ0giDjIxNi43My4yMTYuMTg1KgcyMDMyNDQ0MgoyOTg3MDQ2NDk0OgtJbmxpbmVJbWFnZUIBMFIGT25saW5lWgNJTEliBFRIVU1qCjIwMjQvMDEvMDFyCjIwMjQvMTIvMzF6AIIBIFAtMTAwMDA4NC0yMDEzOTUtU1RBRkYtbnVsbC1udWxskgEGT25saW5lygFnTW96aWxsYS81LjAgQXBwbGVXZWJLaXQvNTM3LjM2IChLSFRNTCwgbGlrZSBHZWNrbzsgY29tcGF0aWJsZTsgQ2xhdWRlQm90LzEuMDsgK2NsYXVkZWJvdEBhbnRocm9waWMuY29tKdIBElNjaG9sYXJseSBKb3VybmFsc5oCB1ByZVBhaWSqAhtPUzpFTVMtRnVsbFRleHQtZ2V0RnVsbFRleHTKAg9BcnRpY2xlfEZlYXR1cmXiAgFO8gIA%2BgIBWYIDA1dlYooDHENJRDoyMDI2MDQxOTEyNTk1OTQyMTo4ODEyNTg%3D&_s=wLv1omxDlpBlF5NR5o5dhLPvkoY%3D "View Image - Figure 2. Development of various types of soft hands. Top: nonanthropomorphic grippers, (a) starfish-like grippers [2], (b), soft gripper for both safe grasping and non-destructive firmness evaluation [88] (c), middle, underactuated anthropomorphic hand, (d), UC soft hand with compact twisted string actuation mechanism [89], (e), soft hand with magnets to robustify the performance [90] (f), soft glove with four kinds of rehabilitation postures [91], and bottom: anthropomorphic dexterous hand (g), BCL-26 soft hand with 16 DOFs [5], (h), RBO Hand 3 with a dexterous opposable thumb [6] (i), soft hand with active palm [75].")
Enlarge this image.Figure 2. Development of various types of soft hands. Top: nonanthropomorphic grippers, (a) starfish-like grippers [2], (b), soft gripper for both safe grasping and non-destructive firmness evaluation [88] (c), middle, underactuated anthropomorphic hand, (d), UC soft hand with compact twisted string actuation mechanism [89], (e), soft hand with magnets to robustify the performance [90] (f), soft glove with four kinds of rehabilitation postures [91], and bottom: anthropomorphic dexterous hand (g), BCL-26 soft hand with 16 DOFs [5], (h), RBO Hand 3 with a dexterous opposable thumb [6] (i), soft hand with active palm [75].
![View Image - Figure 3. Rigid−flexible integration and variable stiffness design of soft grippers and hands. (a) Rigid−flexible integration configuration [33], (b), integration of shape memory polymer for variable stiffness [103] (c), particle jamming method for two fingered−gripper (d), particle jamming method for three−fingered gripper [96].](https://media.proquest.com/media/hms/THUM/6/JRFiX?_a=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%2BgIBWYIDA1dlYooDHENJRDoyMDI2MDQxOTEyNTk1OTQyMTo4ODEyNTg%3D&_s=uCjxueseBxkrg%2FrVWoj8nbe%2Bvm8%3D "View Image - Figure 3. Rigid−flexible integration and variable stiffness design of soft grippers and hands. (a) Rigid−flexible integration configuration [33], (b), integration of shape memory polymer for variable stiffness [103] (c), particle jamming method for two fingered−gripper (d), particle jamming method for three−fingered gripper [96].")
Enlarge this image.Figure 3. Rigid−flexible integration and variable stiffness design of soft grippers and hands. (a) Rigid−flexible integration configuration [33], (b), integration of shape memory polymer for variable stiffness [103] (c), particle jamming method for two fingered−gripper (d), particle jamming method for three−fingered gripper [96].
![View Image - Figure 4. Tactile perception of anthropomorphic soft hands with embedded sensor (a), stretchable optical waveguides-based sensor [118] (b), capacitive sensor [119] (c), flexible optical fiber-based sensor [120] (d), air pressure sensors [121] (e), distributed soft sensing unit [30] and e-skin (f), e-skin for both pressure sensing and actuation [122] (g), capacitive e-skin for detecting normal and tangential forces [123] (h), mechanoreceptor- and nociceptor-based e-skin for neuromorphic tactile perception [124] (i), fingerprint inspired triboelectric e-skin for texture perception [125]).](https://media.proquest.com/media/hms/THUM/8/JRFiX?_a=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%2BgIBWYIDA1dlYooDHENJRDoyMDI2MDQxOTEyNTk1OTQyMTo4ODEyNTg%3D&_s=azc9I6MKE4oMn2cZZazCCNIGgNs%3D "View Image - Figure 4. Tactile perception of anthropomorphic soft hands with embedded sensor (a), stretchable optical waveguides-based sensor [118] (b), capacitive sensor [119] (c), flexible optical fiber-based sensor [120] (d), air pressure sensors [121] (e), distributed soft sensing unit [30] and e-skin (f), e-skin for both pressure sensing and actuation [122] (g), capacitive e-skin for detecting normal and tangential forces [123] (h), mechanoreceptor- and nociceptor-based e-skin for neuromorphic tactile perception [124] (i), fingerprint inspired triboelectric e-skin for texture perception [125]).")
Enlarge this image.Figure 4. Tactile perception of anthropomorphic soft hands with embedded sensor (a), stretchable optical waveguides-based sensor [118] (b), capacitive sensor [119] (c), flexible optical fiber-based sensor [120] (d), air pressure sensors [121] (e), distributed soft sensing unit [30] and e-skin (f), e-skin for both pressure sensing and actuation [122] (g), capacitive e-skin for detecting normal and tangential forces [123] (h), mechanoreceptor- and nociceptor-based e-skin for neuromorphic tactile perception [124] (i), fingerprint inspired triboelectric e-skin for texture perception [125]).
![View Image - Figure 5. Array-based or/and multi-layer based design strategy for multimodal soft sensors. (a) Fingerprint-inspired array-based e-skin for pressure and position detection (Highlighted area indicates corresponding COMSOL simulation of forces applied) [148] (b), soft senor with both array and multi-layer structure for three dimensional force, slip, and temperature perception [149].](https://media.proquest.com/media/hms/THUM/10/JRFiX?_a=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%2BgIBWYIDA1dlYooDHENJRDoyMDI2MDQxOTEyNTk1OTQyMTo4ODEyNTg%3D&_s=IbBDuupK5GgO7cDvD4xpHzU22p8%3D "View Image - Figure 5. Array-based or/and multi-layer based design strategy for multimodal soft sensors. (a) Fingerprint-inspired array-based e-skin for pressure and position detection (Highlighted area indicates corresponding COMSOL simulation of forces applied) [148] (b), soft senor with both array and multi-layer structure for three dimensional force, slip, and temperature perception [149].")
Enlarge this image.Figure 5. Array-based or/and multi-layer based design strategy for multimodal soft sensors. (a) Fingerprint-inspired array-based e-skin for pressure and position detection (Highlighted area indicates corresponding COMSOL simulation of forces applied) [148] (b), soft senor with both array and multi-layer structure for three dimensional force, slip, and temperature perception [149].
Enlarge this image.Figure 6. Schematics of an anthropomorphic soft hand interacting with the environment with machine learning models.
Summary of anthropomorphic soft hands.
| Materials | Fabrication | Actuation | Control | DOF | Main Features | Category | Year | Ref |
|---|---|---|---|---|---|---|---|---|
| Dragon skin-10, Dragon skin-20, Dragon skin-30, Ecoflex 00-10 | Casting molding | Pneumatic | Closed-loop control | 14 | Flexible thenar | Anthropomorphic dextureous hand | 2022 | [ |
| Dragonskin 10, Ecoflex 00-30 | Casting molding | Pneumatic | coordinated control | 6 | Replicate the human-like grasp postures | Anthropomorphic dextureous hand | 2022 | [ |
| Silicone rubber, fibers | Casting molding | Pneumatic | Open-loop control | 12 | Flexible palm | Underactuated anthropomorphic hand | 2013 | [ |
| Dragon skin 30 | Casting molding | Open-loop control | 21 | Soft parallel palm | 2023 | [ | ||
| Ecoflex 00-50, Mold Star 30, ABS | Casting molding | Pneumatic | Open-loop control | 13 | Grasping planning | Anthropomorphic dextureous hand | 2018 | [ |
| Dragon Skin 10, fibers | Casting molding | Pneumatic | Open-loop control | 22 | Flexible operation function | Anthropomorphic dextureous hand | 2022 | [ |
| TPU, ABS | — | Fluidic and Tendon actuation | Open-loop control | 5 | SMA-Based Exo-Glove | Soft Exo-Glove | 2023 | [ |
| Resin, PET, Nylon gauze | Planar laser cutting and stacking | Pneumatic | Open-loop control | 6 | Hybrid pneumatic actuators | Underactuated anthropomorphic hand | 2021 | [ |
| VytaFlex 20, ELASTOSIL M 4601 | 3D printing, soft lithography | Pneumatic | Open-loop control | 12 | Multi material 3D printed | Anthropomorphic dextureous hand | 2020 | [ |
| Dragon skin-10, Dragon skin-30, fibers | Casting molding | Pneumatic | Open-loop control | 3 | SMP actuated | Dextureous finger | 2016 | [ |
| Dragon Skin 10, Ecoflex 00-30, nylon thread, fibers | Casting molding | Pneumatic | Open-loop control | 12 | Highly integrated design | Anthropomorphic dextureous hand | 2023 | [ |
| Vero, Agilus30 | 3D printing | Pneumatic | Closed-loop control | 5 | 3D printed fingers | Underactuated anthropomorphic hand | 2021 | [ |
| PDMS, CNTs | 3D printing | Light-driven | Open-loop control | 5 | SMA actuated | Underactuated anthropomorphic hand | 2020 | [ |
| PDMS, SMA, fiberglass | Casting molding | Tendon-driven | Open-loop control | 10 | SMA actuated | Underactuated anthropomorphic hand | 2023 | [ |
| TPU, SMA | 3D printing | SMA | Open-loop control | 10 | Elastic joints and soft pads | Anthropomorphic dextureous hand | 2014 | [ |
| TPU | 3D printing | Pneumatic | Open-loop control | 10 | Soft-Rigid Hybrid fingers | Anthropomorphic dextureous hand | 2023 | [ |
| Silicone rubber, ABS | — | Pneumatic | Open-loop control | 5 | Self-healing soft fingers | Underactuated anthropomorphic hand | 2017 | [ |
| Dragonskin-10, ecoflex 00-30, fibers | Casting molding | Pneumatic | Closed-loop control | - | Deployable, atraumatic grasper | Surgical grasper | 2014 | [ |
| Smooth-Sil 936, fibers | Casting molding | Fluid-driven | Open-loop control | 1 | Pneu-net actuator | Dextureous finger | 2014 | [ |
| Ecoflex-30, SMA, PDMS | Casting molding | SMA | Closed-loop control | 5 | ECF jet | Underactuated anthropomorphic hand | 2011 | [ |
| TPU | 3D printing | Tendon | Open-loop control | 5 | Completely soft | Soft Exo-Glove | 2021 | [ |
| Electro-conjugate fluid(ECF) | — | ECF jet | Open-loop control | 5 | Planar Laser Cutting and Stacking | Underactuated anthropomorphic hand | 2021 | [ |
| NinjaFlex, particles | 3D printing | Pneumatic | Closed-loop control | 3 | Humanoid hand skeleton | Dextureous finger | 2019 | [ |
| Agilus30, Vero | 3D printing | Pneumatic | Open-loop control | 5 | Pneumatic Exo-Glove | Soft Exo-Glove | 2016 | [ |
| Dragon Skin 10, Ecoflex 00-30 | — | Pneumatic | Myoelectric control | 4 | 3D printed | Underactuated anthropomorphic hand | 2017 | [ |
| Dragon skin 30, paper | Casting molding | Pneumatic | Open-loop control | 4 | Dualmodule pneumatic actuator | Nonanthropomorphic Grippers | 2020 | [ |
| Silicone, PLA | Casting molding | Tendon-driven | Open-loop control | 2 | Two-finger grip | Nonanthropomorphic Grippers | 2022 | [ |
| Silicone, fiber | Casting molding | Pneumatic | Open-loop control | 7 | Active Palm | Underactuated anthropomorphic hand | 2016 | [ |
| Vero White, Tango Black | 3D printing | Open-loop control | 18 | Soft-rigid hybrid hand | Anthropomorphic dextureous hand | 2018 | [ | |
| TPU | 3D printing | Pneumatic | Open-loop control | 9 | Hand sign language | Anthropomorphic dextureous hand | 2019 | [ |
| Fiber, M4601, memory foam | Casting molding | hydraulic | Open-loop control | 8 | Underwater gripper | Nonanthropomorphic Grippers | 2016 | [ |
| Tendons, Agilus Black material | 3D printing | Tendon-driven | Open-loop control | 3 | Grasping | Underactuated anthropomorphic hand | 2022 | [ |
| SmoothSil-960, MoldStar-30 | Casting molding | Pneumatic | Open-loop control | 9 | Three-finger grip | Nonanthropomorphic Grippers | 2023 | [ |
| Smooth-Sil 945, Ecoflex 00-30 | Casting molding | Pneumatic | Open-loop control | 8 | Delicate In-hand manipulation | Soft robotic hand | 2020 | [ |
Flexible sensors with capabilities of multi-dimensional force perception or multi-modal sensing.
| Output | Mechanism | E-Skin | Ref |
|---|---|---|---|
| Force, sliding, temperature | Resistant | √ | [ |
| Curvature, elongation, force | Optical waveguides-based | [ | |
| Normal forces, and tangential | Capacitive | [ | |
| Force, texture | Triboelectric | [ | |
| Pressure, position, material | Triboelectric | √ | [ |
| Normal and shear force | Piezoresistive | [ | |
| Temperature, vibration, shear force, normal force | Thermoresistive, piezoresistive, piezoelectric | [ | |
| Strain, temperature | Resistant, thermoresistive | [ | |
| Normal and shear force, sliding | Capacitive, resistant | √ | [ |
| Three-axis force | Resistant | [ | |
| Location, intensity | Piezoresistive | [ | |
| Pressure, sliding | Capacitive, triboelectric | [ | |
| Sliding, pressure | Piezoresistive, triboelectric | √ | [ |
| Sliding, pressure | Piezoresistive, triboelectric | √ | [ |
| Temperature, pressure | Thermoresistive, piezoresistive | [ | |
| Strain, pressure, and temperature | Capacitive, resistant, thermoresistive | [ |
Research on machine learning related to anthropomorphic soft hands.
| Machine Learning Methods | Learning Features | Implemented Functions | Performance Index | Ref |
|---|---|---|---|---|
| ANN | Reducing the dependence on feature engineering | Texture recognition | Accurary: 92.5% to 93.33% | [ |
| RNN | Hierarchical recurrent sensing network | Estimating pressure responses and localizing the position | Accuracy: 85% | [ |
| CNN, RNN | States and actions based on feedback from the environment | Controlling the position of soft robotic arm | Average error: 4.8 mm | [ |
| Bayesian optimization | Performance of IPMC | 3D printing of IPMC actuators and the motion control of the crawling robot | Maximum possible distance:95% | [ |
| DNN | Data collected by probe terminal electrodes | Position recognition and pressure sensing | Accuracy: 98% | [ |
| Policy learning method | High-resolution tactile inputs | Predicting future grasp actions | Accuracy: 76–98% | [ |
| Multilayer perceptron model | Contact signals | Object Recognition of Robot Hands | Accuracy: 98.6% | [ |
| ANN | Changes in airflow, volume changes | Predicting contact location and force magnitude | Average error: 1.94–2.88 mm | [ |
| ANN | RGB images, RGB-D images, or point clouds | Object Pose Estimation for Robotic Grasping | - | [ |
| CNN | RGB images | Generating grasping poses for unknown objects | Accuracy: 81.1% | [ |
| CNN | State information | Flexible operations of RBO Hand 2 | - | [ |
References
1. Rus, D.; Tolley, M.T. Design, fabrication and control of soft robots. Nature; 2015; 521, pp. 467-475. [DOI: https://dx.doi.org/10.1038/nature14543]
2. Ilievski, F.; Mazzeo, A.D.; Shepherd, R.F.; Chen, X.; Whitesides, G.M. Soft robotics for chemists. Angew. Chem.; 2011; 123, pp. 1930-1935. [DOI: https://dx.doi.org/10.1002/ange.201006464]
3. Laschi, C.; Mazzolai, B.; Cianchetti, M. Soft robotics: Technologies and systems pushing the boundaries of robot abilities. Sci. Robot.; 2016; 1, eaah3690. [DOI: https://dx.doi.org/10.1126/scirobotics.aah3690] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33157856]
4. Hao, T.; Xiao, H.; Liu, S.; Zhang, C.; Ma, H. Multijointed pneumatic soft hand with flexible thenar. Soft Robot.; 2022; 9, pp. 745-753. [DOI: https://dx.doi.org/10.1089/soro.2021.0017] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34747642]
5. Zhou, J.; Chen, X.; Chang, U.; Lu, J.-T.; Leung, C.C.Y.; Chen, Y.; Hu, Y.; Wang, Z. A Soft-Robotic Approach to Anthropomorphic Robotic Hand Dexterity. IEEE Access; 2019; 7, pp. 101483-101495. [DOI: https://dx.doi.org/10.1109/ACCESS.2019.2929690]
6. Puhlmann, S.; Harris, J.; Brock, O. RBO Hand 3: A platform for soft dexterous manipulation. IEEE Trans. Robot.; 2022; 38, pp. 3434-3449. [DOI: https://dx.doi.org/10.1109/TRO.2022.3156806]
7. Zhang, N.; Zhao, Y.; Gu, G.; Zhu, X. Synergistic control of soft robotic hands for human-like grasp postures. Sci. China Technol. Sci.; 2022; 65, pp. 553-568. [DOI: https://dx.doi.org/10.1007/s11431-021-1944-y]
8. Deimel, R.; Brock, O. A compliant hand based on a novel pneumatic actuator. Proceedings of the 2013 IEEE International Conference on Robotics and Automation; Karlsruhe, Germany, 6–10 May 2013; pp. 2047-2053.
9. Gilday, K.; Hughes, J.; Iida, F. Sensing, actuating, and interacting through passive body dynamics: A framework for soft robotic hand design. Soft Robot.; 2023; 10, pp. 159-173. [DOI: https://dx.doi.org/10.1089/soro.2021.0077]
10. Alves, S.; Babcinschi, M.; Silva, A.; Neto, D.; Fonseca, D.; Neto, P. Integrated design fabrication and control of a bioinspired multimaterial soft robotic hand. Cyborg Bionic Syst.; 2023; 4, 0051. [DOI: https://dx.doi.org/10.34133/cbsystems.0051]
11. Wang, Z.; Or, K.; Hirai, S. A dual-mode soft gripper for food packaging. Robot. Auton. Syst.; 2020; 125, 103427. [DOI: https://dx.doi.org/10.1016/j.robot.2020.103427]
12. Schroeffer, A.; Rehekampff, C.; Lueth, T.C. An Automated Design Approach for Task-Specific two Finger Grippers for Industrial Applications. Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO); Dali, China, 6–8 December 2019; pp. 184-189.
13. Mohammadi, A.; Lavranos, J.; Zhou, H.; Mutlu, R.; Alici, G.; Tan, Y.; Choong, P.; Oetomo, D. A practical 3D-printed soft robotic prosthetic hand with multi-articulating capabilities. PLoS ONE; 2020; 15, e0232766. [DOI: https://dx.doi.org/10.1371/journal.pone.0232766]
14. Lai, J.; Song, A.; Shi, K.; Ji, Q.; Lu, Y.; Li, H. Design and evaluation of a bidirectional soft glove for hand rehabilitation-assistance tasks. IEEE Trans. Med. Robot. Bionics; 2023; 3, pp. 730-740. [DOI: https://dx.doi.org/10.1109/TMRB.2023.3292414]
15. Parsa, F.F.; Moghadam, A.A.A.; Stollberg, D.; Tekes, A.; Coates, C.; Ashuri, T. A Novel Soft Robotic Hand for Prosthetic Applications. Proceedings of the 2022 IEEE 19th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET); Marietta, GA, USA, 19–21 December 2022; pp. 1-6.
16. Ahmed, T.A.-O.; Assad-Uz-Zaman, M.; Islam, M.A.-O.X.; Gottheardt, D.; McGonigle, E.; Brahmi, B.A.-O.; Rahman, M.H. Flexohand: A Hybrid Exoskeleton-Based Novel Hand Rehabilitation Device. Micromachines; 2021; 12, 1274. [DOI: https://dx.doi.org/10.3390/mi12111274]
17. Wang, Z.; Hirata, T.; Sato, T.; Mori, T.; Kawakami, M.; Furukawa, H.; Kawamura, S. A soft robotic hand based on bellows actuators for dishwashing automation. IEEE Robot. Autom. Lett.; 2021; 6, pp. 2139-2146. [DOI: https://dx.doi.org/10.1109/LRA.2021.3061063]
18. Milojević, A.; Linß, S.; Handroos, H. Soft Robotic Compliant Two-Finger Gripper Mechanism for Adaptive and Gentle Food Handling. Proceedings of the 2021 IEEE 4th International Conference on Soft Robotics (RoboSoft); New Haven, CT, USA, 12–16 April 2021; pp. 163-168.
19. Mei, D.; Yu, X.; Tang, G.; Liu, S.; Zhao, X.; Zhao, C.; Li, C.; Wang, Y. Blue Hand: A Novel Type of Soft Anthropomorphic Hand Based on Pneumatic Series-Parallel Mechanism. IEEE Robot. Autom. Lett.; 2023; 8, pp. 7615-7622. [DOI: https://dx.doi.org/10.1109/LRA.2023.3320906]
20. Zhou, J.; Yi, J.; Chen, X.; Liu, Z.; Wang, Z. BCL-13: A 13-DOF soft robotic hand for dexterous grasping and in-hand manipulation. IEEE Robot. Autom. Lett.; 2018; 3, pp. 3379-3386. [DOI: https://dx.doi.org/10.1109/LRA.2018.2851360]
21. Gu, G.; Zhang, N.; Xu, H.; Lin, S.; Yu, Y.; Chai, G.; Ge, L.; Yang, H.; Shao, Q.; Sheng, X. A soft neuroprosthetic hand providing simultaneous myoelectric control and tactile feedback. Nat. Biomed. Eng.; 2023; 7, pp. 589-598. [DOI: https://dx.doi.org/10.1038/s41551-021-00767-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34400808]
22. Chin, K.; Hellebrekers, T.; Majidi, C. Machine learning for soft robotic sensing and control. Adv. Intell. Syst.; 2020; 2, 1900171. [DOI: https://dx.doi.org/10.1002/aisy.201900171]
23. Khin, P.M.; Low, J.H.; Ang, M.H., Jr.; Yeow, C.H. Development and grasp stability estimation of sensorized soft robotic hand. Front. Robot. AI; 2021; 8, 619390. [DOI: https://dx.doi.org/10.3389/frobt.2021.619390]
24. Park, J.; Heo, P.; Kim, J.; Na, Y. Qualitative stability analysis of soft hand exoskeleton based on tendon-driven mechanism. Int. J. Precis. Eng. Manuf.; 2020; 21, pp. 2095-2104. [DOI: https://dx.doi.org/10.1007/s12541-020-00383-z]
25. Elango, N.; Faudzi, A.A.M. A review article: Investigations on soft materials for soft robot manipulations. Int. J. Adv. Manuf. Technol.; 2015; 80, pp. 1027-1037. [DOI: https://dx.doi.org/10.1007/s00170-015-7085-3]
26. Coyle, S.; Majidi, C.; LeDuc, P.; Hsia, K.J. Bio-inspired soft robotics: Material selection, actuation, and design. Extrem. Mech. Lett.; 2018; 22, pp. 51-59. [DOI: https://dx.doi.org/10.1016/j.eml.2018.05.003]
27. Schmitt, F.; Piccin, O.; Barbé, L.; Bayle, B. Soft robots manufacturing: A review. Front. Robot. AI; 2018; 5, 84. [DOI: https://dx.doi.org/10.3389/frobt.2018.00084] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33500963]
28. Stano, G.; Percoco, G. Additive manufacturing aimed to soft robots fabrication: A review. Extrem. Mech. Lett.; 2021; 42, 101079. [DOI: https://dx.doi.org/10.1016/j.eml.2020.101079]
29. Zaidi, S.; Maselli, M.; Laschi, C.; Cianchetti, M. Actuation technologies for soft robot grippers and manipulators: A review. Curr. Robot. Rep.; 2021; 2, pp. 355-369. [DOI: https://dx.doi.org/10.1007/s43154-021-00054-5]
30. Shorthose, O.; Albini, A.; He, L.; Maiolino, P. Design of a 3D-printed soft robotic hand with integrated distributed tactile sensing. IEEE Robot. Autom. Lett.; 2022; 7, pp. 3945-3952. [DOI: https://dx.doi.org/10.1109/LRA.2022.3149037]
31. Serrano, D.; Copaci, D.; Arias, J.; Moreno, L.E.; Blanco, D. SMA-Based Soft Exo-Glove. IEEE Robot. Autom. Lett.; 2023; 8, pp. 5448-5455. [DOI: https://dx.doi.org/10.1109/LRA.2023.3295994]
32. Liu, X.; Zhao, Y.; Geng, D.; Chen, S.; Tan, X.; Cao, C. Soft humanoid hands with large grasping force enabled by flexible hybrid pneumatic actuators. Soft Robot.; 2021; 8, pp. 175-185. [DOI: https://dx.doi.org/10.1089/soro.2020.0001]
33. Zhang, N.; Ge, L.; Xu, H.; Zhu, X.; Gu, G. 3D printed, modularized rigid-flexible integrated soft finger actuators for anthropomorphic hands. Sens. Actuators A Phys.; 2020; 312, 112090. [DOI: https://dx.doi.org/10.1016/j.sna.2020.112090]
34. Yang, Y.; Chen, Y. Novel design and 3D printing of variable stiffness robotic fingers based on shape memory polymer. Proceedings of the 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob); Singapore, 26–29 June 2016; pp. 195-200.
35. Bionicsofthand Pneumatic Robot Hand with Artificial Intelligence. 2023; Available online: https://www.festo.com/gb/en/e/about-festo/research-and-development/bionic-learning-network/highlights-from-2018-to-2021/bionicsofthand-id68106/ (accessed on 11 January 2024).
36. Curkovic, P.; Cubric, G. Fused Deposition Modelling for 3D printing of Soft Anthropomorphic Actuators. Int. J. Simul. Model.; 2021; 20, 12. [DOI: https://dx.doi.org/10.2507/IJSIMM20-2-560]
37. Deng, E.; Tadesse, Y. A soft 3D-printed robotic hand actuated by coiled SMA. Actuators; 2020; 10, 6. [DOI: https://dx.doi.org/10.3390/act10010006]
38. Du, H.; Yao, Y.; Zhou, X. A facile fabricating method for smart soft robotic hand. Polym. Eng. Sci.; 2023; 63, pp. 118-125. [DOI: https://dx.doi.org/10.1002/pen.26190]
39. Tavakoli, M.; de Almeida, A.T. Adaptive under-actuated anthropomorphic hand: ISR-SoftHand. Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems; Chicago, IL, USA, 14–18 September 2014; pp. 1629-1634.
40. Zhang, C.; Li, M.; Chen, Y.; Yang, Z.; He, B.; Li, X.; Xie, J.; Xu, G. An Anthropomorphic Robotic Hand With a Soft-Rigid Hybrid Structure and Positive- Negative Pneumatic Actuation. IEEE Robot. Autom. Lett.; 2023; 8, pp. 4346-4353. [DOI: https://dx.doi.org/10.1109/LRA.2023.3280829]
41. Terryn, S.; Brancart, J.; Lefeber, D.; Assche, G.V.; Vanderborght, B. Self-healing soft pneumatic robots. Sci. Robot.; 2017; 2, eaan4268. [DOI: https://dx.doi.org/10.1126/scirobotics.aan4268]
42. Gafford, J.; Ding, Y.; Harris, A.; McKenna, T.; Polygerinos, P.; Holland, D.; Moser, A.; Walsh, C. Shape deposition manufacturing of a soft, atraumatic, deployable surgical grasper. J. Med. Devices; 2014; 8, 030927. [DOI: https://dx.doi.org/10.1115/1.4027048]
43. Mosadegh, B.; Polygerinos, P.; Keplinger, C.; Wennstedt, S.; Shepherd, R.F.; Gupta, U.; Shim, J.; Bertoldi, K.; Walsh, C.J.; Whitesides, A.G.M. Pneumatic Networks for Soft Robotics that Actuate Rapidly. Adv. Funct. Mater.; 2014; 24, pp. 2163-2170. [DOI: https://dx.doi.org/10.1002/adfm.201303288]
44. Yamaguchi, A.; Takemura, K.; Yokota, S.; Edamura, K. A robot hand using electro-conjugate fluid. Proceedings of the 2011 IEEE International Conference on Robotics and Automation; Shanghai, China, 9–13 May 2011; pp. 5923-5928.
45. Kim, W.; Seo, B.; Yu, S.Y.; Cho, K.-J. Deployable soft pneumatic networks (d-pneunets) actuator with dual-morphing origami chambers for high-compactness. IEEE Robot. Autom. Lett.; 2021; 7, pp. 1262-1269. [DOI: https://dx.doi.org/10.1109/LRA.2021.3137504]
46. Luo, Y.; Zou, J.; Gu, G. Multimaterial pneumatic soft actuators and robots through a planar laser cutting and stacking approach. Adv. Intell. Syst.; 2021; 3, 2000257. [DOI: https://dx.doi.org/10.1002/aisy.202000257]
47. Kim, Y.-J.; Yoon, J.; Sim, Y.-W. Fluid lubricated dexterous finger mechanism for human-like impact absorbing capability. IEEE Robot. Autom. Lett.; 2019; 4, pp. 3971-3978. [DOI: https://dx.doi.org/10.1109/LRA.2019.2929988]
48. Yap, H.K.; Lim, J.H.; Goh, J.C.H.; Yeow, C.-H. Design of a soft robotic glove for hand rehabilitation of stroke patients with clenched fist deformity using inflatable plastic actuators. J. Med. Devices; 2016; 10, 044504. [DOI: https://dx.doi.org/10.1115/1.4033035]
49. Scharff, R.B.; Doubrovski, E.L.; Poelman, W.A.; Jonker, P.P.; Wang, C.C.; Geraedts, J.M. Towards behavior design of a 3D-printed soft robotic hand. Proceedings of the Soft Robotics: Trends, Applications and Challenges: Proceedings of the Soft Robotics Week; Livorno, Italy, 25–30 April 2016; pp. 23-29.
50. Liu, S.; Wang, F.; Liu, Z.; Zhang, W.; Tian, Y.; Zhang, D. A two-finger soft-robotic gripper with enveloping and pinching grasping modes. IEEE/ASME Trans. Mechatron.; 2020; 26, pp. 146-155. [DOI: https://dx.doi.org/10.1109/TMECH.2020.3005782]
51. Zhang, P.; Tang, B. A two-finger soft gripper based on bistable mechanism. IEEE Robot. Autom. Lett.; 2022; 7, pp. 11330-11337. [DOI: https://dx.doi.org/10.1109/LRA.2022.3200207]
52. Deimel, R.; Brock, O. A novel type of compliant and underactuated robotic hand for dexterous grasping. Inter. J. Robotl Res.; 2016; 35, pp. 161-185. [DOI: https://dx.doi.org/10.1177/0278364915592961]
53. Hughes, J.; Maiolino, P.; Iida, F. An anthropomorphic soft skeleton hand exploiting conditional models for piano playing. Sci. Robot.; 2018; 3, eaau3098. [DOI: https://dx.doi.org/10.1126/scirobotics.aau3098] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33141692]
54. Salem, M.E.; Wen, R.; Xu, M.H.; Yan, L.; Xiang, M.; Wang, Q. A novel underactuated soft humanoid hand for hand sign language. Proceedings of the 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC); Auckland, New Zealand, 20–23 May 2019; pp. 1-6.
55. Galloway, K.C.; Becker, K.P.; Phillips, B.; Kirby, J.; Licht, S.; Tchernov, D.; Wood, R.J.; Gruber, D.F. Soft robotic grippers for biological sampling on deep reefs. Soft Robot.; 2016; 3, pp. 23-33. [DOI: https://dx.doi.org/10.1089/soro.2015.0019] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27625917]
56. Li, H.; Ford, C.J.; Bianchi, M.; Catalano, M.G.; Psomopoulou, E.; Lepora, N.F. BRL/Pisa/IIT softhand: A low-cost, 3D-printed, underactuated, tendon-driven hand with soft and adaptive synergies. IEEE Robot. Autom. Lett.; 2022; 7, pp. 8745-8751. [DOI: https://dx.doi.org/10.1109/LRA.2022.3187876]
57. Jain, S.; Dontu, S.; Teoh, J.E.M.; Alvarado, P.V.Y. A Multimodal, Reconfigurable Workspace Soft Gripper for Advanced Grasping Tasks. Soft Robot.; 2023; 10, pp. 527-544. [DOI: https://dx.doi.org/10.1089/soro.2021.0225] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36346280]
58. Abondance, S.; Teeple, C.B.; Wood, R.J. A dexterous soft robotic hand for delicate in-hand manipulation. IEEE Robot. Autom. Lett.; 2020; 5, pp. 5502-5509. [DOI: https://dx.doi.org/10.1109/LRA.2020.3007411]
59. Zhu, M.; Mori, Y.; Wakayama, T.; Wada, A.; Kawamura, S. A fully multi-material three-dimensional printed soft gripper with variable stiffness for robust grasping. Soft Robot.; 2019; 6, pp. 507-519. [DOI: https://dx.doi.org/10.1089/soro.2018.0112]
60. Li, H.; Zhou, P.; Zhang, S.; Yao, J.; Zhao, Y. A high-load bioinspired soft gripper with force booster fingers. Mech. Mach. Theory; 2022; 177, 105048. [DOI: https://dx.doi.org/10.1016/j.mechmachtheory.2022.105048]
61. Bao, G.; Ma, X.; Luo, X.; Shao, T.F.Z.; Zhang, L.; Yang, Q. Full compliant continuum robotic finger and its kinematic model. Iran. J. Sci. Technology. Trans. Mech. Eng.; 2014; 38, 389.
62. Yamanaka, Y.; Katagiri, S.; Nabae, H.; Suzumori, K.; Endo, G. Development of a food handling soft robot hand considering a high-speed pick-and-place task. Proceedings of the 2020 IEEE/SICE International Symposium on System Integration (SII); Honolulu, HI, USA, 12–15 January 2020; pp. 87-92.
63. Gao, G.; Chang, C.-M.; Gerez, L.; Liarokapis, M. A pneumatically driven, disposable, soft robotic gripper equipped with multi-stage, retractable, telescopic fingers. IEEE Trans. Med. Robot. Bionics; 2021; 3, pp. 573-582. [DOI: https://dx.doi.org/10.1109/TMRB.2021.3097143]
64. Thuruthel, T.G.; Abidi, S.H.; Cianchetti, M.; Laschi, C.; Falotico, E. A bistable soft gripper with mechanically embedded sensing and actuation for fast grasping. Proceedings of the 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN); Honolulu, HI, USA, 12–15 January 2020; pp. 1049-1054.
65. Bullock, I.M.; Ma, R.R.; Dollar, A.M. A hand-centric classification of human and robot dexterous manipulation. IEEE Trans. Haptics; 2012; 6, pp. 129-144. [DOI: https://dx.doi.org/10.1109/TOH.2012.53] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24808298]
66. Li, Y.; Chen, Y.; Ren, T.; Hu, Y.; Liu, H.; Lin, S.; Yang, Y.; Li, Y.; Zhou, J. A dual-mode actuator for soft robotic hand. IEEE Robot. Autom. Lett.; 2021; 6, pp. 1144-1151. [DOI: https://dx.doi.org/10.1109/LRA.2021.3056357]
67. Fras, J.; Althoefer, K. Soft biomimetic prosthetic hand: Design, manufacturing and preliminary examination. Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Madrid, Spain, 1–5 October 2018; pp. 1-6.
68. Sinatra, N.R.; Teeple, C.B.; Vogt, D.M.; Parker, K.K.; Gruber, D.F.; Wood, R.J. Ultragentle manipulation of delicate structures using a soft robotic gripper. Sci. Robot.; 2019; 4, eaax5425. [DOI: https://dx.doi.org/10.1126/scirobotics.aax5425] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33137785]
69. Andrychowicz, M.; Baker, B.; Chociej, M.; Jozefowicz, R.; McGrew, B.; Pachocki, J.; Petron, A.; Plappert, M.; Powell, G.; Ray, A. Learning dexterous in-hand manipulation. Int. J. Robot. Res.; 2020; 39, pp. 3-20. [DOI: https://dx.doi.org/10.1177/0278364919887447]
70. Salvietti, G.; Hussain, I.; Malvezzi, M.; Prattichizzo, D. Design of the passive joints of underactuated modular soft hands for fingertip trajectory tracking. IEEE Robot. Autom. Lett.; 2017; 2, pp. 2008-2015. [DOI: https://dx.doi.org/10.1109/LRA.2017.2718099]
71. Feix, T.; Romero, J.; Schmiedmayer, H.-B.; Dollar, A.M.; Kragic, D. The grasp taxonomy of human grasp types. IEEE Trans. Hum.-Mach. Syst.; 2015; 46, pp. 66-77. [DOI: https://dx.doi.org/10.1109/THMS.2015.2470657]
72. Su, C.; Wang, R.; Lu, T.; Wang, S. SAU-RFC hand: A novel self-adaptive underactuated robot hand with rigid-flexible coupling fingers. Robotica; 2023; 41, pp. 511-529. [DOI: https://dx.doi.org/10.1017/S0263574722000364]
73. Hang, K.; Morgan, A.S.; Dollar, A.M. Pre-grasp sliding manipulation of thin objects using soft, compliant, or underactuated hands. IEEE Robot. Autom. Lett.; 2019; 4, pp. 662-669. [DOI: https://dx.doi.org/10.1109/LRA.2019.2892591]
74. Tan, N.; Gu, X.; Ren, H. Simultaneous robot-world, sensor-tip, and kinematics calibration of an underactuated robotic hand with soft fingers. IEEE Access; 2017; 6, pp. 22705-22715. [DOI: https://dx.doi.org/10.1109/ACCESS.2017.2781698]
75. Liu, Y.; Xiao, H.; Hao, T.; Pang, D.; Wang, F.; Liu, S. Dexterous all-soft hand (DASH) with active palm: Multi-functional soft hand beyond grasping. Smart Mater. Struct.; 2023; 32, 125012. [DOI: https://dx.doi.org/10.1088/1361-665X/ad07a3]
76. Zhou, J.; Li, Y.; Yang, Y.; Cao, H.; Huang, J.; Liu, Y. A 22-DOFs bio-inspired soft hand achieving 6 kinds of in-hand manipulation. Proceedings of the 2021 IEEE International Conference on Real-Time Computing and Robotics (RCAR); Xining, China, 15–19 July 2021; pp. 20-26.
77. Hao, T.; Liu, S.; Gu, J. Flexible and stable grasping by multi-jointed pneumatic actuator mimicking the human finger-impacts of structural parameters on performance. Smart Mater. Struct.; 2021; 30, 125019. [DOI: https://dx.doi.org/10.1088/1361-665X/ac3405]
78. Wang, H.; Abu-Dakka, F.J.; Le, T.N.; Kyrki, V.; Xu, H. A novel soft robotic hand design with human-inspired soft palm: Achieving a great diversity of grasps. IEEE Robot. Autom. Mag.; 2021; 28, pp. 37-49. [DOI: https://dx.doi.org/10.1109/MRA.2021.3065870]
79. Nanayakkara, V.K.; Cotugno, G.; Vitzilaios, N.; Venetsanos, D.; Nanayakkara, T.; Sahinkaya, M.N. The role of morphology of the thumb in anthropomorphic grasping: A review. Front. Mech. Eng.; 2017; 3, 5. [DOI: https://dx.doi.org/10.3389/fmech.2017.00005]
80. Yoneda, T.; Morihiro, D.; Ozawa, R. Development of a multifingered robotic hand with the thenar grasp function. Adv. Robot.; 2020; 34, pp. 661-673. [DOI: https://dx.doi.org/10.1080/01691864.2020.1751706]
81. Feix, T.; Pawlik, R.; Schmiedmayer, H.-B.; Romero, J.; Kragic, D. A comprehensive grasp taxonomy. Proceedings of the Robotics, Science and Systems: Workshop on Understanding the Human Hand for Advancing Robotic Manipulation; Seattle, WA, USA, 28 June–1 July 2009; pp. 2-3.
82. Chen, W.; Li, G.; Li, N.; Wang, W.; Yu, P.; Wang, R.; Xue, X.; Zhao, X.; Liu, L. Soft exoskeleton with fully actuated thumb movements for grasping assistance. IEEE Trans. Robot.; 2022; 38, pp. 2194-2207. [DOI: https://dx.doi.org/10.1109/TRO.2022.3148909]
83. Zhou, H.; Mohammadi, A.; Oetomo, D.; Alici, G. A novel monolithic soft robotic thumb for an anthropomorphic prosthetic hand. IEEE Robot. Autom. Lett.; 2019; 4, pp. 602-609. [DOI: https://dx.doi.org/10.1109/LRA.2019.2892203]
84. Chalon, M.; Dietrich, A.; Grebenstein, M. The thumb of the anthropomorphic awiwi hand: From concept to evaluation. Int. J. Humanoid Robot.; 2014; 11, 1450019. [DOI: https://dx.doi.org/10.1142/S0219843614500194]
85. Sangole, A.P.; Levin, M.F. Arches of the hand in reach to grasp. J. Biomech.; 2008; 41, pp. 829-837. [DOI: https://dx.doi.org/10.1016/j.jbiomech.2007.11.006]
86. Wang, H.; Xu, H.; Abu-Dakka, F.J.; Kyrki, V.; Yang, C.; Li, X.; Chen, S. A bidirectional soft biomimetic hand driven by water hydraulic for dexterous underwater grasping. IEEE Robot. Autom. Lett.; 2022; 7, pp. 2186-2193. [DOI: https://dx.doi.org/10.1109/LRA.2022.3143297]
87. Pozzi, M.; Malvezzi, M.; Prattichizzo, D.; Salvietti, G. Actuated Palms for Soft Robotic Hands: Review and Perspectives. IEEE/ASME Trans. Mechatron.; 2023; pp. 1-11. [DOI: https://dx.doi.org/10.1109/TMECH.2023.3328944]
88. Lin, J.; Hu, Q.; Xia, J.; Zhao, L.; Du, X.; Li, S.; Chen, Y.; Wang, X. Non-destructive fruit firmness evaluation using a soft gripper and vision-based tactile sensing. Comput. Electron. Agric.; 2023; 214, 108256. [DOI: https://dx.doi.org/10.1016/j.compag.2023.108256]
89. Tavakoli, M.; Batista, R.; Sgrigna, L. The UC Softhand: Light Weight Adaptive Bionic Hand with a Compact Twisted String Actuation System. Actuators; 2015; 5, 1. [DOI: https://dx.doi.org/10.3390/act5010001]
90. Marullo, S.; Salvietti, G.; Prattichizzo, D. On the Use of Magnets to Robustify the Motion Control of Soft Hands. IEEE Robot. Autom. Lett.; 2022; 7, pp. 11743-11750. [DOI: https://dx.doi.org/10.1109/LRA.2022.3205751]
91. Wang, F.; Chen, Y.; Wang, Y.; Liu, Z.; Tian, Y.; Zhang, D. A soft pneumatic glove with multiple rehabilitation postures and assisted grasping modes. Sens. Actuators A. Phys.; 2022; 347, 113978. [DOI: https://dx.doi.org/10.1016/j.sna.2022.113978]
92. Fei, Y.; Wang, J.; Pang, W. A novel fabric-based versatile and stiffness-tunable soft gripper integrating soft pneumatic fingers and wrist. Soft Robot.; 2019; 6, pp. 1-20. [DOI: https://dx.doi.org/10.1089/soro.2018.0015]
93. Ren, T.; Li, Y.; Liu, Q.; Chen, Y.; Yang, S.X.; Yuan, H.; Li, Y.; Yang, Y. Novel Bionic Soft Robotic Hand With Dexterous Deformation and Reliable Grasping. IEEE Trans. Instrum. Meas.; 2023; 72, pp. 1-10. [DOI: https://dx.doi.org/10.1109/TIM.2023.3248098]
94. Chen, C.; Sun, J.; Wang, L.; Chen, G.; Xu, M.; Ni, J.; Ramli, R.; Su, S.; Chu, C. Pneumatic bionic hand with rigid-flexible coupling structure. Materials; 2022; 15, 1358. [DOI: https://dx.doi.org/10.3390/ma15041358]
95. Li, Y.; Wei, Y.; Yang, Y.; Chen, Y. A novel versatile robotic palm inspired by human hand. Eng. Res. Express; 2019; 1, 015008. [DOI: https://dx.doi.org/10.1088/2631-8695/ab2f69]
96. Li, Y.; Chen, Y.; Yang, Y.; Wei, Y. Passive particle jamming and its stiffening of soft robotic grippers. IEEE Trans. Robot.; 2017; 33, pp. 446-455. [DOI: https://dx.doi.org/10.1109/TRO.2016.2636899]
97. Lee, J.; Kim, J.; Park, S.; Hwang, D.; Yang, S. Soft robotic palm with tunable stiffness using dual-layered particle jamming mechanism. IEEE/ASME Trans. Mechatron.; 2021; 26, pp. 1820-1827. [DOI: https://dx.doi.org/10.1109/TMECH.2021.3077941]
98. Choi, W.H.; Kim, S.; Lee, D.; Shin, D. Soft, multi-DoF, variable stiffness mechanism using layer jamming for wearable robots. IEEE Robot. Autom. Lett.; 2019; 4, pp. 2539-2546. [DOI: https://dx.doi.org/10.1109/LRA.2019.2908493]
99. In, H.; Kang, B.B.; Sin, M.; Cho, K.-J. Exo-glove: A wearable robot for the hand with a soft tendon routing system. IEEE Robot. Autom. Mag.; 2015; 22, pp. 97-105. [DOI: https://dx.doi.org/10.1109/MRA.2014.2362863]
100. Sun, Y.; Song, Y.S.; Paik, J. Characterization of silicone rubber based soft pneumatic actuators. Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems; Tokyo, Japan, 3–7 November 2013; pp. 4446-4453.
101. Hu, W.; Lum, G.Z.; Mastrangeli, M.; Sitti, M. Small-scale soft-bodied robot with multimodal locomotion. Nature; 2018; 554, pp. 81-85. [DOI: https://dx.doi.org/10.1038/nature25443] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29364873]
102. Rodrigue, H.; Wang, W.; Han, M.-W.; Kim, T.J.; Ahn, S.-H. An overview of shape memory alloy-coupled actuators and robots. Soft Robot.; 2017; 4, pp. 3-15. [DOI: https://dx.doi.org/10.1089/soro.2016.0008] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29182099]
103. Zhang, Y.F.; Zhang, N.; Hingorani, H.; Ding, N.; Wang, D.; Yuan, C.; Zhang, B.; Gu, G.; Ge, Q. Fast-response, stiffness-tunable soft actuator by hybrid multimaterial 3D printing. Adv. Funct. Mater.; 2019; 29, 1806698. [DOI: https://dx.doi.org/10.1002/adfm.201806698]
104. Tang, Y.; Chi, Y.; Sun, J.; Huang, T.H.; Yin, J. Leveraging elastic instabilities for amplified performance: Spine-inspired high-speed and high-force soft robots. Sci. Adv.; 2020; 6, eaaz6912. [DOI: https://dx.doi.org/10.1126/sciadv.aaz6912] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32494714]
105. Han, H.-Y.; Shimada, A.; Kawamura, S. Analysis of friction on human fingers and design of artificial fingers. Proceedings of the IEEE International Conference on Robotics and Automation; Minneapolis, MN, USA, 22–28 April 1996; pp. 3061-3066.
106. Takahashi, A.; Yamaguchi, A.; Nonomura, Y. Friction between Two Finger Models: Effects of Fingerprints on Friction Dynamics. Chem. Lett.; 2014; 43, pp. 1899-1900. [DOI: https://dx.doi.org/10.1246/cl.140778]
107. Alben, S.; Witt, C.; Baker, T.V.; Anderson, E.; Lauder, G.V. Dynamics of freely swimming flexible foils. Phys. Fluids; 2012; 24, 051901. [DOI: https://dx.doi.org/10.1063/1.4709477]
108. Marchese, A.D.; Onal, C.D.; Rus, D. Autonomous soft robotic fish capable of escape maneuvers using fluidic elastomer actuators. Soft Robot.; 2014; 1, pp. 75-87. [DOI: https://dx.doi.org/10.1089/soro.2013.0009]
109. Hao, Y.; Biswas, S.; Hawkes, E.W.; Wang, T.; Zhu, M.; Wen, L.; Visell, Y. A multimodal, enveloping soft gripper: Shape conformation, bioinspired adhesion, and expansion-driven suction. IEEE Trans. Robot.; 2020; 37, pp. 350-362. [DOI: https://dx.doi.org/10.1109/TRO.2020.3021427]
110. Seibel, A.; Yıldız, M.; Zorlubaş, B. A gecko-inspired soft passive gripper. Biomimetics; 2020; 5, 12. [DOI: https://dx.doi.org/10.3390/biomimetics5020012]
111. Hao, T.; Xiao, H.; Liu, S.; Liu, Y. Fingerprint-inspired surface texture for the enhanced tip pinch performance of a soft robotic hand in lubricated conditions. Friction; 2023; 11, pp. 1349-1358. [DOI: https://dx.doi.org/10.1007/s40544-022-0688-4]
112. Hao, T.; Xiao, H.; Liu, Y.; Pang, D.; Liu, S. Gripping performance of soft grippers with fingerprint-like surface texture for objects with slippery surfaces. Tribol. Int.; 2023; 189, 108992. [DOI: https://dx.doi.org/10.1016/j.triboint.2023.108992]
113. Yamaguchi, T.; Kashiwagi, T.; Arie, T.; Akita, S.; Takei, K. Human-like electronic skin-integrated soft robotic hand. Adv. Intell. Syst.; 2019; 1, 1900018. [DOI: https://dx.doi.org/10.1002/aisy.201900018]
114. Fang, B.; Xia, Z.; Sun, F.; Yang, Y.; Liu, H.; Fang, C. Soft magnetic fingertip with particle-jamming structure for tactile perception and grasping. IEEE Trans. Ind. Electron.; 2022; 70, pp. 6027-6035. [DOI: https://dx.doi.org/10.1109/TIE.2022.3201305]
115. Liu, X.; Han, X.; Guo, N.; Wan, F.; Song, C. Bio-Inspired Proprioceptive Touch of a Soft Finger with Inner-Finger Kinesthetic Perception. Biomimetics; 2023; 8, 501. [DOI: https://dx.doi.org/10.3390/biomimetics8060501]
116. Yamaguchi, T.; Arie, T.; Akita, S.; Takei, K. Electronic skin-integrated soft robotic hand. Proceedings of the 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII); Berlin, Germany, 23–27 June 2019; pp. 543-546.
117. Zhou, Y.; Wan, C.; Yang, Y.; Yang, H.; Wang, S.; Dai, Z.; Ji, K.; Jiang, H.; Chen, X.; Long, Y. Highly stretchable, elastic, and ionic conductive hydrogel for artificial soft electronics. Adv. Funct. Mater.; 2019; 29, 1806220. [DOI: https://dx.doi.org/10.1002/adfm.201806220]
118. Zhao, H.; O’brien, K.; Li, S.; Shepherd, R.F. Optoelectronically innervated soft prosthetic hand via stretchable optical waveguides. Sci. Robot.; 2016; 1, eaai7529. [DOI: https://dx.doi.org/10.1126/scirobotics.aai7529] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33157858]
119. Tavakoli, M.; Lopes, P.; Lourenco, J.; Rocha, R.P.; Giliberto, L.; de Almeida, A.T.; Majidi, C. Autonomous selection of closing posture of a robotic hand through embodied soft matter capacitive sensors. IEEE Sens. J.; 2017; 17, pp. 5669-5677. [DOI: https://dx.doi.org/10.1109/JSEN.2017.2726348]
120. Huang, H.; Lin, J.; Wu, L.; Fang, B.; Wen, Z.; Sun, F. Machine learning-based multi-modal information perception for soft robotic hands. Tsinghua Sci. Technol.; 2019; 25, pp. 255-269. [DOI: https://dx.doi.org/10.26599/TST.2019.9010009]
121. Zhou, H.; Tawk, C.; Alici, G. A 3D printed soft prosthetic hand with embedded actuation and soft sensing capabilities for directly and seamlessly switching between various hand gestures. Proceedings of the 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM); Delft, The Netherlands, 12–16 July 2021; pp. 75-80.
122. Byun, J.; Lee, Y.; Yoon, J.; Lee, B.; Oh, E.; Chung, S.; Lee, T.; Cho, K.-J.; Kim, J.; Hong, Y. Electronic skins for soft, compact, reversible assembly of wirelessly activated fully soft robots. Sci. Robot.; 2018; 3, eaas9020. [DOI: https://dx.doi.org/10.1126/scirobotics.aas9020] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33141703]
123. Boutry, C.M.; Negre, M.; Jorda, M.; Vardoulis, O.; Chortos, A.; Khatib, O.; Bao, Z. A hierarchically patterned, bioinspired e-skin able to detect the direction of applied pressure for robotics. Sci. Robot.; 2018; 3, eaau6914. [DOI: https://dx.doi.org/10.1126/scirobotics.aau6914] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33141713]
124. Osborn, L.E.; Dragomir, A.; Betthauser, J.L.; Hunt, C.L.; Nguyen, H.H.; Kaliki, R.R.; Thakor, N.V. Prosthesis with neuromorphic multilayered e-dermis perceives touch and pain. Sci. Robot.; 2018; 3, eaat3818. [DOI: https://dx.doi.org/10.1126/scirobotics.aat3818] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32123782]
125. Zhao, X.; Zhang, Z.; Xu, L.; Gao, F.; Zhang, Y. Fingerprint-inspired electronic skin based on triboelectric nanogenerator for fine texture recognition. Nano Energy; 2021; 85, 106001. [DOI: https://dx.doi.org/10.1016/j.nanoen.2021.106001]
126. Ponraj, G.; Kirthika, S.K.; Thakor, N.V.; Yeow, C.-H.; Kukreja, S.L.; Ren, H. Development of flexible fabric based tactile sensor for closed loop control of soft robotic actuator. Proceedings of the 2017 13th IEEE Conference on Automation Science and Engineering (CASE); Xi’an, China, 20–23 August 2017; pp. 1451-1456.
127. Yang, H.; Chen, Y.; Sun, Y.; Hao, L. A novel pneumatic soft sensor for measuring contact force and curvature of a soft gripper. Sens. Actuators A Phys.; 2017; 266, pp. 318-327. [DOI: https://dx.doi.org/10.1016/j.sna.2017.09.040]
128. Marasco, P.D.; Kim, K.; Colgate, J.E.; Peshkin, M.A.; Kuiken, T.A. Robotic touch shifts perception of embodiment to a prosthesis in targeted reinnervation amputees. Brain; 2011; 134, pp. 747-758. [DOI: https://dx.doi.org/10.1093/brain/awq361]
129. Lu, Q.; He, L.; Nanayakkara, T.; Rojas, N. Precise in-hand manipulation of soft objects using soft fingertips with tactile sensing and active deformation. Proceedings of the 2020 3rd IEEE International Conference on Soft Robotics (RoboSoft); New Haven, CT, USA, 15 May–15 July 2020; pp. 52-57.
130. Zhou, H.; Tawk, C.; Alici, G. A 3D printed soft robotic hand with embedded soft sensors for direct transition between hand gestures and improved grasping quality and diversity. IEEE Trans. Neural Syst. Rehabil. Eng.; 2022; 30, pp. 550-558. [DOI: https://dx.doi.org/10.1109/TNSRE.2022.3156116]
131. Zimmermann, C.; Ceylan, D.; Yang, J.; Russell, B.; Argus, M.; Brox, T. Freihand: A dataset for markerless capture of hand pose and shape from single rgb images. Proceedings of the IEEE/CVF International Conference on Computer Vision; Seoul, Republic of Korea, 27 October–2 November 2019; pp. 813-822.
132. Hasson, Y.; Varol, G.; Tzionas, D.; Kalevatykh, I.; Black, M.J.; Laptev, I.; Schmid, C. Learning joint reconstruction of hands and manipulated objects. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; Long Beach, CA, USA, 15–20 June 2019; pp. 11807-11816.
133. Hampali, S.; Sarkar, S.D.; Rad, M.; Lepetit, V. Keypoint transformer: Solving joint identification in challenging hands and object interactions for accurate 3d pose estimation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; New Orleans, LA, USA, 18–24 June 2022; pp. 11090-11100.
134. Shih, B.; Shah, D.; Li, J.; Thuruthel, T.G.; Park, Y.-L.; Iida, F.; Bao, Z.; Kramer-Bottiglio, R.; Tolley, M.T. Electronic skins and machine learning for intelligent soft robots. Sci. Robot.; 2020; 5, eaaz9239. [DOI: https://dx.doi.org/10.1126/scirobotics.aaz9239]
135. Liu, F.; Deswal, S.; Christou, A.; Sandamirskaya, Y.; Kaboli, M.; Dahiya, R. Neuro-inspired electronic skin for robots. Sci. Robot.; 2022; 7, eabl7344. [DOI: https://dx.doi.org/10.1126/scirobotics.abl7344]
136. Wang, L.; Jiang, K.; Shen, G. Wearable, implantable, and interventional medical devices based on smart electronic skins. Adv. Mater. Technol.; 2021; 6, 2100107. [DOI: https://dx.doi.org/10.1002/admt.202100107]
137. Ma, Z.; Li, S.; Wang, H.; Cheng, W.; Li, Y.; Pan, L.; Shi, Y. Advanced electronic skin devices for healthcare applications. J. Mater. Chem. B; 2019; 7, pp. 173-197. [DOI: https://dx.doi.org/10.1039/C8TB02862A]
138. Chen, K.; Liang, K.; Liu, H.; Liu, R.; Liu, Y.; Zeng, S.; Tian, Y. Skin-Inspired Ultra-Tough Supramolecular Multifunctional Hydrogel Electronic Skin for Human–Machine Interaction. Nano-Micro Lett.; 2023; 15, 102. [DOI: https://dx.doi.org/10.1007/s40820-023-01084-8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37052831]
139. Chortos, A.; Liu, J.; Bao, Z. Pursuing prosthetic electronic skin. Nat. Mater.; 2016; 15, pp. 937-950. [DOI: https://dx.doi.org/10.1038/nmat4671] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27376685]
140. Jason, N.N.; Ho, M.D.; Cheng, W. Resistive electronic skin. J. Mater. Chem. C; 2017; 5, pp. 5845-5866. [DOI: https://dx.doi.org/10.1039/C7TC01169E]
141. Zhong, F.; Hu, W.; Zhu, P.; Wang, H.; Ma, C.; Lin, N.; Wang, Z. Piezoresistive design for electronic skin: From fundamental to emerging applications. Opto-Electron. Adv.; 2022; 5, pp. 210029-210021-210029-210032. [DOI: https://dx.doi.org/10.29026/oea.2022.210029]
142. Liu, W.; Lin, D.; Zeng, W.; Wang, Q.; Yang, J.; Peng, Z.; Zhang, Q.; Zhu, G. A multifunctional flexible ferroelectric transistor sensor for electronic skin. Adv. Mater. Interfaces; 2021; 8, 2101166. [DOI: https://dx.doi.org/10.1002/admi.202101166]
143. Rao, J.; Chen, Z.; Zhao, D.; Ma, R.; Yi, W.; Zhang, C.; Liu, D.; Chen, X.; Yang, Y.; Wang, X. Tactile electronic skin to simultaneously detect and distinguish between temperature and pressure based on a triboelectric nanogenerator. Nano Energy; 2020; 75, 105073. [DOI: https://dx.doi.org/10.1016/j.nanoen.2020.105073]
144. Zhang, J.; Wan, L.; Gao, Y.; Fang, X.; Lu, T.; Pan, L.; Xuan, F. Highly stretchable and self-healable MXene/polyvinyl alcohol hydrogel electrode for wearable capacitive electronic skin. Adv. Electron. Mater.; 2019; 5, 1900285. [DOI: https://dx.doi.org/10.1002/aelm.201900285]
145. Dargahi, J.; Najarian, S. Human tactile perception as a standard for artificial tactile sensing—A review. Int. J. Med. Robot. Comput. Assist. Surg.; 2004; 1, pp. 23-35. [DOI: https://dx.doi.org/10.1002/rcs.3]
146. Haddadin, S.; De Luca, A.; Albu-Schäffer, A. Robot collisions: A survey on detection, isolation, and identification. IEEE Trans. Robot.; 2017; 33, pp. 1292-1312. [DOI: https://dx.doi.org/10.1109/TRO.2017.2723903]
147. Li, Q.; Natale, L.; Haschke, R.; Cherubini, A.; Ho, A.-V.; Ritter, H. Tactile sensing for manipulation. Int. J. Humanoid Robot.; 2018; 15, 1802001. [DOI: https://dx.doi.org/10.1142/S0219843618020012]
148. Qu, X.; Xue, J.; Liu, Y.; Rao, W.; Liu, Z.; Li, Z. Fingerprint-shaped triboelectric tactile sensor. Nano Energy; 2022; 98, 107324. [DOI: https://dx.doi.org/10.1016/j.nanoen.2022.107324]
149. Harada, S.; Kanao, K.; Yamamoto, Y.; Arie, T.; Akita, S.; Takei, K. Fully printed flexible fingerprint-like three-axis tactile and slip force and temperature sensors for artificial skin. ACS Nano; 2014; 8, pp. 12851-12857. [DOI: https://dx.doi.org/10.1021/nn506293y]
150. Zhu, L.; Wang, Y.; Mei, D.; Ding, W.; Jiang, C.; Lu, Y. Fully elastomeric fingerprint-shaped electronic skin based on tunable patterned graphene/silver nanocomposites. ACS Appl. Mater. Interfaces; 2020; 12, pp. 31725-31737. [DOI: https://dx.doi.org/10.1021/acsami.0c09653]
151. Qiu, Y.; Tian, Y.; Sun, S.; Hu, J.; Wang, Y.; Zhang, Z.; Liu, A.; Cheng, H.; Gao, W.; Zhang, W. Bioinspired, multifunctional dual-mode pressure sensors as electronic skin for decoding complex loading processes and human motions. Nano Energy; 2020; 78, 105337. [DOI: https://dx.doi.org/10.1016/j.nanoen.2020.105337]
152. Duan, S.; Shi, Q.; Hong, J.; Zhu, D.; Lin, Y.; Li, Y.; Lei, W.; Lee, C.; Wu, J. Water-modulated biomimetic hyper-attribute-gel electronic skin for robotics and skin-attachable wearables. ACS Nano; 2023; 17, pp. 1355-1371. [DOI: https://dx.doi.org/10.1021/acsnano.2c09851]
153. Ma, Z.; Zhang, J.; Li, J.; Shi, Y.; Pan, L. Frequency-enabled decouplable dual-modal flexible pressure and temperature sensor. IEEE Electron Device Lett.; 2020; 41, pp. 1568-1571. [DOI: https://dx.doi.org/10.1109/LED.2020.3020937]
154. Han, S.; Alvi, N.U.H.; Granlöf, L.; Granberg, H.; Berggren, M.; Fabiano, S.; Crispin, X. A multiparameter pressure–temperature–humidity sensor based on mixed ionic–electronic cellulose aerogels. Adv. Sci.; 2019; 6, 1802128. [DOI: https://dx.doi.org/10.1002/advs.201802128] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31016118]
155. Zhang, T.; Liu, H.; Jiang, L.; Fan, S.; Yang, J. Development of a flexible 3-D tactile sensor system for anthropomorphic artificial hand. IEEE Sens. J.; 2012; 13, pp. 510-518. [DOI: https://dx.doi.org/10.1109/JSEN.2012.2220345]
156. Lee, B.-Y.; Kim, S.; Oh, S.; Lee, Y.; Park, J.; Ko, H.; Koo, J.C.; Jung, Y.; Lim, H. Human-Inspired Tactile Perception System for Real-Time and Multimodal Detection of Tactile Stimuli. Soft Robot.; 2023; [DOI: https://dx.doi.org/10.1089/soro.2022.0191]
157. Sun, Z.; Wang, S.; Zhao, Y.; Zhong, Z.; Zuo, L. Discriminating soft actuators’ thermal stimuli and mechanical deformation by hydrogel sensors and machine learning. Adv. Intell. Syst.; 2022; 4, 2200089. [DOI: https://dx.doi.org/10.1002/aisy.202200089]
158. Charalambides, A.; Bergbreiter, S. Rapid manufacturing of mechanoreceptive skins for slip detection in robotic grasping. Adv. Mater. Technol.; 2017; 2, 1600188. [DOI: https://dx.doi.org/10.1002/admt.201600188]
159. Wang, Y.; Ding, W.; Mei, D. Development of flexible tactile sensor for the envelop of curved robotic hand finger in grasping force sensing. Measurement; 2021; 180, 109524. [DOI: https://dx.doi.org/10.1016/j.measurement.2021.109524]
160. Pannen, T.J.; Puhlmann, S.; Brock, O. A low-cost, easy-to-manufacture, flexible, multi-taxel tactile sensor and its application to in-hand object recognition. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA); Philadelphia, PA, USA, 23–27 May 2022; pp. 10939-10944.
161. Yuan, Z.; Shen, G.; Pan, C.; Wang, Z.L. Flexible sliding sensor for simultaneous monitoring deformation and displacement on a robotic hand/arm. Nano Energy; 2020; 73, 104764. [DOI: https://dx.doi.org/10.1016/j.nanoen.2020.104764]
162. Chen, H.; Song, Y.; Guo, H.; Miao, L.; Chen, X.; Su, Z.; Zhang, H. Hybrid porous micro structured finger skin inspired self-powered electronic skin system for pressure sensing and sliding detection. Nano Energy; 2018; 51, pp. 496-503. [DOI: https://dx.doi.org/10.1016/j.nanoen.2018.07.001]
163. Chen, H.; Miao, L.; Su, Z.; Song, Y.; Han, M.; Chen, X.; Cheng, X.; Chen, D.; Zhang, H. Fingertip-inspired electronic skin based on triboelectric sliding sensing and porous piezoresistive pressure detection. Nano Energy; 2017; 40, pp. 65-72. [DOI: https://dx.doi.org/10.1016/j.nanoen.2017.08.001]
164. Yang, W.; Xie, M.; Zhang, X.; Sun, X.; Zhou, C.; Chang, Y.; Zhang, H.; Duan, X. Multifunctional soft robotic finger based on a nanoscale flexible temperature–pressure tactile sensor for material recognition. ACS Appl. Mater. Interfaces; 2021; 13, pp. 55756-55765. [DOI: https://dx.doi.org/10.1021/acsami.1c17923]
165. Kim, J.; Lee, M.; Shim, H.J.; Ghaffari, R.; Cho, H.R.; Son, D.; Jung, Y.H.; Soh, M.; Choi, C.; Jung, S. Stretchable silicon nanoribbon electronics for skin prosthesis. Nat. Commun.; 2014; 5, 5747. [DOI: https://dx.doi.org/10.1038/ncomms6747]
166. Rothemund, P.; Ainla, A.; Belding, L.; Preston, D.J.; Kurihara, S.; Suo, Z.; Whitesides, G.M. A soft, bistable valve for autonomous control of soft actuators. Sci. Robot.; 2018; 3, eaar7986. [DOI: https://dx.doi.org/10.1126/scirobotics.aar7986]
167. Kim, D.; Kim, S.-H.; Kim, T.; Kang, B.B.; Lee, M.; Park, W.; Ku, S.; Kim, D.; Kwon, J.; Lee, H. Review of machine learning methods in soft robotics. PLoS ONE; 2021; 16, e0246102. [DOI: https://dx.doi.org/10.1371/journal.pone.0246102]
168. Honji, S.; Arita, H.; Tahara, K. Stochastic approach for modeling soft fingers with creep behavior. Adv. Robot.; 2023; 37, pp. 1471-1484. [DOI: https://dx.doi.org/10.1080/01691864.2023.2279600]
169. Mirza, N.M. Machine learning and soft robotics. Proceedings of the 2020 21st International Arab Conference on Information Technology (ACIT); Giza, Egypt, 28–30 November 2020; pp. 1-5.
170. Zhao, W.; Jiang, J.; Qin, H.; Li, X.; Li, J. Machine learning based soft sensor and long-term calibration scheme: A solid oxide fuel cell system case. Int. J. Hydrog. Energy; 2021; 46, pp. 17322-17342. [DOI: https://dx.doi.org/10.1016/j.ijhydene.2021.02.134]
171. Han, S.; Kim, T.; Kim, D.; Park, Y.-L.; Jo, S. Use of deep learning for characterization of microfluidic soft sensors. IEEE Robot. Autom. Lett.; 2018; 3, pp. 873-880. [DOI: https://dx.doi.org/10.1109/LRA.2018.2792684]
172. Satheeshbabu, S.; Uppalapati, N.K.; Chowdhary, G.; Krishnan, G. Open loop position control of soft continuum arm using deep reinforcement learning. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA); Montreal, QC, Canada, 20–24 May 2019; pp. 5133-5139.
173. Carrico, J.D.; Hermans, T.; Kim, K.J.; Leang, K.K. 3D-printing and machine learning control of soft ionic polymer-metal composite actuators. Sci. Rep.; 2019; 9, 17482. [DOI: https://dx.doi.org/10.1038/s41598-019-53570-y] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31767889]
174. Sohn, K.-S.; Chung, J.; Cho, M.-Y.; Timilsina, S.; Park, W.B.; Pyo, M.; Shin, N.; Sohn, K.; Kim, J.S. An extremely simple macroscale electronic skin realized by deep machine learning. Sci. Rep.; 2017; 7, 11061. [DOI: https://dx.doi.org/10.1038/s41598-017-11663-6]
175. Calandra, R.; Owens, A.; Jayaraman, D.; Lin, J.; Yuan, W.; Malik, J.; Adelson, E.H.; Levine, S. More than a feeling: Learning to grasp and regrasp using vision and touch. IEEE Robot. Autom. Lett.; 2018; 3, pp. 3300-3307. [DOI: https://dx.doi.org/10.1109/LRA.2018.2852779]
176. Wang, T.; Noori, M.; Altabey, W.A.; Wu, Z.; Ghiasi, R.; Kuok, S.-C.; Silik, A.; Farhan, N.S.; Sarhosis, V.; Farsangi, E.N. From model-driven to data-driven: A review of hysteresis modeling in structural and mechanical systems. Mech. Syst. Signal Process.; 2023; 204, 110785. [DOI: https://dx.doi.org/10.1016/j.ymssp.2023.110785]
177. Luo, Y.; Xiao, X.; Chen, J.; Li, Q.; Fu, H. Machine-learning-assisted recognition on bioinspired soft sensor arrays. ACS Nano; 2022; 16, pp. 6734-6743. [DOI: https://dx.doi.org/10.1021/acsnano.2c01548]
178. Tian, Y.; Su, D.; Lauria, S.; Liu, X. Recent advances on loss functions in deep learning for computer vision. Neurocomputing; 2022; 497, pp. 129-158. [DOI: https://dx.doi.org/10.1016/j.neucom.2022.04.127]
179. Navarro, S.E.; Goury, O.; Zheng, G.; Bieze, T.M.; Duriez, C. Modeling novel soft mechanosensors based on air-flow measurements. IEEE Robot. Autom. Lett.; 2019; 4, pp. 4338-4345. [DOI: https://dx.doi.org/10.1109/LRA.2019.2932604]
180. Toro-Ossaba, A.; Tejada, J.C.; Rúa, S.; López-González, A. A Proposal of Bioinspired Soft Active Hand Prosthesis. Biomimetics; 2023; 8, 29. [DOI: https://dx.doi.org/10.3390/biomimetics8010029] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36648815]
181. Kleeberger, K.; Bormann, R.; Kraus, W.; Huber, M.F. A survey on learning-based robotic grasping. Curr. Robot. Rep.; 2020; 1, pp. 239-249. [DOI: https://dx.doi.org/10.1007/s43154-020-00021-6]
182. Della Santina, C.; Arapi, V.; Averta, G.; Damiani, F.; Fiore, G.; Settimi, A.; Catalano, M.G.; Bacciu, D.; Bicchi, A.; Bianchi, M. Learning from humans how to grasp: A data-driven architecture for autonomous grasping with anthropomorphic soft hands. IEEE Robot. Autom. Lett.; 2019; 4, pp. 1533-1540. [DOI: https://dx.doi.org/10.1109/LRA.2019.2896485]
183. Gupta, A.; Eppner, C.; Levine, S.; Abbeel, P. Learning dexterous manipulation for a soft robotic hand from human demonstrations. Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Daejeon, Republic of Korea, 9–14 October 2016; pp. 3786-3793.
184. Mandikal, P.; Grauman, K. Dexvip: Learning dexterous grasping with human hand pose priors from video. Proceedings of the Conference on Robot Learning; Auckland, New Zealand, 14–18 December 2022; pp. 651-661.
185. Choi, C.; Schwarting, W.; DelPreto, J.; Rus, D. Learning object grasping for soft robot hands. IEEE Robot. Autom. Lett.; 2018; 3, pp. 2370-2377. [DOI: https://dx.doi.org/10.1109/LRA.2018.2810544]
186. Arapi, V.; Zhang, Y.; Averta, G.; Catalano, M.G.; Rus, D.; Della Santina, C.; Bianchi, M. To grasp or not to grasp: An end-to-end deep-learning approach for predicting grasping failures in soft hands. Proceedings of the 2020 3rd IEEE International Conference on Soft Robotics (RoboSoft); New Haven, CT, USA, 15 May–15 July 2020; pp. 653-660.
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; Tianze Hao 2
; Liu, Yibo 1 ; Xiao, Huaping 3 ; Liu, Shuhai 3 ; Zhu, Hongwu 1 1 College of Mechanical and Transportation Engineering, China University of Petroleum-Beijing, Beijing 102249, China;
2 Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin 300384, China;
3 College of Mechanical and Transportation Engineering, China University of Petroleum-Beijing, Beijing 102249, China;