December 17, 2009 11:56 WSPC/191-IJHR

00189

International Journal of Humanoid Robotics
Vol. 6, No. 4 (2009) 699–725
c World Scientiﬁc Publishing Company


DESIGN, CONTROL AND EVALUATION OF A
WHOLE-SENSITIVE ROBOT ARM FOR PHYSICALHUMAN-ROBOT INTERACTIONDZMITRY TSETSERUKOU∗ and NAOKI KAWAKAMI†
Department of Information Physics and Computing,
the University of Tokyo, 7-3-1 Hongo,
Bunkyo-ku, Tokyo, 113-8656, Japan∗dima teterukov@ipc.i.u-tokyo.ac.jp†Naoki Kawakami@ipc.i.u-tokyo.ac.jp
SUSUMU TACHI
Graduate School of Media Design, Keio University,
4-1-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8526, Japantachi@tachilab.org
Received 9 June 2008
Accepted 4 July 2009
The paper focuses on design and control of a new anthropomorphic robot arm enabling
the torque measurement in each joint to ensure safety while performing tasks of physicalinteraction with human and environment. When the contact of the robot arm with anobject occurs, local admittance algorithm provides active compliance of corresponding
robot arm joint. Thus, the whole structure of the manipulator can safely interact withan unstructured environment. The detailed design procedure of the 4-DOF robot armand optical torque sensors is described in the paper. The experimental results of joint
admittance control revealed the feasibility of the proposed approach to provide safeinteraction of entire structure of robot arm with a person. The control system with load
angle position feedback and lead compensator is proposed to improve dynamic behaviorof ﬂexible joint arm. The experimental results show high performance of the developed
controller in terms of successful damping of vibrations.
Keywords: Human-robot physical interaction; robot arm; optical torque sensor;
admittance control.

1. Introduction
In near future, robots will be introduced in human daily life environment to assist
with hospital care, surgery operations, high-risk maintenance tasks, construction
works, and oﬃce aﬀairs. The desired coexistence of robotic systems and humans inthe same physical domain (sharing the same workspace and actually cooperating ina physical manner) poses very fundamental problem of ensuring safety to the user
and robot. Even without wrong programming, a robot, moving freely in a human699

December 17, 2009 11:56 WSPC/191-IJHR

700

00189

D. Tsetserukou, N. Kawakami & S. Tachi

environment, is potentially dangerous because of its large moving masses, powerful actuators and unpredictably complex behavior. A robot falling on someone orjust stepping on his foot by mistake might cause much more damage to the victim
than a human being of the same size. Thus, following the First Law of robotics
introduced by I. Asimov1 in 1942: “A robot may not injure a human being, or,through inaction, allow a human being to come to harm” — is the key aspect forsuccessful integration of robots into human environment. Design and programmingof the robots exhibiting intrinsically safe behavior in a human domain are greatchallenges in robotics because such robots have to deal with unstructured timevarying environment. Several humanoid robots aimed at integration into people
environment were developed. The Honda’s ASIMO2 and HRP 23 show the best
performance among humanoid robots while cooperating with human being. However, despite the splendid means for sensing the environment (visual, audio, anda sense of touch), the 6 axis force/torque sensors attached at the tip of the robot
arm and a stereo vision system which is slow to track the changing environment
in real-time, are only the abilities to anticipate the collision. The rest parts of therobot body (forearm, elbow, upper arm, shoulder, and torso) are presenting thesigniﬁcant danger not only for human being, but also for the robot structure itself.
Several eﬀective methods for enhancement of contact detection ability of manipulator were reported. To avoid collisions in time-varying environment, Lumelsky
et al.4 proposed to cover a manipulator with a sensitive skin capable of detecting
nearby objects. Mitsunaga et al.5 progressively improved the tactile ability of therobot through covering its entire body with piezoelectric ﬁlm-based tactile sensors.
Since this device integrates a huge amount of small sensors incorporated into soft
layer, and requires complicated wiring and signal processing hardware, it has highcost and reliability issues. Finding the technical solution for trade-oﬀ between performance and safety is the target of a new manipulation technology. To cope withthis issue, an active compliance control implying the fast joint torque controllingbased on measuring the applied external torque in each joint was developed. The
ﬁrst embodiment of torque measurement is the integration of a torque sensor into
each joint of the manipulator. The impedance control generates compliant trajectory based on information on the measured external torque. Such approach has two
main advantages: (1) sensor detects not only forces applied to the robot hand but
also those exerted at other points on the manipulator, (2) and allows to increase
the performance of fast movements by active vibration damping. Several attempts
have been made by researchers to improve the joint torque control. Wu and Paul6
proposed a simple, wide bandwidth, torque servo system using strain-gauge-based
joint torque sensor. The torque-controlled lightweight robot arm with high load toweight ratio was developed by Hirzinger et al.7 Each joint of the arm is facilitatedwith strain-gauge-based torque sensor, position sensor and piezzo-brake. Sakaki andIwakane8 proposed to use a compact motor drive with embedded magnetostrictive
torque sensor for impedance control of the robot arm. The approach of torque measurement through the elasticity of the harmonic drive ﬂexsplines allows keeping theDecember 17, 2009 11:56 WSPC/191-IJHR

00189

Whole-Sensitive Robot Arm for Physical Human-Robot Interaction

701

same stiﬀness and mechanical structure of the robot.9,10 This method requires thestrain gauges to be installed on the ﬂexsplines. The crucial shortcomings of thetorque measurement approaches mentioned above will be discussed in Sec. 3.
The alternative method for increasing the safety level of robot arms interacting with humans is the intentionally introducing the compliance at the mechanicaldesign level. The main idea here is the decoupling the rotor inertia from the linkinertia through use of passive elasticity.11 However, the robot control is complicatedby many unknown parameters (e.g., actuator stiﬀness and damping). Furthermore,
compliant transmission negatively aﬀects the performance in term of increased oscillations and settling time.
Our main research is devoted to realization of teleoperated robot enabling safeinteraction with humans. The line of master-slave robot systems (TELESAR I and
TELESAR II) was successfully developed in our laboratory.12,13 The performanceand stability of teleoperation were gradually improved. The slave robot enables toperform dexterous operations by means of 7-DOF arms and 8-DOF hands. The
safety in interaction at the tip of the end-eﬀector is provided by impedance control
algorithm.
To realize the safe physical contact of entire robot arm structure with human,
our primary idea is concentrated on the design of a whole-sensitive robot arm (byusing distributed torque sensors in each joint). When contact with environment
occurs, manipulator automatically generates compliant motion according to themeasured external torque and time derivative of torque. The developed anthropomorphic manipulator iSoRA (intelligent Soft Robot Arm) having 4-DOF arm and
8-DOF hand (Fig. 1) is capable of safe physical interaction with environment wherever contact occurs on the arm surface. The distinctive features of our approach are
as follows:(1) We use an optical approach to the torque measurement, which allows avoiding
inherent shortcomings of strain-gauge-based and magnetostrictive force/torquesensors. The developed new optical torque sensors have high dependability, good
accuracy (even in electrically noisy environment), low price, compact sizes, andeasy manufacturing and calibration procedure.14(2) The stiﬀness of the sensor developed by us is much smaller than in previously
proposed sensors. So, when the collision takes place, the injuries of human canbe considerably reduced through compliant coupling between the motor rotor
inertia and the link inertia.
(3) The admittance control was employed to realize safe and smooth human-robot
interaction and to provide the eﬀectiveness of the contact task performance.
The teleoperated robot arm is controlled by the operator’s motion (Fig. 2). To
achieve human-level dexterity of manipulation, the master arm was endowed with 6
DOF (Fig. 3). The motion of the operator’s elbow is controlled by the accelerometerbased position sensor. In order to make the level of motion constraint lower, theDecember 17, 2009 11:56 WSPC/191-IJHR

702

00189

D. Tsetserukou, N. Kawakami & S. Tachi

Position
Orientation
Velocity

manipulator was manufactured using components with small inertia and embedded
gravity compensation mechanism. During operation of manipulator the movement
information (position, orientation and velocity) is acquired through motor encoders
installed into each joint of the master arm.
The remainder of the paper is structured as follows. Section 2 describes thedevelopment of a whole-sensitive robot arm. Newly developed optical torque sensors are presented in Sec. 3. The admittance control algorithm allowing compliant
interaction with environment, eﬀect of joint ﬂexibility on robot arm dynamics, andthe experimental results are discussed in Sec. 4. In Sec. 5, we conclude the paper.
2. Design of a New Anthropomorphic Robot Arm
From the safety point of view, to minimize injures in the case of collision, most of
the robot arm parts were manufactured from aluminium alloys to obtain as muchlightweight structure as possible. The robot links were designed in round shape toreduce impact force. The distribution of the arm joints replicates the human arm
structure in order to make it easier to operate using kinesthetic sensation during
teleoperation.
To remove mechanical subsystems without disassembling the main structure
when the failures do occur, we use a modular approach while designing the anthropomorphic robot arm. Therefore, we selected CSF-series gear head type of harmonic
drive instead of compact and lightweight component one. The harmonic drive oﬀers
such advantages as accurate positioning, high torque capability, torsional stiﬀness,

December 17, 2009 11:56 WSPC/191-IJHR

00189

Whole-Sensitive Robot Arm for Physical Human-Robot Interaction

Yaw

Torque sensor
distribution in
Shoulder Pitch
and Roll Joints

Z1
Y2

J2

Y1

+

703

Z0

Y0
+
X0 +
Pitch
Roll
J1

X1

X2

Z2

L1
J3

Y4
Sensors
in Yaw
and
Elbow
Joints

Y3

Z4
X4

J4

X3
Z3
L2

Y5
Z5
X5

Fig. 4. 3D CAD arm model and coordinate systems.

and high single stage ratios. Developed robot arm iSoRA has 4-DOF: roll, pitch,yaw joints of a shoulder, and a pitch joint of an elbow. Such orthogonal dispositionof the axes simpliﬁes the installation of the torque sensors and motor drives into
the joints, allowing thus avoidance of application of additional belt driven actuators. The 3D CAD model of the developed arm and coordinate systems based on
Denavit-Hartenberg convention are represented in Fig. 4.
Each robot joint is equipped with optical torque sensor directly connected to the
output shaft of harmonic drive. The sizes and appearance of the arm were chosen
so that any sense of incongruity during interaction with a human is avoided. We
kept the arm proportions the same as in average height Japanese male, aged 25:
upper arm length L1 0.308 m; upper arm circumference 0.251 m (diameter 0.080 m);
forearm length L2 0.241 m; forearm circumference 0.189 m (diameter 0.06 m). The
motors are equipped with magnetic encoders having 512 pulses per revolution. To
protect the sensor against inﬂuence of bending moment and axial force, the simple
supported loaded shaft conﬁguration was implemented using two sets of bearings.
The kinematic parameter values are listed in Table 1.
To calculate the motor power, we considered cyclical movement of drive in accordance with the torque-speed diagram. The movement cycle time of 1.2 sec withangular speed of π/2 rad/sec was assumed. The acceleration period of 25% of theDecember 17, 2009 11:56 WSPC/191-IJHR

704

00189

D. Tsetserukou, N. Kawakami & S. Tachi
Table 1. Denavit-Hartenberg parameters of the arm.
iαi−1 [deg]ai−1 [m]di [m]

θi [deg]

1
2
3
4
5

90
−90
90
−90
0

0
0
0
0
L2

0
0
L1
0
0

θ1 − 90θ2 + 90
θ3 + 90
θ4 − 900Table 2. Principal speciﬁcations.
Parameters

Arm joints
ShoulderElbow

J1, Pitch

J2, Roll

J3, Yaw

J4, Pitch

Mobility range(New robot arm/
Human arm) [deg]−180 to 180(−60 to 180)

−180 to 10(−165 to 0)−180 to 180(−60 to 180)

0 to 112(0 to 130)

Motor power[W],motor type

90.0,
Maxon RE 35

60.0,
Maxon RE 35

26.6,
Faulhaber
2657

26.6,
Faulhaber
2657

Harmonic drive
rated torque [Nm],(type/gear ratio)

7.8,(CSF-14-GH
/100)

7.8,
(CSF-14-GH
/100)

5.0,
(CSF-112XH/100)

5.0,(CSF-112XH/100)

positioning time and standstill time of 0.2 sec were deﬁned. The root mean squared
values of required torque of the entire work cycle were calculated for the motors ofeach joint using preliminary information of moment of inertia of the motor rotor,
moment of inertia of the load, angular acceleration, the eﬃciency of harmonic drives,
the load torques, and friction torques. To provide high output torque of drive andsmall sizes of the motor side, the harmonic drive with high gear ratio of 100 was
adopted.
The principal speciﬁcations of the developed arm are given in Table 2.

3. Development of a New Optical Torque Sensor
Strain gauges are widely used to measure the exerted force value providing suchbeneﬁts as high linearity, about 0.03%–2.5% of full scale (FS), and high resolution. However, their maximum allowable strain is close to their breaking point.
To guarantee overload protection of transducers, mechanical stops with very small
clearances limiting deﬂections of ﬂexures are necessary. Moreover, such sensors haveconsiderable electrical noise level, fragility, complicated manufacturing procedure,etc. Furthermore, in case of noisy environment (e.g., in close vicinity of powerful
DC motor creating high level of electromagnetic noise), the additional low-pass ﬁlter
reducing bandwidth of the signal to as small as 10–20 Hz is required to get readable
data.

December 17, 2009 11:56 WSPC/191-IJHR

00189

Whole-Sensitive Robot Arm for Physical Human-Robot Interaction

705

In order to facilitate the realization of local impedance control in each robot arm
joint, we developed new optical torque sensors having high reliability, high accuracy(even in electrically noisy environment), easy mounting procedure, and low price.
In our previous work, we described the techniques of the torque measurement,
their advantages and shortcomings, and pointed out the motivations behind using
an optical approach.14 Hirose and Yoneda15 proposed to use a split type photodiodeto detect the 2D displacement of light source (LED) caused by the applied force.
In cooperation with Minebea Co., OPFT series of 6-axes optical force sensors were
manufactured.16,17 In comparison with conventional transducers, they have electrical noise immunity and low cost. However, these sensors suﬀer from complicatedcalibration procedure, and have an average accuracy of 5% FS. The list of desirable properties of torque sensor aimed at anthropomorphic robot arm integration
is given as follows:(1) Addition of the torque sensor to a robot joint should cause a minimal modiﬁcation in kinematics and dynamics. Therefore, lightweight sensor with small
width is preferable.
(2) High signal-to-noise ratio ensuring a high resolution of the torque measurement
is desirable.
(3) Torsional stiﬀness of the sensor should not considerably reduce the natural
frequency of the robot arm.
(4) Mechanical structure should be machined from a single piece of metal in order
to eliminate hysteresis.
(5) Inﬂuence from the non-torsional components of the load should be avoided.
(6) Simple to manufacture, low in cost, and robust.
The novelty of our method is the application of the ultra-small-size photointerrupter(PI) RPI-121 as a sensitive element to measure the relative motion of sensor components. The linear section of the transferring characteristic corresponding approximately to 0.2 mm can be used for detection of the relative displacement of theobject.18 The dimensions of the PI (RPI-121: 3.6 mm × 2.6 mm × 3.3 mm) and its
weight of 0.05 g allow realization of compact design. Furthermore, PI also has small
inﬂuence from the electromagnetic ﬁeld and stray light. Separation of the sensing
element from the deﬂected beams results in high robustness of the sensor. We developed several sensor prototypes with cross-shaped, hub-spoke-shaped, ring-shaped,
and semi-circular-shaped ﬂexure on the basis of FEM analysis results in order toﬁnd out the optimal solution. Because of suﬃcient stiﬀness, high natural frequency
and small inﬂuence of bending moment and axial force on the sensor accuracy, thespoke-shaped spring as deﬂecting part of the optical torque sensor was chosen. The
optical torque sensor is set between the driving shaft of the harmonic transmission
and driven shaft of the joint (Fig. 5).
When the load is applied to the arm, the spring in corresponding joints is
deﬂected. This causes the adapter plate rotation with small angle of twist, θ. The
shield displacement is detected by the degree of interruption of infrared light that

December 17, 2009 11:56 WSPC/191-IJHR

706

00189

D. Tsetserukou, N. Kawakami & S. Tachi

Output shaft ofharmonic drive
Adjusting slider
PI

2

Driven shaft
adapter plateT

Springθ1

Fig. 5. 3D model of the optical torque sensor aimed at robot arm application.

falls on the phototransistor. Thus, the magnitude of the output signal from PI corresponds to the exerted load. The relationship between the sensed torque, T , andthe angle of twist is the following:
T = kθ = k(θ1 − θ2 ),(1)

where k is the torsional stiﬀness of the spring member.
Since the angle of twist is small, it can be calculated from the displacement of
the shield in the tangential direction, ∆x:
T = k∆x/R,(2)

where R is the distance from the sensor center to the middle axis of the shield platein radial direction.
The drawing of the hub-spoke-shaped spring is presented in Fig. 6.
The detector consists of an inner part 1 connected by ﬂexure 3 with an outerpart 2, a ﬁxed PI 5, and a shield plate 4. A protrusion 6 is intended for axialalignment of a spring with an output shaft of harmonic drive. A part 7 is provided
for increasing the transferring torque rate and diminishing hysteresis. The zero
position of a shield is adjusted by rotation of screw while sensor assembling. The
torsional stiﬀness19 of the sensor is derived from:
1 3r 3r2+ 2 + 3 ,(3)
k = 4NEIlll

December 17, 2009 11:56 WSPC/191-IJHR

00189

Whole-Sensitive Robot Arm for Physical Human-Robot Interaction

5

707

b

4

2
Y

l

R
6
7

r
Z

X

D

t

1

T

3

Fig. 6. Hub-spoke structure layout.

where N is the number of spokes; E is the modulus of elasticity, I is the momentof inertia of the spoke cross section, l is the spoke length, and r is inner radius of
the sensor.
The value of I can be calculated as follows:
I = bt3 /12,

(4)

where b is the beam width, t is the beam thickness (Fig. 6).
The sensing range of sensors was calculated using gravity force value and contact
force of ±25 N at the tip of the robot arm. The sensors attached to the ﬁrst, second,
and third/fourth joints were designed to measure torque of ±12.5 Nm, ±10.5 Nm,
and ±4.5 Nm, respectively. Such materials as high yield-strength stainless steel 174PH, 13-8 VAR as well as AISI 4135 and 4130 steel can be used for manufacturing the spring member. We decided to use AISI 4135 steel with tensile yieldstrength σyield of 785 MPa. The results of analysis of third/fourth joint sensor using
FEM demonstrate von Mises stress σVonMises(Tz ) in MPa under a torque TZ of
4.5 Nm (Fig. 7(a)), tangential displacement in mm (Fig. 7(b)), stress under a bending moment TX of 4.5 Nm (Fig. 7(c)), and stress under an axial force FZ of 70 N
(Fig. 7(d)).
The results of analysis show that spring also has suﬃcient strength to support
bending and axial load. The torsional stiﬀness is calculated from Eq. (2) using tangential displacement (Fig. 7(a)). The spring components were manufactured fromone piece of AISI 4135 steel using a wire electrical discharge machining (EDM).
Components and assembly of the torque sensor incorporated in second joint arepresented in Fig. 8.
The test rig for calibration of the optical sensor was developed (Fig. 9(a)).
Loading torque was created by means of attachment of reference weights to the leverarm. Calibration results for torque sensor attached to elbow joint are presented inFig. 9(b).

December 17, 2009 11:56 WSPC/191-IJHR

708

00189

D. Tsetserukou, N. Kawakami & S. Tachi(a) von Mises stress (torque)(b) Tangential displacement(c) von Mises stress (bending moment)(d) von Mises stress (axial force)

Fig. 7. Results of FEM analysis of sensor in the third/fourth joints.

Fig. 8. Torque sensor of shoulder pitch joint.

December 17, 2009 11:56 WSPC/191-IJHR

00189

Whole-Sensitive Robot Arm for Physical Human-Robot Interaction

709

4.0

Output voltage U, (V)Torque sensor

Lever arm

Calibration data
Best fit line

3.5
3.0
2.5
2.0
1.5

Weight
1.0
-4

-2

0

2

4

Torque T, (Nm)(a) Calibration setup(b) Output vs. loading torqueFig. 9. Calibration setup and calibration results.

In order to determine the sensitivity and non-linearity of the optical torquesensors, we used the best-ﬁt line approach. Calculated sensitivity of the transducer
attached to the fourth joint equals 0.317 V/Nm. Non-linearity of 2.5% of Full Scale
was calculated using maximum deviated value from the best-ﬁt line. Torque sensor
resolution was determined based on the least signiﬁcant bit value and calibration
diagram. For the torque sensors attached to the ﬁrst, second, and third/fourth joints,
resolution equals 10.77 mNm, 9.02 mNm, and 4.31 mNm, respectively. The average
signal-to-noise ratio for the developed sensors in the presence of electromagnetic
noise generated by DC motors has as high value as 52 dB. The cost of the sensor isin times cheaper than of commercially available one. In order to smooth the signal
acquired from the sensors, we implemented the digital low-pass ﬁltering techniquewith the cut-oﬀ frequency of 70 Hz. Speciﬁcations of the developed optical torquesensors and analogous 6-axis T/F sensors are listed in Table 3.
The main advantages of ATI sensors20 are high torsional stiﬀness and large factorof safety. The 6-axis force/torque sensors of the Minebea Co. have small electricalnoise level. However, the weight and sizes make diﬃculties for their application as
joint torque detector in robot. Such features of newly developed optical torque sensors as high dependability, good accuracy (even in electrically noisy environment),
low price, compact sizes, lightweight, easy manufacturing procedure make the developed sensors better solution comparing with analogs for the torque measurement
in robot arm joints.
In addition to the contact force, torque sensor continuously measures the gravity
load. To extract the value of the contact force from the sensor signal, we elaborated
the gravity compensation algorithm based on Newton-Euler dynamics formulation.
The plot of measured (while rotating) and calculated values of torque acting on the
fourth joint is shown in Fig. 10. The plot of error of the torque measurement ispresented in Fig. 11.

First
joint
12.5
3,230
1.76
45.0
7.0
70.15

SensorSensing range [±Nm]
Torsional stiﬀness [Nm/rad]Factor of safety
Outer diameter [mm]Thickness [mm]Sensor mass [g]

10.5
2,730
1.7
42.0
6.0
50.81

Second
joint
4.5
950
1.82
37.0
5.0
34.95

Third/
Fourth joint
10.0
35,000
5.4
45.0
15.7
92.0

ATI Mini
45SI-290-10
4.0
4,300
4.3
40.0
12.25
50.0

ATI Mini
40SI-80-4
5.0
—
1.5
50.0
31.5
330.0

Minebea
OPFT-100N

11.0
—
1.5
50.0
31.5
330.0

Minebea
OPFT-220N

710

Table 3. Technical speciﬁcation.

December 17, 2009 11:56 WSPC/191-IJHR
00189

D. Tsetserukou, N. Kawakami & S. Tachi

December 17, 2009 11:56 WSPC/191-IJHR

00189

Torque in elbow joint τ EXT4 , (Nm)

Whole-Sensitive Robot Arm for Physical Human-Robot Interaction

711

0.40
0.35
0.30
0.25
0.20
0.15
0.10
Measured torque
Reference torque

0.05
0.00
0

20

40

60

80

100

120

140

160

180

160

180

Joint angle θ4 , deg

Error of measurement ε, Nm

Fig. 10. Measured and referenced torques.

0.015
0.010
0.005
0.000
-0.005-0.010-0.015
0

20

40

60

80

100

120

140

Joint angle θ4 , (deg)Fig. 11. Error of torque measurement.

Observing the measurement error plot, we can assign the relevant threshold of
0.015 Nm that triggers control of the constraint motion.
4. Joint Admittance Control and Joint Flexibility Consideration
4.1. Joint admittance control
People can perform dexterous contact tasks in daily activities, regulating their own
dynamics according to a time-varying environment. The most eﬃcient method ofcontrolling the interaction between a manipulator and an environment is impedancecontrol.21 This approach enables us to regulate response properties of the robot toexternal forces through modifying the mechanical impedance parameters. In manystudies, the adaptive model-based impedance control is adopted.22,23 However, dueDecember 17, 2009 11:56 WSPC/191-IJHR

712

00189

D. Tsetserukou, N. Kawakami & S. Tachi

to parametric uncertainties of the robot dynamics model, it is diﬃcult to obtain thecomplete description of the dynamics. Therefore, model-based adaptive impedancecontrol must rely on either repeated motions or time for adaptation to achieve
convergence to the desired system parameters. For systems with more than 1 DOF,
such an approach can hardly be applied. To ensure the eﬀectiveness of service
task accomplishment, we decided to implement position-based impedance control(admittance control). In this algorithm, the compliant trajectory generated by the
impedance controller is tracked by the PD control loop. The desired impedance
properties of ith joint of manipulator can be expressed as:
Jdi ∆θ̈i + Ddi ∆θ̇i + Kdi ∆θi = τEXT i ;∆θi = θci − θdi ,(5)

where Jdi , Ddi , Kdi are the desired inertia, damping, and stiﬀness of the ith joint,respectively; τEXTi is the torque applied to ith joint and caused by external forces,∆θi is the diﬀerence between the current position θci and desired position θdi . The
state-space presentation of the equation of local impedance control is written as:   

0
1
θi
0
∆θ̇i
(6)
=
+
τEXTi (t),vi
−Kd /Jd −Dd /Jd
1/Jd
v̇i
or:




 
∆θ̇i
θ
= A i + BτEXTi (t),
v̇i
vi

(7)

where the state variable is deﬁned as vi = ∆θ̇i ; A, B are matrices. After integrationof Eq. (7), the discrete time presentation of the impedance equation is expressed
as:




∆θk
∆θk+1
(8)
= Ad
+ Bd TEXT (k ) .
∆θ̇k∆θ̇k+1
To achieve the fast non-oscillatory response on the external force, we assigned
the eigenvalues λ1 and λ2 of matrix A as real and unequal λ1 = λ2 . By using
Cayley-Hamilton method for matrix exponential determination, we have: λT
1eλ1 T − eλ2 T
e 2 λ1 − eλ1 T λ2
AT=
Ad = e(9)eλ2 T (λ1 + a) − eλ1 T (λ2 + a)
λ1 − λ2 −b eλ1 T − eλ2 T
Bd = (Ad − I) A−1 B = − λ2 Tce λ1 −eλ1 T λ2 − (λ1 − λ2 ),−b eλ1 T − eλ2 Tb (λ1 − λ2 )(10)where T is the sampling time; coeﬃcients a, b, and c are equal to Dd /Md , Kd /Md ,and 1/Md, respectively; I is the identity matrix. The eigenvalues λ1 and λ2 can becalculated from:√√−a + a2 − 4b−a − a2 − 4bλ1 =; λ2 =.
(11)22

December 17, 2009 11:56 WSPC/191-IJHR

00189

Whole-Sensitive Robot Arm for Physical Human-Robot Interaction

713

Admittance control

τEXT

Impedance
trajectory
generator∆ θk+1

TZ
θc

θd

+-

θc

Kv

+-

θd

θ

1
Ki KT

Kp

θ

Optical torquesensorA/D∆ θk+1 ∆ θk+1

θdTargettrajectory
generator

GravityCompensation

- TG

D/A

Driverθ

Encoder

im

Link

θc

s

Signal from the Master arm
PD control
Fig. 12. Block diagram of admittance control.

Applied torque TEXT(k) , (Nm)

We adopted Kd = 29 (Nm/rad), Dd = 6.9 (Nm·s/rad), Jd = 0.4 (kg·m2 ),to achieve closed to critical damped response and suﬃcient for safe interaction
compliance.
To verify the theory and to evaluate the feasibility and performance of the
proposed admittance controller (Fig. 12), the experiments were conducted with
developed teleoperated robot arm.
During the ﬁrst experiment, a person pushed the robot forearm once. The experimental results for the elbow joint — applied torque, angle generated by admittancecontroller, measured joint angle, and torque versus joint angle — are presented in
Figs. 13–16, respectively.

0.6
0.5
0.4
0.3
0.2
0.1
0.0
0.0

0.5

1.0

Time t, (sec)Fig. 13. External torque.

1.5

2.0

December 17, 2009 11:56 WSPC/191-IJHR

Impedance trajectory ∆ θκ +1, (deg)D. Tsetserukou, N. Kawakami & S. Tachi

0.0

-0.3
-0.6
-0.9
-1.2
0.0

0.5

1.0

1.5

2.0

Time t, (sec)Fig. 14. Compliant trajectory.

Joint angle θ4 , (deg)

-6.0
-6.3
-6.6
-6.9
-7.2
0.0

0.5

1.0

1.5

2.0

Time t, (sec)Fig. 15. Measured joint angle.

Applied torque TEXT(k) , (Nm)

714

00189

0.6

0.4

0.2

0.0
-1.2

-0.9

-0.6

-0.3

Impedance trajectory ∆ θ k+1, (deg)Fig. 16. Joint angle trace error.

0.0

December 17, 2009 11:56 WSPC/191-IJHR

00189

Whole-Sensitive Robot Arm for Physical Human-Robot Interaction

715

Applied torque TEXT(k) , (Nm)

1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
0

4

8

12

16

Time t, (sec)Fig. 17. External torque.

During the second experiment, a person pushed the robot forearm several times
in opposite directions with forces having diﬀerent magnitude. The plots of theapplied torque, angle generated by admittance controller, measured joint angle, anderror of joint angle in the function of time are given in Figs. 17–20, respectively.
The experimental results show the successful realization of the joint admittancecontrol. While contacting with human, the robot arm generates compliant soft
motion according to the sensed force (Figs. 14 and 18). The larger force applied to
the robot arm, the more compliant trajectory is generated by impedance controller(Fig. 18). As we assigned closed to critically damped response of dynamic model todisturbance force, output angle (∆θk+1 ) has ascending-descending exponential trajectory. The dash line in Fig. 16 indicates the stiﬀness while steady-state behavior.
PD control provides small joint angle trace error in borders of −0.2◦ –0.25◦ (Fig. 20).
The conventionally force-controlled robot can provide contacting task only at the
tip of the end-eﬀector with predetermined dynamics. By contrast, our approachprovides delicate and safe physical interaction of all surface of the robot arm withthe environment.
In order to verify the ability of the developed robot arm to safely interact withhumans and to perform cooperative tasks through the human-following motion,
the experiments were conducted. The robot arm was controlled by the programon a DELL Precision computer (CPU: Intel Pentium, 3.2 GHz, memory 2 GB).
Figure 21 demonstrates the physical interaction of a human with the robot forearmin the cases of the human-following motion and the haptic communication (e.g.,handshaking). The robot arm generates compliant motion according to the applied
force value and the desired impedance properties of the manipulator. The example
of robot upper arm reaction to the force exerted by a human is presented in Fig. 22.
The human operator can easily adjust the position of the robot links while teaching
the robot a new motion trajectory (Fig. 23). The dynamic behavior of the robot arm

December 17, 2009 11:56 WSPC/191-IJHR

Impedance trajectory ∆θκ +1, (deg)D. Tsetserukou, N. Kawakami & S. Tachi

2.7
1.8
0.9
0.0
-0.9
-1.8
-2.7
0

4

8

12

16

12

16

12

16

Time t, (sec)Fig. 18. Compliant trajectory.

-3

Joint angle θ4 , (deg)

-4
-5
-6
-7
-8
-9
0

4

8

Time t, (sec)Fig. 19. Measured joint angle.

0.3

Joint angle error ε , (deg)

716

00189

0.2
0.1
0.0
-0.1
-0.2
-0.3
0

4

8

Time t, (sec)Fig. 20. Joint angle trace error.

December 17, 2009 11:56 WSPC/191-IJHR

00189

Whole-Sensitive Robot Arm for Physical Human-Robot Interaction

717

FEXT

EXT(1)

EXT(4)

t=0 sect=0.267 sec

t=0.733 sec

Fig. 21. Physical interaction of a human with the robot forearm.

FEXT

EXT(1)

t=0 sect=0.267 sec

t=0.667 sec

Fig. 22. Haptic interaction with the upper arm of robot.

FEXT

EXT(1)FEXT
EXT(4)

t=0 sect=0.300 sec

t=0.667 sec

Fig. 23. Human-following motion.

colliding with human hand is shown in Fig. 24. The robot arm generates continuous
compliant motion downward, thus smoothing the impact forces considerably.
It can be seen from Figs. 21–24 that the developed joint admittance controller
and whole-sensitive robot arm enable the human to control the position and orientation of the robot arm, and to realize the human-following motion through haptic

FEXT

EXT(1)

t=0 sec

EXT(4)

t=0.133 sec

Fig. 24. Collision of a human hand and the robot forearm.

t=0.633 sec

December 17, 2009 11:56 WSPC/191-IJHR

718

00189

D. Tsetserukou, N. Kawakami & S. TachiθL= 0i -1θm

Jm
i

JL

2

i Jm

JD

JL

k1

k2

i τm

Tb

k

θL

τd

Ta

B
A
(a)

A
(b)

Fig. 25. Presentation of joint with ﬂexibility.

interaction along the entire robot arm surface. The robot successfully prevents thelarge impact forces during the collision with a human, thus improving the safety ofhuman-robot coexistence greatly.
4.2. Joint flexibility consideration
Flexibility introduced into robot arm joint through harmonic drive and torque sensors changes the dynamic behavior of mechanical structure. Robot joint with compliance can be represented as three-inertia system (Fig. 25(a)) including: (1) theinertias of motor Jm , harmonic drive output shaft JD , and loadJL ; (2) the springelements of harmonic drive (with torsional stiﬀness k1 ) and torque sensor (withtorsional stiﬀness k2 ). Due to neglible value of inertia of output shaft of harmonic
drive, three-inertia model can be reduced to the equivalent two-DOF one with massinertias i2 Jm and JL (Fig. 25(b)).
The torsional stiﬀnesses k1 , k2 , and equivalent spring constant ke derived from
Eq. (12) are listed in Table 4.
1
1
k1 k2
1
=
+ , k = ke =
.
(12)
ke
k1
k2
k1 + k2
The dynamic equations of the n-DOF manipulator with joint compliance injoint space coordinates can be obtained by D’Alembert’s principle. The equationsof oscillations for mass A (Eq. (13)) and mass B (Eq. (14)) ignoring damping are
as follows:JL (θL )θ̈L = k i−1 θm − θL + τd ,
(13)




 J θ̈ = −i−1 k i−1 θ − θ  + τ ,
(14)
m m
m
L
m

(15)
τd = τEXT − G(θL ) − C(θL , θ̇L )θ̇L ,



 −1


τ = k i θm − θL ,(16)
where θL , θ̇L , θ̈L ∈ Rn are the vectors of the link angles, the link angular velocities,
and the link angle accelerations, respectively; θm , θ̈m ∈ Rn are the vectors of motor
angles, and the motor angle accelerations, respectively; k ∈ Rn×n is the diagonalDecember 17, 2009 11:56 WSPC/191-IJHR

00189

Whole-Sensitive Robot Arm for Physical Human-Robot Interaction

719

Table 4. The torsional stiﬀnesses of robot joints.
Arm joints
ShoulderElbow

Parameters

J1, Pitch

J2, Roll

J3, Yaw

J4, Pitch

Torsional stiﬀness ofharmonic drive k1[kNm/rad]Torsional stiﬀness of thetorque sensors k2[kNm/rad]Equivalent torsional
stiﬀness ke [kNm/rad]

6.1

6.1

3.33

3.33

3.23

2.73

0.95

0.95

2.112

1.886

0.739

0.739

matrix of joint stiﬀness; i ∈ Rn is the vector of gear reduction ratios; JL (θL ) ∈ Rn×n
is the symmetric, positive deﬁnite link inertia matrix; Jm ∈ Rn×n is the diagonal,
positive deﬁnite rotor inertia matrix; τd ∈ Rn is the vector of disturbance torques;τm ∈ Rn is the vector of torques applied by motor; τEXT ∈ Rn is the vector of
applied external joint torques; G(θL ) ∈ Rn is the vector of gravitational torques;C(θL , θ̇L ) ∈ Rn represents the vector of Coriolis and centrifugal torques; τm ∈ Rn
is the vector of torques applied by motor; the torque in the shaft τ ∈ Rn is directly
proportional to twist angle θ = (i−1 θm − θL ).
The transfer function from motor torque τm to link angle θL is expressed by:
W =

2
θL
ωL
.
=
τm
Jm s2 i (s2 + ωn2 )(17)

The natural frequency of two-mass system ωn and natural frequency of one-massrobot joint ωL are derived from the following equations:

ωn =

k


1
1
+
,
i2 Jm
JL

ωL =

k
.
JL

(18)

The experimental results of human-robot interaction for the ﬁrst joint (the mostloaded one) show that despite the smooth trajectory (Fig. 26) generated by theadmittance controller according to the sensed joint torque, the vibratory behavior
caused by unbalanced inertial load is recorded by torque sensor (Fig. 27).
Diﬀerent approaches to control the robot with ﬂexible joints are proposed byresearches. Spong24 proposed to use nonlinear control methods: (1) the feedback linearization technique and (2) the integral manifold method. Such approaches require
the exact knowledge of robot arm parameters. Techniques that attenuate vibrations by using negative torque feedback were elaborated.25,26 The idea behind thisapproach is that negative torque feedback reduces the eﬀective inertia of the motor
rotor in (KT +1) times, where KT is the torque gain. However, whereas this approach
can suppress the vibrations to some extent, it cannot control the main source ofvibrations, namely, the inertial loading.

December 17, 2009 11:56 WSPC/191-IJHR

D. Tsetserukou, N. Kawakami & S. Tachi

Applied torque TEXT, (Nm)

720

00189

1.5
1.0
0.5
0.0
-0.5
-1.0

Vibrations at deceleration stage of motion
Actual data plot
Low-Pass filtered data plot

-1.5
-2.0
0.0

1.5

3.0

4.5

6.0

7.5

9.0

7.5

9.0

Time t, (sec.)

Target and real trajectory, (deg.)

Fig. 26. External torque.

30

Joint angle θ1
Target impedance trajectory

-3
-6
-9
-12
0.0

1.5

3.0

4.5

6.0

Time t, (sec.)

Fig. 27. Compliant trajectory and joint angle.

Through conducting the experiments we came to conclusion that there are tworobust solutions. The straightforward and simple approach is to increase the coeﬃcient of the desired damping of the heavily loaded joints. It leads to a small magnitude of the joint angle acceleration and deceleration. However, with high damping
we cannot achieve a fast reaction of the robot upper arm to impact forces. The
approach proposed by us is to employ position feedback based on information fromaccelerometer and lead controller to improve the damping property of the compliant robot joint. The angular position of the link is calculated from accelerometerdata by means of double integration and high-pass ﬁltering. The detailed procedureof oscillation parameter identiﬁcation and design of vibration damping controller
are given in the paper.27 The block diagram of the closed loop system with leadcompensator is shown in Fig. 28.

December 17, 2009 11:56 WSPC/191-IJHR

00189

Whole-Sensitive Robot Arm for Physical Human-Robot InteractionuK ( s ωz )(s ωp)

θd

721

θL

ωL2
s 2 2ζωLs ωL2

Fig. 28. System with lead compensator.

Bode Diagram
Magnitude, (dB)

80
40
0
-40
-80

Phase, (deg)0-60

φ = 60 deg at ωc=1067 rad/sec

-120

-180
1

10

2

10

3

10

Frequency, (rad/sec)

4

10

5

10

Fig. 29. Bode diagram.

The discrete presentation of the lead compensator with sampling time of T is:x(k+1) = e−ωp T x(k) + ((1 − e−ωp T )/ωp )Uc(k) ,y(k) = θd(k) = (ωz − ωp )x(k) + Uc(k) ,

(19)
(20)

where Uc(k) is the discrete input signal; ω z , ω p are the zero and pole.
The obtained value of desired load angle θd is then processed by PD control
loop. The magnitude curve and phase-angle curve for the compensated system areshown in Fig. 29. The phase and gain margins are 60 deg. and +∞, respectively.
Therefore, compensated system meets both the steady-state and the relative stability requirements. Thus, the desirable transient behaviour was achieved throughusage of lead compensator.
We conducted the experiment in order to evaluate the performance of the proposed damping controller. During the experiment, elbow joint was ﬁxedly mounted,
and the impulse force was applied to the tip of the robot forearm. The acceleration

December 17, 2009 11:56 WSPC/191-IJHR

D. Tsetserukou, N. Kawakami & S. Tachi

20

2

Angular acceleration α , (rad/s )

722

00189

15

α = 15.142

Actual data plot
Low-Pass filtered data plot10

α = 1.696

5
0
-5
-10

T = 0.106 sec

-15
-20
0.0

0.5

1.0

1.5

2.0

Time t, (sec)2Angular acceleration α , (rad/s )Fig. 30. Acceleration without controller.

20
15

Actual data plot
Low-Pass filtered data plot

10
5
0
-5
-10
-15
-20
-25
-30
0.0

0.5

1.0

1.5

2.0

Time t, (sec)Fig. 31. Acceleration with controller.

signals without and with vibration damping controller (Figs. 30 and 31, correspondingly) were recorded. To eliminate high frequency noise, the digital low-pass ﬁlter
was deployed with cut-oﬀ frequency of 100 Hz.
The experimental results show the successful damping of oscillation magnitude.
The system resists the quick change in angular speed, and, therefore, provides moresmooth dynamic behavior.
5. Conclusion
New whole-sensitive teleoperated robot arm iSoRA was developed to provide
human-like capabilities of contact task performing in a wide variety of environments. To facilitate the developed anthropomorphic arm with joint torque measuring ability, the new optical torque sensors characterized by good accuracy, highDecember 17, 2009 11:56 WSPC/191-IJHR

00189

Whole-Sensitive Robot Arm for Physical Human-Robot Interaction

723

signal-to-noise ratio, compact sizes, easy manufacturing, high signal bandwidth,robustness, and low cost were designed and manufactured. The main novelty of
our method is application of the optical approach based on the ultra-small size PI
RPI-121 as sensitive element. The eﬀectiveness of the admittance control for ensuring the safety in human-robot interaction was experimentally illustrated on a newwhole-sensitive robot arm.
To compensate joint ﬂexibility eﬀect, we elaborated the dynamic model of therobot joint as two-mass system. The system with lead compensator and angular
position of the load feedback is proposed to dump undesired vibrations. The oscillation parameters required to design the lead controller were determined by using
experimental data and analytical equations. The experimental results show high performance of the developed controller in terms of successful damping of vibrations.
The developed robot iSoRA and control system have great potential to impact on
the robot technologies for emerging human-robot coexistence society. In future, suchrobots will assist humans in daily life tasks, and provide the physical collaborativeinteraction and haptic communication while ensuring safety to the humans.

Acknowledgments
The research was supported in part by a Japan Society for the Promotion of Science(JSPS) Postdoctoral Fellowship for Foreign Scholars.

References
1. I. Asimov, I, Robot, (New York: Doubleday & Company, 1950).
2. Y. Sakagami, R. Watanabe, C. Aoyama, S. Matsunaga, N. Higaki and K. Fujimura,
The intelligent ASIMO: System overview and integration, IEEE/RSJ Int. Conf. onIntelligent Robots and Systems (IROS) (IEEE Press, Lausanne, Switzerland, 2002),
pp. 2478–2483.
3. K. Kaneko, F. Kanehiro, S. Kajita, H. Hirukawa, T. Kawasaki, M. Hirata, K. Akachi
and T. Isozumu, Humanoid Robot HRP 2, in IEEE Int. Conf. Robotics and Automation (ICRA) (IEEE Press, New Orleans, 2004), pp. 1083–1090.
4. V. J. Lumelsky and E. Cheung, Real-time collision avoidance in teleoperated wholesensitive robot arm manipulators, IEEE Trans. on Systems, Man, and Cybernetics
23(1) (1993) 194–203.
5. N. Mitsunaga, T. Miyashita, H. Ishiguro, K. Kogure and N. Hagita. Robovie IV: A
communication robot interacting with people daily in an oﬃce, Proc. IEEE/RSJ Int.
Conf. Intelligent Robots and Systems (IROS) (IEEE Press, San Diego, US, 2006),
pp. 5066–5072.
6. C. H. Wu and R. P. Paul, Manipulator compliance based on joint torque control, Proc.
IEEE Conf. Decision and Control (IEEE Press, Albuquerque, US, 1980), pp. 88–94.
7. G. Hirzinger, A. Albu-Schaﬀer, M. Hahnle, I. Schaefer and N. Sporer, On a newgeneration of torque controlled light-weight robots, Proc. IEEE Int. Conf. Robotics
and Automation (ICRA) (IEEE Press, Seoul, Korea, 2001), pp. 3356–3363.
8. M. Hashimoto, Y. Kiyosawa and R. P. Paul, A torque sensing technique for robots
with harmonic drives, IEEE Trans. on Robotics and Automation 9(1) (1993) 108–116.

December 17, 2009 11:56 WSPC/191-IJHR

724

00189

D. Tsetserukou, N. Kawakami & S. Tachi

9. I. Golder, M. Hashimoto, M. Horiuchi and T. Ninomiya, Performance of gain-turned
harmonic drive torque sensor under load and speed conditions, IEEE/ASME Trans.
on Mechatronics 6(2) (2001) 155–160.
10. T. Sakaki and T. Iwakane, Impedance control of a manipulator using torque-controlled
lightweight actuators, IEEE Trans. Industry Applications 28(6) (1992) 1399–1405.
11. A. Bicchi and G. Tonietti, Fast and soft arm tactics: dealing with the safetyperformance trade-oﬀ in robot arms design and control, IEEE Robotics and Automation Magazine 11(2) (2004) 22–33.
12. S. Tachi, N. Kawakami, M. Inami and Y. Zaitsu, Mutual telexistence system using
retro-reﬂective projection technology, International Journal of Humanoid Robotics
1(1) (2004) 45–64.
13. R. Tadakuma, Y. Asahara, H. Kajimoto, N. Kawakami and S. Tachi, Development ofanthropomorphic multi-D.O.F. master-slave arm for mutual telexistence, IEEE Trans.
on Visualization and Computer Graphics 11(6) (2005) 626–636.
14. D. Tsetserukou, R. Tadakuma, H. Kajimoto and S. Tachi, Optical torque sensors
for implementation of local impedance control of the arm of humanoid robot, Proc.
IEEE Int. Conf. Robotics and Automation (ICRA) (IEEE Press, Orlando, US, 2006),
pp. 1674–1679.
15. S. Hirose and K. Yoneda, Robotic sensors with photodetecting technology, Proc. 20thInt. Symp. on Industrial Robots (ISIR) (Tokyo, Japan, 1989), pp. 271–278.
16. A. Kitamura, S. Adachi and S. Tanabe, Optical displacement sensor and external force
detecting device, US Patent 7 057 154 B2, Jun 6, 2006.
17. Minebea, 6-Axial Force Sensor, Minebea Co., Ltd., [Online], Available: http://
www.minebea-mcd.com/6-axial/index.htm.
18. Photointerrupter Design Guide. Product Catalog of ROHM, pp. 6–7, 2005.
19. D. Vischer and O. Khatib, Design and development of high-performance torque controlled joints, IEEE Trans. on Robotic and Automation 11(4) (1995) 537–544.
20. ATI, Multi-axis F/T sensor, ATI Industrial Automation, [Online], Available: http://
www.ati-ia.com/
21. N. Hogan, Impedance control: an approach to manipulation, Part I–III, Trans. of
ASME Journal of Dynamic Systems, Measurement and Control 107 (1985) 1–23.
22. R. Carelli and R. Kelly, Adaptive impedance/force controller for robot manipulators,
IEEE Trans. Automatic Control 36(8) (1991) 967–972.
23. R. Colbaugh and A. Engelmann, Adaptive compliant motion control of manipulators:
theory and experiments, Proc. IEEE Int. Conf. on Robotics and Automation (ICRA)(IEEE Press, San Diego, US, 1994), pp. 2719–2725.
24. M. W. Spong, Modeling and control of elastic joint robots, Trans. of ASME Journalof Dynamic Systems, Measurement and Control 109(4) (1987) 310–319.
25. G. Zhang and J. Furusho, Control of robot arms using joint torque sensors, Proc.
IEEE Int. Conf. Robotics and Automation (ICRA) (IEEE Press, Albuquerque, US,1997), pp. 3148–3153.
26. C. Ott, A. Albu-Schaﬀer, A. Kugi, S. Stramigioli and G. Hirzinger, A passivity based
cartesian impedance controller for ﬂexible joint robots — Part I: Torque feedback
and gravity compensation, Proc. IEEE Int. Conf. Robotics and Automation (ICRA)(IEEE Press, New Orleans, US, 2004), pp. 2659–2665.
27. D. Tsetserukou, N. Kawakami and S. Tachi, Vibration damping control of robot arm
intended for service application in human environment, Proc. IEEE-RAS Int. Conf.
on Humanoids Robots (Humanoids) (IEEE Press, Daejeon, Korea, 2008), pp. 441–446.

December 17, 2009 11:56 WSPC/191-IJHR

00189

Whole-Sensitive Robot Arm for Physical Human-Robot Interaction

725

Dzmitry Tsetserukou received his BS degree with honors inElectrical Engineering from the Mogilev Machine Building Institute, Belarus, in 1999, the MS degree in Mechanical Engineering from the National Academy of Sciences of Belarus, in 2002,
and the PhD degree in Information Science and Technology fromthe University of Tokyo, Japan, in 2007. From 2002 to 2004, he
was a Research Associate in the scientiﬁc research group at the
National Academy of Sciences of Belarus. He is currently a JSPS
Post-Doctoral Fellow in the Department of Information Physics and Computing
at the University of Tokyo, Japan. He is a member of the IEEE and VRSJ andthe author of over 50 technical publications, 3 patents, book chapter, and a book.
His research interests include humanoid robots, force/torque sensors, impedance
and admittance control, teleoperation, haptic interfaces and displays, and aﬀective
haptics.
Naoki Kawakami received his MS and PhD degrees from the
University of Tokyo, Japan, in 1996 and 1999, respectively. He iscurrently a Lecturer in the Department of Information Physics
and Computing at the University of Tokyo. His research interests
include virtual reality, real-time remote robotics, telexistence,and retro-reﬂective projection technology. He is the author ofover 150 technical publications.
Susumu Tachi received his BS, MS and PhD degrees fromthe University of Tokyo, in 1968, 1970 and 1973, respectively.
From 1975 to 1989, he was a Director of the Biorobotics Divisionat the Mechanical Engineering Laboratory, Ministry of International Trade and Industry, Tsukuba Science City, Japan. From1989 to 2009, he was a Professor in the Department of Information Physics and Computing, the University of Tokyo. He iscurrently a Professor in the Graduate School of Media Design,
Keio University, Japan. He is a Founding Director of the Robotic Society of Japan(RSJ), and a Founding President of the Virtual Reality Society of Japan (VRSJ).
From 1988, he serves as a Chairman of the IMEKO (International Measurement
Confederation) Technical Committee on Measurement in Robotics. He is a memberof IEEE VR Steering Committee, and he served as a General Chair of IEEE VR
2001 and 2002. He was responsible for the following research projects: Guide Dog
Robot (1976–1983), National Large Scale Project on Advanced Robotics in Hazardous Environment (1983–1990), National Large Scale Project on Humanoids andHuman Friendly Robotics (HRP) (1998–2003), and CREST Project on TelexistenceCommunication Systems (2000–2005). He is the author of over 500 technical publications. His research interests include virtual reality, real-time remote robotics,
telexistence, and retro-reﬂective projection technology.

December 17, 2009 11:56 WSPC/191-IJHR

00189