areviewonanthropomorphicroboticend-eﬁectors · how far is the human hand?...

How Far Is the Human Hand?

A Review on Anthropomorphic Robotic End-effectors

L. Biagiotti, F. Lotti, C. Melchiorri, G. Vassura

DEIS - DIEM, University of Bologna

Via Risorgimento 2,

40136 Bologna, Italy

{lbiagiotti, cmelchiorri}@deis.unibo.it, {fabrizio.lotti, gabriele.vassura}@mail.ing.unibo.it

Abstract

In this paper, some considerations about the state ofthe art and the current trends in the design and con-trol of “robot hands” are reported and discussed. Theauthors, on the basis of a long research activity forthe development of robotic end-effectors and relatedtechnologies, address what they consider key-pointsfor the development of “dextrous hands”, being re-lated this term not only to kinematics properties oraesthetic appearance, but also to sensory system, con-trol strategies, integration with the carrying arm andso on... . In particular, some indices are defined asan attempt to present a clear comparison of the dif-ferent “robot hands” presented in the literature. Theindices refer to the degree of anthropomorphism, thatis the resemblance with the human hand concerningaspect and mechanical structure, and to the level ofdexterity, resultant from the kinematic configuration,the sensory apparatus and the control system.

1 Introduction

With the growth of interest towards humanoid robots,anthropomorphism of robotic hands becomes a neces-sary design goal, that has been purposely addressedby most recent research projects, e.g. the Robo-naut hand by NASA (Lovchik and Diftler 1999,Ambrose, Aldridge, Askew, Burridge, Bluethmann,Diftler, Lovchik, Magruder and Rehnmark 2000),the DLR hands (J.Butterfass, G.Hirzinger, S.Knochand H.Liu 1999, Butterfass, Grebenstein, Liu andHirzinger 2001), the University of Tokyo hand (Leeand Shimoyama 1999), the Karlsruhe University ultra-light hand (Schulz, Pylatiuk and Bretthauer 2001),the GIFU hand (Kawasaki, Shimomura and Shimizu

2001), and others. Consistent levels of anthropo-morphism were present, however, in many previousdesign proposals, e.g. the Utah hand (Jacobsen,Iversen, Knutti, Johnson and Biggers 1986), the Stan-ford/JPL hand (Salisbury and B.Roth 1983), the UBhand (Melchiorri and Vassura 1992, Melchiorri andVassura 1993)and many others.The reason of such a tendency is easily understandableconsidering that the human hand is the most dexter-ous “device” created by the nature during a millenaryevolution. Therefore it has became a model for themajority of the researchers in the field of robotic ma-nipulation, even if the projects, in which they are in-volved, do not explicitly require anthropomorphism asdesign specification.Despite such a trend towards the development ofhuman-like robotic hands, the results so far achievedare not yet comparable with the performances of ourhand. In this paper we try to define a “quantita-tive” measure of the distance of a generic robotic end-effector from this ideal target. Such a distance is theresult of several aspects, which jointly contribute tothe “real” dexterity of a robotic device. Therefore, thismeasure has been expressed by means of some indices,which refer to the degree of anthropomorphism, thatis the resemblance with the human hand concerningaspect and mechanical structure, and to the level ofdexterity, resultant from the kinematic configuration,the sensory apparatus and the control system. Theseindices allow to evaluate the features of the different“robot hands” available in the literature, and make aclear comparison between them. In this way it hasbeen possible to outline trends and open problems inrobot hand design, in an integrated perspective, whichconsiders mechanical aspects, sensing capabilities andcontrol issues.

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2 Basic concepts: anthropo-morphism, functional capabil-ities, dexterity

With the term “anthropomorphism” we intend the ca-pability of a robotic end-effector to mimic the humanhand, partly or totally, as far as shape, size, consis-tency, and general aspect (including color, tempera-ture, and so on) are considered. As the word itselfsuggests, anthropomorphism is related to external per-ceivable properties, and is not, itself, a measure ofwhat the hand can do. On the contrary, “dexterity”is related to actual functionality and not to shape oraesthetic factors.As a matter of fact we can find in the literature an-thropomorphic end-effectors with very poor dexter-ity level, even if they are called hands, as the tasksthey perform are limited to very rough grasping pro-cedures (Fukaya, Toyama, Asfour and Dillmann 2000).Similarly, we can find smart end-effectors, capable ofsophisticated manipulation procedures, without anylevel of anthropomorphism, e.g the DxGrip-II (Bicchiand Sorrentino 1995a). Anthropomorphism itself isneither necessary nor sufficient to achieve dexterity,even if it is quite evident that the human hand achievesa very high level of dexterity and can be considered avalid model for dexterous robotic hands.Anthropomorphism is a desirable goal in the design ofrobotic end-effectors mainly for the following reasons:

• the end-effector can operate on a man-orientedenvironment, where tasks may be executed by therobot or by man as well, acting on items, objectsor tools that have been sized and shaped accord-ing to human manipulation requirements;

• the end-effector can be tele-operated by man,with the aid of special-purpose interface devices(e.g. a data-glove), directly reproducing the op-erator’s hand behavior;

• it may be specifically required that the robot hasa human-like aspect and behavior, like for hu-manoid robots for purposes of entertainment, as-sistance, and so on.

2.1 Functional capabilities of a(robotic) hand

Besides the geometrical reproduction of the humanhand, the main research target remains the emulationof those functionalities which make it such a versatile

end-effector.Two are the main skills of a human hand:

• prehension, i.e. the hand’s ability to grasp andhold objects of different size and shape;

• apprehension, or the hand’s ability to understandthrough active touch.

In this sense, the human hand is both an output andinput device(see (Iberall and MacKenzie 1990)). Asoutput device, it can apply forces in order to obtainstable grasps or perform some procedures of manip-ulation while, as input device, besides providing in-formation about the state of the interaction with theobject during the task, it is capable to explore an un-known environment. The same characteristics shouldbe desirable in advanced robot hands. As a matterof fact, the application of robotic systems in unknownservicing environments requires dexterous manipula-tion abilities and facilities to execute complex opera-tions in a flexible way.

2.2 The meaning of dexterity

Generally speaking, with the term “dexterity” weintend the capability of the end-effector, operated bya suitable robotic system, to autonomously performtasks with a certain level of complexity. An exhaus-tive review of scientific work done so far about robotichands dexterity, with a complete list of references,can be found in (Bicchi 2000).Even if the word dexterity itself has a highly positivemeaning, it is useful to consider different levels ofdexterity, associated with growing complexity andcriticality of performable tasks. The dexterity domainfor robotic hands can be roughly divided in two mainareas, that are grasping and internal manipulation.Grasping is intended as constraining objects insidethe end-effector with a constraint configuration thatis substantially invariant with time (the object is fixedwith respect to the hand workspace), while internalmanipulation means controlled motion of the graspedobject inside the hand workspace, with constraintconfiguration changing with time. Further subdivi-sions of these two domains have been widely discussedin the literature (different grasp topologies on oneside (Cutkosky 1989), different internal manipulationmodes based on internal mobility and/or contactsliding or rolling on the other side (Bicchi 2000)).

Level of anthropomorphism

SizeContact surfacesKinematics

Main upper fingers

Opposable thumb

Palm Fourth finger Fifth finger Smoothness Extension Soft pads Overall size Size between links

w1

w11 w31

w3

w23w22w21w15w14w13w12 w32

w2

Figure 1: Evaluation graph of the anthropomorphism level of a robotic hand.

3 An anthropomorphism index

From the observation of the many robotic end-effectorsinspired by the human hand, we can conclude thatthe level of achieved resemblance with a human handis greatly variable from case to case, although all ofthem are defined as anthropomorphic hands.An interesting problem arises: what are the compo-nents of anthropomorphism and how the achieved levelof anthropomorphism can be quantified? Is it more an-thropomorphic a hand with five fingers but sharp rigidedges or one with three fingers well shaped and coveredwith a compliant layer? With the only aim of tryinga comparison between different design proposals pre-sented in the literature, the authors have defined ananthropomorphism index (in the following denoted asαx ), that is determined considering the following as-pects:

• Kinematics. This aspect considers the presence ofthe main morphological elements (principal up-per fingers, secondary upper fingers, opposablethumb, palm). Each of them, whose value rangesbetween 0 and 1 (according to the number of ar-ticulations inside each finger, in comparison withthe human case), gives a different contribution tothe kinematic evaluation score, weighted by thefactor w1i;

• Contact surfaces: extension and smoothness ofthe contact surfaces, that means the capability tolocate contacts with objects all over the surfaceof the available links, and availability of externalcompliant pads;

• Size. This contribution takes into account theactual size of the robotic hand compared with themedium size of a human hand and the “correct”

size ratio between all the links.

The index αx is calculated as the weighted sum ofthese three aspects, as shown in Fig. 1. If we considerthe structure of the human hand the final value forαx will be obviously equal to 1, therefore the indexassociated to a given design (e.g. αx = 0.75) providesan immediate idea of how far from the human shapeand aesthetics it places. For example, in Tab. 1 the in-dex αx relative to the UB-Hand, shown in figure Fig. 2,is presented (Melchiorri and Vassura 1992, Melchiorriand Vassura 1993).

4 A measure of dexterity

If the notion of dexterity is well settled, the wayto achieve it remains debated. The factors affectingthe actual capabilities of a robotic end-effector are somany, that often the analysis and above all the syn-thesis of dexterous hands do not take in the right con-sideration some of these elements, namely:

• morphological features;

• sensory equipment;

• control algorithms;

• task planning strategies;

• . . .

As a matter of fact, a very simple end-effector like arigid stick can be used for very sophisticated object-pushing tasks if used by a robot with visual and forcefeedback, while a complex articulated hand withoutadequate control can limit its dexterity to trivial self-adapting encompassing grasps. Evaluating the design

Evaluated Elements and Related Weights Value Result

Kinematics(w1 = 0.6)

Main Upper Fingers (w11 = 0.3) 1 0.18Opposable Thumb (w12 = 0.3) 0.8 0.144Palm (w13 = 0.2) 0.8 0.096Fourth Finger (w14 = 0.1) 0 0Fifth Finger (w15 = 0.1) 0 0

Contact Surfaces(w2 = 0.2)

Smoothness (w21 = 0.33) 0.9 0.0594Extension (w22 = 0.33) 0.9 0.0594Soft Pads (w23 = 0.33) 0.3 0.0198

Size (w3 = 0.2)Overall Size (w31 = 0.5) 1 0.1Size Between Links (w32 = 0.5) 0.9 0.09

Total 0.745

Table 1: Evaluation of the anthropomorphism level (index αx ) of the UB Hand II.

of a robotic hand, for example examining its kinemat-ical configuration or its sensory equipment, we candefine a potential dexterity intrinsically related to itsstructure.

4.1 Potential dexterity of a given me-chanical structure

It is quite evident that the potential dexterity of an ar-ticulated five-finger hand is better than that of a rigidstick, but it is obvious at the same time that muchof the potential dexterity of such a complex structurecan be wasted if proper actuation or sensory systemare not adopted and suitable control procedures arenot implemented. The evaluation of potential dexter-ity of an articulated hand depending on its kinematicalconfigurations (e.g. evaluation of manipulation ellip-soid) has been widely discussed in the literature, asreported in (Bicchi 2000).This kind of analysis requires the knowledge of somemechanical details and parameters, which are oftenunavailable. Therefore in the following, the potentialdexterity of a robotic hand will be roughly quanti-fied considering its functional capabilities (allowed bythe features of its mechanical structure, such as num-ber of degrees of freedom, smoothness of the contactsurfaces,...). In particular two main areas can be rec-ognized:

- hands with capability limited to grasping (sim-plified kinematical configuration or complex kine-matical configuration but reduced number of con-trolled degrees of freedom)

- hands that are capable of some kind of internalmanipulation.

Each of these two areas can be further subdivided intwo parts, distinguishing if the capability is limited to

fingertip operation or is extended to the other activeelements of the hand (whole hand grasp and manipu-lation). It is a rough subdivision, but can help to dis-tinguish between projects that may look aestheticallysimilar but in practice achieve quite different levels ofoperating capabilities. In order to make this compari-son easier, an index of the kinematic dexterity can beconstructed, by tacking into account the contributionof the different abilities (as shown in figure 3).

4.2 Potential dexterity related to thesensory apparatus

Dexterous manipulation, besides suitable mechanicalconfigurations, requires an adequate sensory system.In fact, the manipulation of an object needs preciseinformation about the configuration of the hand andthe state of the interaction with the environment (typ-ically the grasped object), and often the success (orsimply the completion time) of the task depends onthe level of this information. Since the human handcan be considered as the best known example of dex-terous end-effector, not only its structure but also itssensory system has become a paradigm for the re-searchers. As a matter of fact, many of them tendto adopt similar sensory configurations even in de-vices quite simple from the mechanical point of viewand not anthropomorphic at all. This is the case ofthe ROTEX Gripper (see Fig. 4) (Hirzinger, Brunner,Dietrich and Heindl 1993), whose equipment includesposition, force and tactile sensors.

The internal state of the human hand (position,velocity and force) is known by means of receptorscollocated in muscles, tendons, and joint capsules(for a complete overview see (Grupen, Henderson andMcCammon 1989, McCloskey 1978)). But the key

point of human dexterity is the richness of cutaneousinformation (high-frequencies vibrations, small scaleshape or pressure distribution, accelerations anddynamic forces, thermal properties). As a matterof fact, it has been shown that the lack of touchsensation, due for example to thick gloves (e.g. inspace) degrades the human ability and prolongs thetask completion time up to 80%, (Shimoga 1993).

If the sensing system of the human hand is thedesired target, unfortunately current technologies arestill far from their biological models, in particularconsidering transducers of touch sensations. As amatter of fact, tactile sensors are object of greatresearch efforts and the sensors currently availablestill present some important problems and functionallimitations: basically they can detect the contactpoint and the magnitude of applied forces (whileacceleration or vibration sensors are current underdevelopment (Howe and Cutkosky 1993) but notyet available for their integration in advanced robothands) but they are generally characterized by lowreliability, non-linear (hysteresis) phenomena and alarge number of electrical connections.

A synthesis of sensing technologies for manipulationThe standard equipment of an advanced robotic end-effector includes, besides sensors directly collocated inthe actuators (e.g. encoders), a number of additionalsensing elements; in particular three main classes canbe identified:

• Joint position sensors

Although position sensors on motor shaft are a so-lution simple, reliable and with a relatively highresolution (considering that the rotor motion is‘reduced’ several times by the mechanical trans-mission), back-lashes and deformations of the mo-tions transmissions can render the measure quite

Figure 2: The University of Bologna Hand II.

Level of mechanical dexterity

ManipulationGrasp

Fingertip Whole Hand

w1=0.4

w11=0.6 w22=0.4w21=0.6w12=0.4

w2=0.6

Fingertip Whole Hand

Figure 3: Evaluation graph of the kinematic dexteritylevel of a robotic hand.

short range distance sensors

tactile sensor array

medium range distance sensor

force/torque sensing unit

Figure 4: Sensory equipment of ROTEX gripper.

rough. Besides, the use of non-rigidly coupledjoints or under-actuated systems makes the min-imal solution of motor encoders not applicable,since a well-defined relation between the rotor po-sitions and the joint configuration does not exist:in general it depends also on external conditions(e.g. contact with the grasped object). In anycase, when a single motor is used to drive morethan one joint, and, in general, in order to im-prove the position measurements, additional posi-tion sensors must be added directly (or as close aspossible) to the joints in the kinematic chain. Po-sition sensors are based on different physical prin-ciples and methods: Hall effect sensors (e.g. theposition sensors on the gripper designed by theUniversity of Bologna, (Biagiotti, Melchiorri andVassura 2000)), potentiometer, optical sensors(e.g. in the DLR hand I,(Butterfass, Hirzinger,Knoch and Liu 1998)), and so on.

• Interaction sensors

If the sense of touch (and in general force informa-tion) is the main reason of human hand dexter-ity, a robot hand, which will physically interact

with the environment, can not leave aside forcesensing. The measure of the interaction can bedone in different way, but schematically it is pos-sible to find three alternative methods (completeoverview are available in (Nicholls and Lee 1989,Nicholls and Lee 1999, Melchiorri 2001)); on onehand the force exchanged with the external envi-ronment can be known by means of force/torquesensors collocated within the kinematic chain ofthe end-effector, on the other hand tactile sensors,directly placed on external surface, can provideinformation on the contact area and force magni-tude when the interaction occurs. In the middle,Intrinsic Tactile (IT) can be considered.Force/torque sensors measure the efforts exertedby fingers, at different levels and in different way:it is possible to detect the torques on finger joints,or consider the tension of tendons (which oftenare used to transmit the motion in robot hands),or if the mechanical chain is back-drivable mea-sure the force/toruqe provided by the actuators.Other kind of force/torque sensors are able to de-tect all the components of the applied wrench; ba-sically, the major part of these devices are trans-ducers which measure forces/torques by means ofthe induced mechanical strains on flexible partsof their mechanical structure. The mechani-cal strains are in turns measured by elastomers(strain gauges), properly glued on the structure,that change their resistance according to local de-formations. Based on a force/torque sensor withknown external shape and connected to a link ofa manipulator (see Fig. 5), the IT sensor has thepossibility to determine, when a contact is estab-lished between the link and an object, both theapplied wrench and the position of the contactcentroid on the surface of the link. For this rea-

Strain Gauges

Force sensor

Finger-link shell

(a) (b)

Figure 5: An IT sensor (a) within a finger of the UB-Hand II (b).

son, the IT sensor can be considered an interme-diate solution between force sensors and tactile

ones, even if one of the main drawbacks of thistechnology is the fact that they can not detectthe difference between one contact and multiplecontacts in the same structure (producing in thesecond case wrong estimations) and measure theshape/extent of the contact area. For this pur-poses, that is to determine the exact shape andposition of contact (possibly not-punctual) area,tactile sensors are used. Usually, they consist in amatrix (array) of sensing elements. Each sensingelement is referred to as a taxel (from “tactile ele-ment”), and the whole set of information is calleda tactile image. Main goal of this class of sensorsis to measure the map of pressures over the sens-ing area, allowing to get geometrical information(position and shape of contacts), as well as knowl-edge about mechanical properties (e.g. frictioncoefficient), and to detect when a slip conditionoccurs. In order to realize this kind of transducersseveral technologies have been developed, rangingfrom piezoresistive to magnetic, to optical effects(Nicholls and Lee 1989, Nicholls and Lee 1999).

• Additional sensors

Additional sensors can be added for particularapplications or to obtain specific capabilities; forexample end-effectors for space activities are of-ten equipped with proximity sensors and/or cam-eras directly installed within the hand (Butterfasset al. 1998). Other classes of sensors, whichcan increase the dexterity of a robot hand, in-clude accelerations or vibrations sensors (Howeand Cutkosky 1993), but their development is stillin progress.

A comparative indexIn order to give an immediate idea of the complexity ofthe adopted solutions in some noticeable examples ofrobot hands, we have defined an index σx which takesinto account the sensory equipment. In Table 2 thesensory apparatus of the UB Hand II has been consid-ered: the index is the result of evaluation of the threeclasses defined in the previous section, considered withdifferent weights according the level of dexterity theycan, in authors’ opinion, allow.Sensors that detect the status of the interaction(force/torque and tactile sensors) are considered aspreeminent to achieve dexterity. Moreover, it is worthto notice that tactile “array” sensors and intrinsic tac-tile sensors are treated as alternative: the informationthey provide are quite different and normally used fordifferent aims (planning the former, control the lat-ter). Tactile capabilities are further specialized con-

sidering their peculiar features:

- distribution on the robotic devices(fingertips/phalanges/palm) and number ofdetectable force/torque components for ITsensors;

- distribution, covering (partial/total of the fingerlink surfaces), and spatial resolution for tactilearray sensors.

The index σx can be very useful to compare differentdesigns and to have an immediate idea of how differentresearchers have faced the problem of dexterity.Moreover, it provides a measure of the gap with thehuman hand, whose index is not far from one (notexactly one, because of the lack of some sensors, suchas proximity sensors)

5 Integration

Besides the dexterity or the anthropomorphism, in au-thors’ opinion a key point in the design of a roboticend-effector is the integration. This term has severalmeanings, but all of them are fundamental in robothands design.As a matter of fact, a right integration between me-chanical parts, sensors and electronics systems andcontrol algorithms is one of the most stressed con-cepts in the design of robotic devices and automaticsystems in order to achieve structural simplification,increase of reliability, and drop of costs. In particularthis is true for a dexterous robot hands, which usu-ally are extremely complex devices with quite smalldimensions. Moreover, as shown in Sec. 4, the dexter-ity and the functional capabilities of robot hands arethe result of several contributions, which must bal-anced as much as possible. Therefore, as stated inSec. 4.2, it is very important to properly match me-chanical structure and sensory apparatus and also amedium-complexity hand can be dexterous and effec-tive if an adequate mix has been done.But, considering a robotic end-effector, integrationconcerns also the relation between the hand to be de-signed and the rest of the robotic system, consideringboth the physical parts of the system (structural in-tegration) and the way they interact or cooperate inorder to accomplish manipulation tasks (functional in-tegration). Structural integration directly determinesmechanical design guidelines, while functional integra-tion is mainly a conditioning goal as far as controlstrategies and task planning procedures are concerned.

5.1 Structural integration

Two different concepts about the structural integra-tion of robotic hands are described in the literature,which can be summarized with the following formula:

• Modular Hands (MH), Fig. 6.a;

• Integrated design Hands (IH), Fig. 6.b.

(a) (b)

Figure 6: Example of modular hand (DLR Hand II)(a) and of hand-arm structural integration (RobonautHand) (b).

In the former case, the hand is considered like an in-dependent device to be applied at the end of an arm:the same hand can be applied to any kind of arm be-cause it has been designed independently of it (exam-ples of this approach are the DLR Hands (J.Butterfasset al. 1999, Butterfass et al. 2001), the Barret Hand(Townsend 2000), the Salisbury’s hand (Salisbury andB.Roth 1983),...).In the latter case, reproducing the biological model,the hand is considered a non-separable part of thearm, deeply integrated with it: the hand and the armare jointly designed and cannot be conceived as sepa-rate subsystems (as examples of this approach we canremember the Robonaut hand (Lovchik and Diftler1999, Ambrose et al. 2000), the UB Hand (Melchiorriand Vassura 1992, Melchiorri and Vassura 1993)).The main difference between these two approachesis that a modular hand must contain all its func-tional components (actuators, sensors, electronics,etc:), while an integrated system (hand + arm) candistribute these components in the whole structure,placing them where room is available. The differentdesign approaches have the most evident implicationsin the placement of the actuators, necessary to move

Evaluated Elements and Related Weights Value Result

Position(w1 = 0.2)

Joint Position Sensors (w11 = 1) 1 0.2

Interaction(w2 = 0.6)

Force/Torque Sensors (w21 = 0.3) 0.4 0.072

TactileSensors(w22 = 0.7)

Intrinsic # Axis (w2211 = 0.5) 1 0.126(w221 = 0.6) Placement (w2212 = 0.5) 1 0.126

Array(w222 = 0.4)

Spatial Resolution (w2221 = 0.3) 0 0Covering (w2222 = 0.2) 0 0Placement (w2223 = 0.5) 0 0

Additional(w3 = 0.2)

Proximity,Vision, Dynamic Force Sensors (w31 = 1) 0 0

Total 0.524

Table 2: Evaluation of the sensory equipment of the UB Hand II.

the hand joints.In the first case all the needed actuators have to beplaced in the hand, while in the second case the handdesign is developed considering the possibility to putthe actuator in the forearm. Each of these two modal-ities has advantages and drawbacks. At present, bulkand performance of available actuators make very dif-ficult to host the required number of actuators insidethe hand. As a matter of fact, the size of the proposedmodular hands is larger with respect to the humanhand, the grasping forces are weaker and the overalldesign seems complex and not enough reliable.On the other side, the choice of integrating the handand the arm with simultaneous design allows theplacement of actuators for example in the forearm.The size and bulk of stronger actuators is no longer aproblem, but many other problems arise due to theneed of transmitting the motion through the wristjoints. There are pros and cons on both sides, butit is in authors’ opinion that an integrated hand canreach more easily a high level of anthropomorphism,at least with the technical resources available so far.

6 A review of robotic handswith some degree of anthro-pomorphism

Several robotic hands, more or less anthropomorphic,have been developed over the past two decades. Thegoals of each project were most times rather differ-ent, and the results are not easily comparable to thepurpose of declaring one project better than another.Anyway, in order to point out the effectiveness of eachcontribution and to trace the historical evolution ofthis sector of robotics, a classification of the poten-tial dexterity and level of achieved anthropomorphism

of each design can help to outline results, tendencies,open problems and goals for future evolution of re-search.In Tab. 3-10 a survey of some noticeable examplesof robotic hands is reported, considering the the mainfeatures of the mechanical design, as well as of theadopted sensory system. The review is limited to thoseprojects that clearly addressed the achievement, at asignificant level, of both dexterity and anthropomor-phism.

From the data collected in the tables, the indices men-tioned in Sec. 3, 4 have been computed for each handand graphically displayed in order to give a syntheticidea of the main characteristics of each project andto compare the different designs. In order to give anhistorical perspective of the considered aspects (an-thropomorphism, dexterity,...), the robot hands arepresented according to a chronological order.Firstly, in Fig. 7 the anthropomorphism level has beenconsidered: it is clear that in last years (in particularin the last 5-6 years) the interest towards fully anthro-pomorphic devices has been growing. As a matter offact the kinematic structure of robotic hands becomesmore and more close to the human model and the dis-similarity with our hand mainly concern the size andthe “skin”. If the former difference is above all dueto technological problems (in particular, to the lack ofminiaturized actuators), the latter strongly dependson a traditional way of designing robotic devices. De-spite it has been recognized that suitable contact sur-faces (in particular soft pads) can greatly enhance (be-sides their appearances) the dexterity of robot hands(Shimoga and Goldenberg 1992), only in the last yearsthis issue has been explicitly faced and the first en-doskeletal structures, apt to be integrated with softlayers, have been presented (Schulz et al. 2001, Lottiand Vassura 2002).

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Figure 7: Anthropomorphic level of the reviewedrobotic hands.

As mentioned in Sec.3 anthropomorphism and dex-terity are orthogonal concepts; this is evident if weconsider the other two defined index, that is the de-gree of dexterity related to the mechanical structureand to the sensory equipment, respectively reportedin Fig. 8 and 9. Tacking into account the me-

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Figure 8: Potential dexterity related to the mechanicalstructure of the reviewed robotic hands.

chanical structure, it can not be observed the trend,which characterizes the anthropomorphism level, to-wards an increase of dexterity. There are examplesin the scale of evolution, from very anthropomorphicbut low dexterity designs (it is the case of hands sim-ply oriented to adaptable grasp applications, e.g. theTuat/Karlsruhe Hand (Fukaya et al. 2000) and theLaval Hand (Underactuated robotic hands webpage ))to fairly dexterous but less anthropomorphic ones. InFig. 10 possible relations between anthropomorphismand dexterity are displayed, considering some notableexamples of robotic hands. These designs are usu-

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0,3

0,4

0,5

0,6

0,7

0,8

Oka

da H

and

Sta

nfor

d/JP

L H

and

Ultr

alig

ht H

and

Tuat

|Kar

lsru

he H

and

DLR

Han

d II

Toky

o H

and

Rob

onau

t Han

d

Dis

t Han

d

LMS

Han

d

DLR

Han

d I

UB

Han

d II

Bar

ret H

and

Bel

grad

e/U

SC

Han

d

Uta

h/M

it H

and

Sha

dow

Han

d

Gifu

Han

d

Additional sensorsTactile Array sensorsIntrinsic Tactile sensorsForce/Torque sensorsJoint Position sensors

Figure 9: Potential dexterity related to the sensorysystem of the reviewed robotic hands.

Anthropomorphism

Dex

terit

y

Human hand

Barret handTuathand

Robonaut hand

Stanfordhand

HighLow

Low

Hig

h

Figure 10: Relation between anthropomorphism anddexterity.

ally associated to very restricted and limiting specifi-cations and precise purposes:

- Salisbury, designing the Stanford/JPL hand, ex-plicitly focus the problem of dexterity, but no con-siderations about resemblance with the humanhand have been done;

- the target of the Tuat/Karlsruhe hand is to ex-ploit the structure of the human hand in order toachieve good grasp capabilities with a very lowcomplexity (only one actuator has been used);

- Robonaut hand aims to substitute the humanhand concerning both functional capabilities andshape/structure;

- Barret hand is, according the definition of its de-signer, a grasper and therefore neither anthro-pomorphism nor high-level of dexterity has beenspecifically addressed.

High dexterity is usually synonym of complexity. Inthis sense the designs of the reported robotic hands ap-

pear very coherent, according to the criterion of inte-gration between mechanical and electronic parts men-tioned in Sec. 5. As a matter of fact hands showingthe highest degrees of structural dexterity, and there-fore the largest number of controlled degrees of free-dom and actuators, are characterized by an extremelycomplex sensing apparatus. Conversely commercialhands (like Barret hand or Shadow hand), that mustbe particularly reliable and consequently not too com-plex have only a basic set of sensors.In any case, if we observe the potential dexterity re-lated to the sensory system, all the projects show high-level equipments (compared with traditional robotmanipulators), including positions and force/torquesensors. Moreover the design of such an equipmentis somehow incremental, and often additional sensorsare employed afterwards. In particular, this is truefor tactile sensing: despite the contribution of tactilesensors to the dexterity of robotic hands is widely rec-ognized, their use is not settled yet. From the Fig.9 it is clear that a “final decision” between intrinsictactile sensors, tactile array sensors, or both has notbeen definitely made and it is currently an importantresearch topic in the field of robot manipulation.

7 Potential or real dexterity?The role of control

As stated by Bicchi (Bicchi 2000), citing the Greekphilosopher Aristoteles, one of the (old) theories re-garding the relationship between human hands andmind claims that “because of his intelligence he (man)has hands”. Despite, researches of paleoanthropolo-gists have shown that the converse opinion, which con-siders the development of the brain of human beingsas a result of the structural dexterity of their hands, ispreferable, the former theory gives an insight into theimportance of the “intelligence”(in a broad sense) inorder too obtain the dexterity of an (artificial) hand.As shown in previous sections, the dexterity of a robothand is the result of its mechanical structure as well asof its sensory equipment. Adding up the contributionssketched in Fig. 8 and Fig. 9 it is possible to quantifythe overall degree of dexterity of the reviewed hands.At this point, a first consideration is that some of thedevices taken into account are not distant (consider-ing their structure and their features) from the humanhand but the tasks they can autonomously perform arestill simple and quite far from the human capabilities.Therefore, in order to estimate the real dexterity ofa robot hand, the “intelligence”, that is control algo-

rithms and task planning strategies, can not be ne-glected. Indeed, the control is a key element, whichputs potential dexterity into real one and is the mainreason of the success of the human hand.Because of its “control system”, the human hand canfully exploit its complex structure. The same does nothappen for robot hands: as qualitatively shown in Fig.11.a their actual dexterity is considerably lower thanthe dexterity given by their structure and paradoxi-cally some simple devices with suitable control strate-gies may be more dexterous than a complex robothand (see Fig.11.b). A tangible example of such asmart device is given by Dx-Grip II, a 2-jaw gripperdeveloped by Bicchi et al. (Bicchi and Marigo 2002),able to arbitrarily change the position/orientation ofquite general objects, by means of rolling.A number of theoretical works show that rolling and

Potential dexterity

Anthropomorphichands

Simpledevices

Rea

l dex

terit

y

Human hand1

1

(a)

Potential dexterity

Rea

l dex

terit

y

Human hand1

1P2P1

D1

D2

(b)

Figure 11: Potential versus real dexterity: general case(a) and an example (b).

sliding can greatly enhance the robot dexterity (Brock1988, Howe and Cutkosky 1996, Payandeh 1997, Bic-chi and Sorrentino 1995b) but, despite the effective-ness of manipulation by rolling or sliding can be ob-served also in human beings, these results are not ap-plied to complex robot hands. In the same way, theuse of tactile sensors for direct servoing have beenthe subject of several recent works (Okamura andCutkosky 1999, Moll and Erdmann 2002), but prac-

tical demonstrations of the achieved results has beendone only by means of special purpose robotic devices.The challenge for the future is to take the results men-tioned above to robot hands, exploiting the complex-ity of the available devices (which are potentially verydexterous) and fill the gap between theoretical specu-lations and practical applications.

8 Conclusions

This paper presents an attempt to classify robotichands, proposed so far by research institutions orindustries, focusing on their anthropomorphism anddexterity. In particular, the human hand has beentaken as paradigm, and the “distance” of the reviewedrobot hands from this ideal target has been estimateby taking into account the degree of anthropomor-phism as well as the level of dexterity. In this wayit has been possible to outline trends and open prob-lems in robot hand design, in an integrated perspec-tive, which considers mechanical aspects, sensing ca-pabilities and control issues.

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Table 3: Main features of Okada and Stanford/JPL Hands.

Projectidentification

Project denomination Okada Hand Stanford/JPL Hand

Reference author(s) T. Okada Salisbury

Research institute Electrotechnical laboratory,Japan

Stanford University

Year of presentation 1979 1983

Reference (Okada 1986) (Salisbury andB.Roth 1983, Chase andLuo 1997, Fearing 1987)

Picture

Mechanicalstructure:

Arm/hand Integration MH IH

Main upper fingers X X

Kinematicalscheme:

Opposable thumb X X

Fourth finger - -

Fifth finger - -

Palm - -

Number of links 12 10

Number of joints 11 9

Number of controlleddegrees of freedom

11 9

Morphologicalfeatures:

Size w. r. to a humanhand

> =

Surfaces apt to contactwith objects

Fingertips/Phalanges Fingertips

Contact surfacesmoothness and conti-nuity

Fair Poor

Mechanicaldetails

Structural design con-cept

Exoskeletal Exoskeletal

Actuator location Remote Remote

Actuation type Electrical revolute motor Electrical revolute motor(DC)

Act. joints back-drivability

Not Found (NF) X

Kind of not-actuatedjoints

- -

Type of transmission Tendons Tendons

Transmission routing Pulleys/sheaths Pulleys/sheaths

Sensors:

PositionMotor position sensors X X

Joint position sensors Potentiometers -

Force/Torquesensors

Joint torque sensors - -

Tendon tension sensors - X

Motor effort sensors X -

Contactsensors

Intrinsic tactile sensors - Fingertip force sensors

Tactile array sensors - 8×8 tactile sensors arraywith complete coverage ofthe cylindrical fingertip

AdditionalSensors

Table 4: Main features of Utah/Mit And Belgrade/USC Hands.


Project denomination Utah/Mit Hand Belgrade/USC Hand

Reference author(s) Jacobsen G.A. Bekey/R. Tomovic/I.Zeljkovic

Research institute Utah University University of Belgrade


Reference (Jacobsen et al. 1986, Mc-Cammon andJacobsen 1990, Johnstonet al. 1996)

(Bekey et al. 1990)

Picture


Arm/hand Integration IH MH

Main upper fingers X

Kinematicalscheme:

Opposable thumb X X

Fourth finger X X

Fifth finger - X

Palm X X




16 4 (2 thumb+2 fingers)



= =


Fingertips/Phalanges/Palm Fingertips/Phalanges/Palm


Good Fair

Mechanicaldetails




Actuation type Pneumatic actuator DC Motors


X -


- Rigid passive-driven joints

Type of transmission Tendons Linkages

Transmission routing Pulleys -

Sensors:


Joint position sensors Rotary Hall effect Rotary potentiometers

Force/Torquesensors


Tendon tension sensors X -

Motor effort sensors - -

Contactsensors

Intrinsic tactile sensors - -

Tactile array sensors Capacitive tactile sensorscovering finger segments andpalm

Touch-pressure sensors(Force sensing resistor) onfingertips

AdditionalSensors

Table 5: Main features of Barret and UB (II) Hands.


Project denomination Barret Hand UB Hand II

Reference author(s) W.T.Townsend Bonivento/Melchiorri/Vassura

Research institute Barret Technology, Inc Bologna University


Reference (Townsend 2000, Barrethand webpage )

(Melchiorri and Vassura1992, Melchiorri andVassura 1993, Boniventoand Melchiorri 1993)

Picture




Kinematicalscheme:

Opposable thumb X X

Fourth finger - -

Fifth finger - -

Palm X X




4 13 (2 wrist+11 hand)



= =




Fair Good

Mechanicaldetails


Exoskeletal Endoskeletal

Actuator location Inside the fingers Remote

Actuation type Electrical revolute motors(Brushless)

Electrical revolute motors


X X


Underactuated -

Type of transmission Spur and worm gear Tendons

Transmission routing - Pulleys/sheaths

Sensors:

PositionMotor position sensors Optical encoders X

Joint position sensors - Hall-effect based

Force/Torquesensors

Joint torque sensors Strain-gauges based -

Tendon tension sensors - -

Motor effort sensors Implicit (by means of break-away clutches)

-

Contactsensors

Intrinsic tactile sensors - 6-axis IT-sensors in the pha-langes and the palm

Tactile array sensors - -

AdditionalSensors

Table 6: Main features of DLR (I) and LMS Hands.


Project denomination DLR Hand I LMS Hand

Reference author(s) Butterfass/Hirzinger/Knoch/LiuGazeau/Zeghloul/Arsicualt

Research institute DLR-German AerospaceCenter

Universite de Poities


Reference (J.Butterfass et al. 1999, Liuet al. 1999)

(Gazeau et al. 2001)

Picture




Kinematicalscheme:

Opposable thumb X X

Fourth finger X X

Fifth finger - -

Palm X X




12 16



À =


Fingertips/Phalanges/Palm Fingertips/Phalanges


Good Good

Mechanicaldetails



Actuator location Inside the finger Remote

Actuation type Electrical revolute motors Electrical revolute motors


X NF


Adaptive passive-drivenjoint

-

Type of transmission Tendons Tendons

Transmission routing Pulleys Pulleys/Sheaths

Sensors:


Joint position sensors Optical based Potentiometers

Force/Torquesensors

Joint torque sensors X -

Tendon tension sensors - Implicit (tendon elongation)

Motor effort sensors - -

Contactsensors

Intrinsic tactile sensors x-y force sensor on fingertips -

Tactile array sensors Tactile sensors(Force sens-ing resistor)in each fingerlink

-

AdditionalSensors

Stereo-camera in the palmand light projection diodesin the fingertip to simplifyimage processing

Table 7: Main features of DIST and Robonaut Hands.


Project denomination DIST Hand Robonaut Hand

Reference author(s) Cafes/Cannata/Casalino C.S.Lovhik/M.A.Diftler

Research institute DIST-Universita di Genova NASA Johnson Space Cen-ter


Reference (Caffaz and Cannata 1998,Dist hand webpage )

(Lovchik and Diftler 1999,Ambrose et al. 2000, Robo-naut webpage )

Picture




Kinematicalscheme:

Opposable thumb X X

Fourth finger X X

Fifth finger X X

Palm X X


Number of joints 16 22 (2 wrist + 20 hand)


16 14 (2 wrist + 12 hand)



> =


Fingertips Fingertips/Phalanges/Palm


Poor Very Good

Mechanicaldetails


Exoskeletal Endoskeletal


Actuation type Electrical revolute motors Electrical revolute motors(Brushless)


NF X


- Adaptive Passive-drivenjoints

Type of transmission Tendons Flex-shaft + lead screw

Transmission routing Pulleys/Sheaths -

Sensors:


Joint position sensors Hall-effect based X

Force/Torquesensors

Joint torque sensors -

Tendon tension sensors X

Motor effort sensors -

Contactsensors

Intrinsic tactile sensors 3-axis fingertip force sensors -

Tactile array sensors - FSR (Under development)

AdditionalSensors

Table 8: Main features of Tokyo and DLR (II) Hands.


Project denomination Tokyo Hand DLR Hand II

Reference author(s) Y.K.Lee/I.Simoyama Butterfass/Grebestein/Hirzinger/Liu

Research institute Univ.of Tokio,bunkyo-ku,J DLR-German AeropsaceCenter


Reference (Lee and Shimoyama 1999) (Butterfass et al. 2001)

Picture


Arm/hand Integration IH MH


Kinematicalscheme:

Opposable thumb X X

Fourth finger X X

Fifth finger X -

Palm X X




12(1 wrist + 11 hand) 13



= À




Very good Good

Mechanicaldetails


Endoskeletal Endoskeletal

Actuator location Remote Inside the fingers

Actuation type Pneumatic Mckibben artifi-cial muscles

Electrical revolute motors


X X


Rigid passive-driven joints Rigid passive-driven joints

Type of transmission NF Harmonic drives/gears

Transmission routing NF -

Sensors:


Joint position sensors - Potentiometers

Force/Torquesensors

Joint torque sensors - Strain-gauges based


Motor effort sensors X -

Contactsensors

Intrinsic tactile sensors - 6-axis force sensors in thefingertips

Tactile array sensors Pressure sensors foreseen -

AdditionalSensors

Table 9: Main features of Tuat/Karlsruhe and Ultralight Hands.


Project denomination Tuat/Karlsruhe Hand Ultralight Hand

Reference author(s) Fukuya/Toyama/Asflur/DillmanSchultz/Pylatiuk/Bretthaue

Research institute Tokyo and Karlsruhe Uni-versities

Research center of Karlsruhe


Reference (Fukaya et al. 2000) (Kawasaki et al. 2001)

Picture


Arm/hand Integration IH IH


Kinematicalscheme:

Opposable thumb X X

Fourth finger X X

Fifth finger X X

Palm X X




1 13 (3 wrist+ 10 fingers)



= À


Fingertips/Phalanges Fingertips/Phalanges/Palm


Poor Good

Mechanicaldetails


Endoskletal Exoskeletal

Actuator location Remote Inside the fingers

Actuation type Electrical revolute motors Pneumatic


NF X


Adaptive passive-drivenjoints

Rigid passive-driven joints

Type of transmission Link mechanisms Direct drive

Transmission routing - -

Sensors:


Joint position sensors - Bending sensors

Force/Torquesensors



Motor effort sensors Self-adapting mechanism -

Contactsensors

Intrinsic tactile sensors - -

Tactile array sensors - Pressure sensors in fingerlinks

AdditionalSensors

Table 10: Main features of GIFU and Shadow Hands.


Project denomination Gifu Hand Shadow Hand

Reference author(s) Kawasaki/Shimomura/Shimizu

Research institute Gifu University Shadow Robot CompanyLtd


Reference (Jacobsen et al. 1986,Kawasaki et al. 1999)

(Shadow hand webpage )

Picture




Kinematicalscheme:

Opposable thumb X X

Fourth finger X X

Fifth finger X X

Palm X X




16 23 (4×4 fingers + 5 thumb+ 2 wrist)



= ≥




Good Fair

Mechanicaldetails



Actuator location Inside the fingers Remote

Actuation type Built-in DC Maxon servo-motors

Pneumatic


- X


Rigid passive-driven Joints -

Type of transmission Worm gear Tendons

Transmission routing - NF

Sensors:

PositionMotor position sensors X -

Joint position sensors Hall-effect based

Force/Torquesensors

Joint torque sensors

Tendon tension sensors

Motor effort sensors X

Contactsensors

Intrinsic tactile sensors 6-axis fingertip force sensors

Tactile array sensors Distributed resistive tactilesensors

AdditionalSensors

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