Robot Hands: Past, Present, and Future
Tsuneo Yoshikawa
Department of Human and Computer Intelligence
Ritsumeikan University
Kusatsu, Shiga 525-8577, Japan
yoshikawa@ci.ritsumei.ac.jp
Abstract—Robot hands have been one of the major research
topics since the beginning of robotics because grasping andmanipulation of various objects by robot hands arefundamental functionalities of various robotic systems. A brief
survey of the history and current state of research on
multifingered robot hands both from hardware and software
aspects is presented. Some of our recent results in grasping
unknown objects optimally and performing various
manipulation tasks by robot hands with soft fingers are alsopresented. Some future research directions are discussed.

I.

INTRODUCTION

Grasping and manipulation of various known andunknown objects by robot hands are fundamental
functionalities for robotic systems, including humanoid
robots, industrial robots, and wheel-type mobile robots, to be
useful in various environments, such as home, publicbuildings, city area, factory, space, sea, and so on.
Motor function of humans may be considered asconsisting of their two main abilities: walking ability by feetand grasping and manipulation ability by hands. Much
research has been done for realizing these abilities by robotic
mechanisms. At first, robot hands were more actively studied
than robot feet and it was thought that robot hands weremore advanced than robot feet. However, after successful
development of humanoid walking robots by Honda in 1996
and by some other research groups after that, walking abilityof robots has advanced so quickly that I now feel thatdevelopment of robot hands for performing various skillfulgrasping and manipulation tasks turned out to be more urgent
and critical issue for realizing useful humanoid robot that canreally help people.
In this article, a brief survey of the history and current
state of research on grasping and manipulation bymultifingered robotic hands both from hardware andsoftware aspects will be presented. Some of our recent
results in grasping unknown objects and manipulating
various objects optimally by robot hands with two soft
fingers will also be introduced. Finally some future research
directions and issues will be discussed.

II.

HARDWARE

Various experimental multifingered mechanical hands
have been developed in the last 45 years or so. After several
preliminary mechanical hands, robotic hands with multiarticulated fingers for computer control have been developed.
Representative examples are three-fingered Okada-Hand(1979) [1], three-fingered Stanford/JPL hand (1981) [2], andthree-fingered Utah/MIT hand (1984) [3]. These hands haveseveral things in common. They use tendon cables and havelarge separate drive units.
Then around 2000 several new hands were developed:
four-fingered DLR hand (2000)[4], five-fingered Robonaut
Hand (1999)[5], and five-fingered Gifu Hand (2002)[6]. All
these hands do not use tendon cables but use somemechanical linkages such as lead screws and differential
bevel gears. The drive mechanisms are integrated into handitself or contained in the forearm. They are equipped withvarious sensors. The size of these hands is still a little larger
than our hands. Another interesting hand is the Threefingered High-Speed Hand (2003) [7] which can move sofast that it can even catch a falling ball.
More recently, small size hands that could be used forhumanoid robots have been developed, such as UniversalHand (2005) [8] and Shadow Dexterous Hand (2007) [9].
These hands have five fingers and are of human size. They
were developed by private companies. One interesting
feature is that both of them have 5dof thumb showing thedesigner’s view on the importance of the thumb. Anotherinteresting hand is five-fingered lightweight 328 g
Anthropomorphic Hand (2009) [10] with maximum fingertip
force more than 20 N.
The latest development is that two hands have beendeveloped with the purpose of actually mounting on
humanoid robots. One is the hand for HRP3P (2007) [11]and the other is the hand of TWENDY-ONE Robot [12].
Both hands have four fingers and have smaller number ofmotors than most of the hands mentioned above. Aninteresting feature of the latter hand is that the whole surface
is covered by soft skin and the fingers have nails.

From these developments, it can be said that the size andmechanical functionality of robotic hands are now coming
very close to human hand. Experimental realizations ofseveral specific manipulation tasks by using these hands
have also been reported. However, the control algorithms forthese tasks are rather task specific. Establishing more generalframework of planning and control for wider class ofmanipulation tasks will be an important future research topic.
III.

GRASPING

A. Analysis and Synthesis of Grasp
Various analytical studies of grasping by multifingered
hands have been done extensively. Among basic concepts
developed are grasp map (or grasp matrix) [13], hand
Jacobian [13], form and force closure grasps [14], and active
and passive force closures [15], and manipulability [16].
Determining the grasp position for any given object by amultifingered robot hand is a fundamental problem which
has not yet been solved satisfactorily. Many grasp quality
measures have been proposed [17]-[26] for grasp
optimization. See [17] for a very thorough survey ofliteratures until about 1992. A good short summary on the
topic is also given in [18].
In [19] and [20], two general grasp measures wereintroduced: they are radii of the largest balls, centered in theorigin of the wrench space, contained in the set of all
possible wrenches that can be resisted by applying allowable⊥
finger forces along the normal of the object surface, f i ,under two kinds of magnitude constraints max{ f i
and ∑

n
i =1

fi

⊥

⊥

}≤1

several sample objects. Our grasp measure introduced below
is also very heuristic.
Most of these studies are for rigid fingers which makepoint contacts with grasped objects. However, rigid fingers
sometimes have stability and versatility problem in practicalapplications. Partly because of this, hands with deformablesoft fingers have also drawn much attention [27]-[32].
B. Grasping by Two Soft Fingers
We have proposed a method of shape recognition andoptimal grasping of unknown planar objects [33] for arobotic hand system consisting of an arm, a hand with 2 softfingers, and an omnidirectional camera attached to the hand(Fig. 1). The fingertips are round and covered by thin andsoft skin made of elastic gel as shown in Fig. 2.
For shape recognition, we move the camera attached tothe hand around the object horizontally keeping the height ofthe camera constant and take images of the object fromdifferent viewpoints. Then by applying a two-dimensionalversion of the volume intersection method [34], we obtainthe visual hull in the horizontal plane, which corresponds toa polygon circumscribing the horizontal cross section of theobject.
For grasping we propose a simple grasp quality criterion
to obtain the best grasping position for the two soft fingers
based on a visual hull in a horizontal grasp plane. One meritof soft fingers is that they can grasp an object not only atsmooth surfaces but also at corners due to the deformation of
the finger surface. Taking this into account, the grasp qualityArm≤ 1 where n is the number of fingers. In[21] a simulator for grasp analysis, called GraspIt, wasdeveloped and the second grasp measure of [20] and thevolume of the set of all resistible wrenches were adopted forevaluation of grasps. A task-directed quality measure were
proposed in [22] based on the concept of task ellipsoid which
represents the set of all wrenches needed to perform the task.
A quality measure which takes into account the object shape
was proposed in [23] based on the object wrench set
consisting of wrenches caused by all possible disturbing
force distributions on the object surface. In [24], a method ofgrasp optimization using the concept of required external
force set was proposed.
Grasp quality measures mentioned above are all given interms of fingertip forces and external wrenches. In [25] aheuristic criterion was proposed for grasp of polygonal
objects using a three-fingered hand. The criterion is notdirectly related to the forces, but is given by a weighted sumof five terms: the arrangement of force directions,distribution of grasp points, grasp location with respect to thecentroid of polygon, number of convex vertices, and numberof grasp points located near the end of the edge. The
weighting factors were selected by using human intuition for

Soft-Finger Hand
Rotation of
CameraObjectOminidirectional
CameraFigure 1. System Structure

Figure 2. Inner Structure of Soft Fingertip

criterion [33] is given byc2

J (c1 , c 2 ) = k p J p (c1 , c 2 ) + k α J α (c1 , c 2 )(1)where c1 and c2 are a pair of candidate contact points on theobject surface. J p (c1 , c 2 ) is the term evaluating the global
location of the contact points and is a function of distancesd a and d b (see Fig.3). J α (c1 , c 2 ) is the term evaluating
the angle α i between the surface normal of the object andthe grasp force direction at the contact points.

ã1r

c2
ã1l

ã1r

c1

c1
(a) α i = 0ã1ã1l(b) α i =α ilFigure 4. Angle α i for Corners

When the contact point i is at a corner of the object, we
define the angle α i as the minimum absolute value in theinterval [ α ir , α il ] where α ir ( α il ) is the angle from thenormal vector on the right (left) side surface of the corner tothe grasp force direction. For instance, α i = 0 in the caseof Fig. 4 (a), and α i =α il in the case of Fig. 4 (b).

We have performed experiments of grasping prisms withvarious cross sections. Fig.5 shows the case of a rectangularprism. As can be seen from the figure, the prism was grasped
successfully at an intuitively proper grasp position. We arealso studying the relation between the criterion (1) and themeasured grasp positions by human hands [35]IV.

MANIPULATION

A. Control Synthesis for Manipulation
Various control algorithms for multifingered robot handsfor grasping and manipulation with fixed, rolling, and sliding
contacts have been proposed [14] [36]-[41] (see [40] for abrief survey on control algorithm for manipulation bymultifingered hands). They can roughly be divided into twogroups. One is impedance control and the other is hybrid
position and force control (see Fig.6). Impedance control
method controls the force indirectly through specification of
the mechanical impedance of the grasped object against
external forces. Hybrid control method controls
simultaneously the force in some directions and the positionin other directions. Although these control methods seem tohave not been used widely, they will be useful as thetheoretical baseline for developing more practical control(a) Initial position(b) Image taking 1(c) Image taking 2(d) Image taking 3(e) Grasping 1(f) Grasping 2

Figure 5. Experiment of Grasping a Rectanglar Prism

algorithms.
B. Manipulation by Soft-Fingered Robot Hands
Our result on dexterous manipulation of various objects
by a robot hand with two soft fingers [42] will be introduced.
This hand is basically controlled by a position-based PID
control algorithm. However, by utilizing the fingertip force
sensor measurement and taking advantage of the soft skin, it
tries to regulate the grasping force and to comply with theconstraints given by the manipulation task and excessive
external forces in the following way.
To control the grasping force between the two fingers,
the fingertip distance is changed according to the grasping(a) Impedance control
Figure 3. Parameters of the Criterion(b) Hybrid control

Figure 6. Control Methods of Miltifingered Hands

force information obtained from the fingertip force sensors.
A first-order filter with a rather large time-constant is used tosmooth out the fingertip force measurement.

We have shown experimentally that, by using the above
control algorithm, the hand can perform various
manipulation tasks stably and firmly, such as manipulationof a wooden cube in free space, opening and closing of abottle cap, and turning a crank. The result of manipulation ofa wooden cube is shown below. The size of cube was
50[mm] on a side and the weight was 77[g]. To grasp the
cube, the distance between the two fingertips was madesmaller by using position control until the grasping force
became a target value. When the grasping force reached thetarget value, the cube was lifted upward by 30[mm]. Then,
while keeping the grasping force to the desired force, eachfingertip was moved following some circular paths in ahorizontal plane, a vertical plane, and a diagonal plane. The
sequential images of this experiment are shown in Fig.7 forthe case of grasp target force of 3[N]. Fig.8 shows thechange of the distance between the two fingers and thegrasping force. From the figure it can be seen that the(a) Initial pose(b) Grasp(c) Lift up(d) Rotate 30[deg](e) Rotate -30[deg](f) Rotate 30[deg]verticallyverticallyhorizontally

finger0
finger1

7

34

Distance [mm]

5
4
3
2

33

32

31

30

29

28

1
0

current distancetarget distance35

6Force [N]When the motion of the grasped object is constrained insome way to the environment or some large external force isapplied to the object, it is desirable to comply with thisexcessive force. In our system this is achieved in thefollowing way. When the total external force is larger thancertain threshold, the fingertip positions are translated in the
direction of external force so that the external force becomes
smaller. In a similar way, when the total external moment islarger than certain threshold, the fingertip positions arerotated in the direction of total external moment.

36

8

27

26
0

2

4

6

8
10 12
Time [sec]14(a) Grasping force

16

18

0

5

10
Time [sec]1520(b) Distance between fingers

Figure 8. Grasping Force and Finger Distance for Target Force 3[N]grasping force is tracking well the desired value by adjusting
the fingertip distance. The servo control period was 1[ms].
V.

FUTURE DIRECTIONS AND ISSUES

Several future research directions and issues are listed
below.
Human Size Robot Hand with Enough Functionality: An
ultimate goal of robot hand research is to develop hands forhumanoid robots. More powerful and compact actuators areneeded to develop them. Tactile and force sensors should be
densely integrated into the design.
Hand Covered by Soft Skin: Robotic hands totally
covered by soft skin have large potential for realizing firm
grasping and stable manipulation.
Planning and Skill of Grasping and Manipulation: Oneof the most difficult problems is to make a detailed motion
plan for a given grasp and manipulation task. For a skillful
manipulation, feedback of various information on the object
and environment will be needed. Only preliminary studies
have been done so far. Coordination of hand and other partof the body will also be needed.
Selection of Best Grasp Potion: In handling various
unknown objects, it is desirable to be able to select a good
grasp position that is suitable not only for resisting external
forces but also for performing a given manipulation task. An
optimality criterion taking into consideration the factors ofworkspace, manipulating ability, and resisting ability against
predicted external force will be necessary. This problem has
not been solved yet satisfactorily.
Versatile vs. Customized Hands: Multifingered hands
discussed in this article are mostly those aiming at versatile
hands. However, if we consider just a particular task or aclass of tasks, customized hands will work more efficiently.
Both approaches will be needed depending on the situation.
REFERENCES(g) Rotate -30[deg](h) Rotate 30[deg](i) Rotate -30[deg]horizontally

diagonallydiagonally

Figure 7. Manipulation of Cube

[1]

T. Okada, “Object-Handling System for Manual Industry,” IEEE
Transactions on Systems, Man, and Cybernetics, vol. SMC-9, no. 2,
1979.

[2]

M. T. Mason and J. K. Salisbury, Jr., Robot Hands and the Mechanicsof Manipulation, MIT Press, Cambridge, MA, 1986.
[3] S. C. Jacobsen, E.K. Iversen, D. F. Knutti, R. T. Johnson, and K. B.
Biggers, “Design of the Utah/M.I.T. Dexterous Hand,” Proc. of IEEE
Int. Conference on Robotics and Automation, pp.1520-1532, 1986.
[4] G. Hirzinger et al., “A Mechatronics Approach to the design of lightweight arms and multifingered hands,” Proc. of IEEEInt.
Conference on Robotics and Automation, pp.46-54, 2000.
[5] C. S. Lovchik, M. A. Diftler, “The Robonaut Hand: A Dexterous
Robot Hand for Space,” Proc. of IEEE Int. Conference on Robotics
and Automation, pp.907-912, 1999.
[6] H. Kawasaki, T. Komatsu, and K. Uchiyama, “Dexterous
Anthropomorphic Robot Hand with Distributed Tactile Sensor: Gifu
Hand II,” IEEE/ASME Transactions on Mechatronics, vol.7, no.3, pp.
296-303, 2002.
[7] A. Namiki, Y. Imai, M. Ishikawa, and M. Kaneko, “Development of aHigh-Speed Multifingered Hand System and Its Application to
Catching,” IEEE/RSJ Int. Conf. on Intelligent Robots and Systems,
pp.2666-2671, 2003.
[8] K. Hoshino and I. Kawabuchi, “Pinching at Finger tips for Humanoid
Robot Hand,” Journal of Robotics and Mechatronics, vol. 17, no. 6,
pp.655-663, 2005.
[9] http://www.shadowrobot.com/[10] T. Takaki and T. Omata, “High Performance Anthropomorphic Robot
Hand with Grasp Force Magnification Mechanism,” Proc. of IEEE Int.
Conf. on Robotics and Automation, pp.1697-1703, 2009.
[11] K. Kaneko, K. Harada, and F. Kanehiro, “Development of Multifingered Hand for Life-size Humanoid Robots,” Proc. of IEEE Int.
Conf. on Robotics and Automation, pp. 913-920, 2007.
[12] H. Iwata and S. Sugano, “Design of Human Symbiotic Robot
TWENDY-ONE,” Proc. of IEEE Int. Conf. on Robotics andAutomation, 2009.
[13] R. M. Murray, Z. Li, and S. S. Sastry, A Mathematical Introduction to
Robotic Manipulation, CRC Press, 1994.
[14] M. T. Mason and J. K. Salisbury, Jr., Robot Hands and the Mechanicsof Manipulation, MIT Press, Cambridge, Mass., 1985.
[15] T. Yoshikawa, “Passive and Active Closures by Constraining
Mechanisms,” Transactions of the ASME Journal of Dynamic
Systems, Measurement, and Control, vol. 121, pp. 418-424, 1999.
[16] T. Yoshikawa, Foundations of Robotics, MIT Press, Cambridge, MA,
1990.
[17] K. B. Shimoga, “Robot Grasp Synthesis Algorithms: A Survey,”
International Journal of Robotics Research, vol. 15, no. 3, pp. 230266, 1996.
[18] M. Strandberg and B. Wahlberg, “A Method for Grasp Evaluation
Based on Disturbance Force Rejection,” IEEE Transactions on
Robotics, pp. 461-469, 2006.
[19] D. G. Kirkpatrick, B. Mishra, and C. K. Yap, “Quantitative Steinitz's
Theorems with Application to Multifingered Grasping,” Proc. 20th
ACM Symp.on Theory of Computing, pp.341-351, 1990.
[20] C. Ferrari and J. Canny, “Planning Optimal Grasps,” Proc. of IEEE
Int. Conf. on Robotics and Automation, pp. 2290-2295, 1992.
[21] A. T. Miller and P. K. Allen, “GraspIt!: A Versatile Simulator for
Grasp Analysis,” Proc. of ASME International MechanicalEngineering Congress & Exposition, pp. 1251-1258, November 2000.
[22] Z. Li and S. Sastry, “Task-oriented optimal grasping by multifingered
robot hands,” IEEE J. Robot. Autom., vol. 4, no. 1, pp. 32-44, 1988.
[23] N. S. Pollard, “Parallel methods for synthesizing whole-hand grasps
from generalized prototypes,” Ph.D. dissertation, Dept. Elect. Eng.
Comput. Sci., MIT, Cambridge, MA, 1994.

[24] T. Watanabe and T. Yoshikawa, “Grasping Optimization Using a
Required External Force Set,” IEEE Trans. on Automation Scienceand Engineering, vol. 4, no. 1, pp. 52-66, 2007.
[25] Y. C. Park and G. P. Starr, “Grasp Synthesis of Polygonal Objects
Using a Three-Fingered Robot Hand,” Int. J. Robotics Res., vol. 11,
no. 3, pp. 163--184, 1992.
[26] B. Mirtich and J. Canny, “Easily computable optimum grasps in 2-D
and 3-D,” Proc. of IEEE Int. Conf. on Robotics and Automation,
pp.739-747, 1994.
[27] P. Akella and M. Cutkosky, “Manipulating with Soft Fingers,” Proc.
of IEEE Int. Conf. on Robotics and Automation, pp. 764-769, 1989.
[28] S. Arimoto, P. A. Nguyen, H.-Y. Han, and Z. Doulgeri, “Dynamics
and control of a set of dual fingers with soft tips,” Robotica, vol. 18,
pp.71-80, 2000.
[29] Z. Doulgeri and J. Fasoulas, “Grasping Control of Rolling
Manipulation with Deformable Fingertips,” IEEE/ASME Trans. on
Mechatronics}, vol. 8, no. 2, pp. 283-286, 2003.
[30] M. Yoshida, S. Arimoto, and Z. W. Luo, “Three- Dimensional Object
Manipulation by Two Robot Fingers with Soft Tips and Minimum
D.O.F,” Proc. of IEEE Int. Conf. on Robotics and Automation, pp.
1615-1616, 2008.
[31] N. Xydas and I. Kao, “Modeling of contact mechanics and friction
limit surfaces for soft fingers in robotics, with experimental results,”
International Journal of Robotics Research, vol. 18, no. 8, pp. 941950, 1999.
[32] T. Inoue and S. Hirai, “Study on hemispherical soft-fingered handlingfor fine manipulation by minimum d.o.f. robotic hand,” Proc. of IEEE
Int. Conf. on Robotics and Automation, pp. 2454-2459, 2006.
[33] T. Yoshikawa, M. Koeda and H. Fujimoto, “Shape Recognition and
Grasping by Robotic Hands with Soft Fingers and Omnidirectional
Camera,” Proc. of IEEE Int. Conf. on Robotics and Automation,
paper WeA8.6, 2008.
[34] A. Laurentini, “The Visual Hull Concept for Silhouette-Based Image
Understanding,” IEEE Trans. on Pattern Analysis and MachineIntelligence, vol. 16, no. 2, pp. 150-162, 1994.
[35] S. Sugiyama, M. Koeda, H. Fujimoto and T. Yoshikawa,“Measurement of Grasping Position by Human Hands and Grasp
Criterion for Two Soft-fingered Robot Hands,” Proc. of IEEE Int.
Conf. on Robotics and Automation, paper FrC5.2, 2009.
[36] Z. Li, P. Hsu, and S. Sastry, “Grasping and Coordinated Manipulation
by a Multifingered Robot Hand,” Int. J. Robotics Res., vol. 8, no. 4,
pp. 33-50, 1989.
[37] Y. Nakamura, K. Nagai, and T. Yoshikawa, “Dynamics and Stability
in Coordination of Multiple Robotic Mechanisms,” Int. J. Robotics
Res., vol. 8, no. 1, pp. 44-61, 1989.
[38] T. Yoshikawa and X.-Z. Zheng, “Coordinated Dynamic Hybrid
Position/Force Control for Multiple Robot Manipulators Handling
One Common Object,” Int. J. Robotics Res., vol. 12, no. 3, pp. 219230, 1993.
[39] T. Yoshikawa, “Virtual Truss Model for Characterization of Internal
Forces for Multiple Finger Grasps,” IEEE Transaction on Robotics
and Automation, vol. 15, no. 5, pp. 941-947, 1999.
[40] T. Yoshikawa, “Control Algorithm for Grasping and Manipulation by
Multifingered Robot Hands Using Virtual Truss Model
Representation of Internal Force,” Proc. of IEEE Int. Conf. on
Robotics and Automation, pp. 369-376, 2000.
[41] H. Liu and G. Hirzinger, “Cartesian Impedance Control for the DLR
Hand,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots andSystems, pp. 106-112, 1999.
[42] T. Yoshikawa, M. Koeda, and H. Fukuchi, “Grasping andManipulation by Soft-Fingered Robot Hands,” Proc. of IEEE Int.
Conf. on Advanced Robotics, paper F3D.3, 2007.