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Wang, Y.; Hao, T.; Liu, Y.; Xiao, H.; Liu, S.; Zhu, H. Dexterity of Anthropomorphic Soft Hand. Encyclopedia. Available online: https://encyclopedia.pub/entry/55860 (accessed on 03 May 2024).
Wang Y, Hao T, Liu Y, Xiao H, Liu S, Zhu H. Dexterity of Anthropomorphic Soft Hand. Encyclopedia. Available at: https://encyclopedia.pub/entry/55860. Accessed May 03, 2024.
Wang, Yang, Tianze Hao, Yibo Liu, Huaping Xiao, Shuhai Liu, Hongwu Zhu. "Dexterity of Anthropomorphic Soft Hand" Encyclopedia, https://encyclopedia.pub/entry/55860 (accessed May 03, 2024).
Wang, Y., Hao, T., Liu, Y., Xiao, H., Liu, S., & Zhu, H. (2024, March 05). Dexterity of Anthropomorphic Soft Hand. In Encyclopedia. https://encyclopedia.pub/entry/55860
Wang, Yang, et al. "Dexterity of Anthropomorphic Soft Hand." Encyclopedia. Web. 05 March, 2024.
Dexterity of Anthropomorphic Soft Hand
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This entry is adapted from the peer-reviewed paper 10.3390/act13030084

Humans possess dexterous hands that surpass those of other animals, enabling them to perform intricate, complex movements. Soft hands, known for their inherent flexibility, aim to replicate the functionality of human hands. By mimicking the shape, structure, and functionality of human hands, soft hands can partially replicate human-like movements, offering adaptability and operability during grasping tasks. 

anthropomorphic soft hand soft robotics dexterous

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.

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. 
Figure 1. Schematic expression of (a) human hand joints; (b) and muscle groups and palm arches.

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 [25][26]. 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 [27][28].
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. [29][30][31].
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 [26][32], 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 [25], 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][33].
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][34]. 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][35][36]. 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 [37]. 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][38].
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 [39]. 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 [40].
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 [41]. Similarly, the RBO 2 hand, with a mere 7 DOFs, successfully executes 31 out of 33 grasp postures from the Feix taxonomy [34]. 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 [36].

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][42]. Moreover, the majority of soft hands, limited by soft mechanical design and manufacturing technologies, typically only exhibit several DOFs [5][34][43]. 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 [44][45][46]. 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][47].
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][48][49]. 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 [35]. 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 [47][50][51].
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 [52]. 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][53]. First, the thumb can oppose the other four fingers, which is a prerequisite for grasping objects [50]. Second, the thumb can simultaneously translate, rotate, and flex, which is an ability that other primate hands are incapable of [54]. 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 [55].
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 [56]. Many modular designs still treat the thumb the same as the other fingers, merely placing it in an opposing configuration [57], 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) [58]. 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 [51][59], while the RBO Hand 2 and 3 feature activatable anthropomorphic palms [6][34]. 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 [47][60]. A comparison of the main features of various types of soft hands is presented in Table 1. 
Table 1. 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 [4]
Dragonskin 10, Ecoflex 00-30 Casting molding Pneumatic coordinated control 6 Replicate the human-like grasp postures Anthropomorphic dextureous hand 2022 [7]
Silicone rubber, fibers Casting molding Pneumatic Open-loop control 12 Flexible palm Underactuated anthropomorphic hand 2013 [8]
Dragon skin 30 Casting molding   Open-loop control 21   Soft parallel palm 2023 [19]
Ecoflex 00-50, Mold Star 30, ABS Casting molding Pneumatic Open-loop control 13 Grasping planning Anthropomorphic dextureous hand 2018 [20]
Dragon Skin 10, fibers Casting molding Pneumatic Open-loop control 22 Flexible operation function Anthropomorphic dextureous hand 2022 [50]
TPU, ABS — Fluidic and Tendon actuation Open-loop control 5 SMA-Based Exo-Glove Soft Exo-Glove 2023 [61]
Resin, PET, Nylon gauze Planar laser cutting and stacking Pneumatic Open-loop control 6 Hybrid pneumatic actuators Underactuated anthropomorphic hand 2021 [62]
VytaFlex 20, ELASTOSIL M 4601 3D printing, soft lithography Pneumatic Open-loop control 12 Multi material 3D printed Anthropomorphic dextureous hand 2020 [63]
Dragon skin-10, Dragon skin-30, fibers Casting molding Pneumatic Open-loop control 3 SMP actuated Dextureous finger 2016 [64]
Dragon Skin 10, Ecoflex 00-30, nylon thread, fibers Casting molding Pneumatic Open-loop control 12 Highly integrated design Anthropomorphic dextureous hand 2023 [65]
Vero, Agilus30 3D printing Pneumatic Closed-loop control 5 3D printed fingers Underactuated anthropomorphic hand 2021 [66]
PDMS, CNTs 3D printing Light-driven Open-loop control 5 SMA actuated Underactuated anthropomorphic hand 2020 [67]
PDMS, SMA, fiberglass Casting molding Tendon-driven Open-loop control 10 SMA actuated Underactuated anthropomorphic hand 2023 [68]
TPU, SMA 3D printing SMA Open-loop control 10 Elastic joints and soft pads Anthropomorphic dextureous hand 2014 [69]
TPU 3D printing Pneumatic Open-loop control 10 Soft-Rigid Hybrid fingers Anthropomorphic dextureous hand 2023 [70]
Silicone rubber, ABS — Pneumatic Open-loop control 5 Self-healing soft fingers Underactuated anthropomorphic hand 2017 [71]
Dragonskin-10, ecoflex 00-30, fibers Casting molding Pneumatic Closed-loop control - Deployable, atraumatic grasper Surgical grasper 2014 [72]
Smooth-Sil 936, fibers Casting molding Fluid-driven Open-loop control 1 Pneu-net actuator Dextureous finger 2014 [73]
Ecoflex-30, SMA, PDMS Casting molding SMA Closed-loop control 5 ECF jet Underactuated anthropomorphic hand 2011 [74]
TPU 3D printing Tendon Open-loop control 5 Completely soft Soft Exo-Glove 2021 [75]
Electro-conjugate fluid(ECF) — ECF jet Open-loop control 5 Planar Laser Cutting and Stacking Underactuated anthropomorphic hand 2021 [76]
NinjaFlex, particles 3D printing Pneumatic Closed-loop control 3 Humanoid hand skeleton Dextureous finger 2019 [77]
Agilus30, Vero 3D printing Pneumatic Open-loop control 5 Pneumatic Exo-Glove Soft Exo-Glove 2016 [78]
Dragon Skin 10, Ecoflex 00-30 — Pneumatic Myoelectric control 4 3D printed Underactuated anthropomorphic hand 2017 [79]
Dragon skin 30, paper Casting molding Pneumatic Open-loop control 4 Dualmodule pneumatic actuator Nonanthropomorphic Grippers 2020 [25]
Silicone, PLA Casting molding Tendon-driven Open-loop control 2 Two-finger grip Nonanthropomorphic Grippers 2022 [26]
Silicone, fiber Casting molding Pneumatic Open-loop control 7 Active Palm Underactuated anthropomorphic hand 2016 [34]
Vero White, Tango Black 3D printing   Open-loop control 18 Soft-rigid hybrid hand Anthropomorphic dextureous hand 2018 [37]
TPU 3D printing Pneumatic Open-loop control 9 Hand sign language Anthropomorphic dextureous hand 2019 [42]
Fiber, M4601, memory foam Casting molding hydraulic Open-loop control 8 Underwater gripper Nonanthropomorphic Grippers 2016 [80]
Tendons, Agilus Black material 3D printing Tendon-driven Open-loop control 3 Grasping Underactuated anthropomorphic hand 2022 [43]
SmoothSil-960, MoldStar-30 Casting molding Pneumatic Open-loop control 9 Three-finger grip Nonanthropomorphic Grippers 2023 [81]
Smooth-Sil 945, Ecoflex 00-30 Casting molding Pneumatic Open-loop control 8 Delicate In-hand manipulation Soft robotic hand 2020 [82]

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 [83][84]. 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 [85][86]. 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 [87][88]. Other variable stiffness methodologies include interference-based methods [89], motor-based methods [90], variable modulus-based methods [91], electromagnetic field-based methods [92], and phase change material-based methods [93]. 
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 [94]. 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 [62]. 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 [95]. Liu et al. developed flexible hybrid pneumatic actuators for soft hand fingers [62]. 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 [96][97]. In wet environments, fingerprints enhance the grasping area, thereby improving the success rate [98][99]. This mechanism differs from the adhesive effect observed in tree frog toe pads or gecko claw setae, which operate at the nanoscale [100][101]. 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 [102]. 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 [103].

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Subjects: Engineering, Mechanical
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Yang Wang
,
Tianze Hao
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Yibo Liu
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Huaping Xiao
,
Shuhai Liu
,
Hongwu Zhu
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Update Date: 06 Mar 2024
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