Advanced Robotics 25 (2011) 1271–1294

brill.nl/ar(c) Robotics Society of Japan, 2011

Full paper
Remote Tactile Transmission with Time Delay for Robotic
Master–Slave Systems
S. Okamoto a,∗ , M. Konyo a , T. Maeno b and S. Tadokoro aaGraduate School of Information Sciences, Tohoku University, 6-6-1 Aoba, Aramaki-aza, Aoba-ku,
Sendai 980-8579, Japanb
Graduate School of System Design and Management, Kyoseikan, 4-1-1 Hiyoshi, Kita-ku,
Yokohama 223-8526, Japan
Received 26 March 2010; accepted 27 May 2010

Abstract
This study develops a method to compensate for the communication time delay for tactile transmission
systems. For transmitting tactile information from remote sites, the communication time delay degrades thevalidity of feedback. However, so far time delay compensation methods for tactile transmissions have yet
to be proposed. For visual or force feedback systems, local models of remote environments were adopted
for compensating the communication delay. The local models cancel the perceived time delay in sensory
feedback signals by synchronizing them with the users’ operating movements. The objectives of this study
are to extend the idea of the local model to tactile feedback systems and develop a system that delivers
tactile roughness of textures from remote environments to the users of the system. The local model fortactile roughness is designed to reproduce the characteristic cutaneous deformations, including vibratory
frequencies and amplitudes, similar to those that occur when a human finger scans rough textures. Physicalproperties in the local model are updated in real-time by a tactile sensor installed on the slave-side robot.
Experiments to deliver the perceived roughness of textures were performed using the developed system.
The results showed that the developed system can deliver the perceived roughness of textures. When thecommunication time delay was simulated, it was confirmed that the developed system eliminated the timedelay perceived by the operators. This study concludes that the developed local model is effective for remotetactile transmissions.
© Koninklijke Brill NV, Leiden, 2011
Keywords
Tactile roughness, haptics, tactile display, tactile sensor, time delay* To whom correspondence should be addressed. E-mail: okamoto@rm.is.tohoku.ac.jp© Koninklijke Brill NV, Leiden, 2011

DOI:10.1163/016918611X574713

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1. Introduction
The transmission of tactile sensations enables human operators of robotic systems
to perceive textures (e.g., rough or smooth) when touching a material using robots.
Textures in a remote environment are conveyed to an operator by means of tactile
sensors and displays. This approach can also be useful for operators of teleoperation
systems; it can enable texture recognition, improve the perception of reality andallow for stable grasps to prevent slipping.
Thus far, several researchers have reported on the development of tactile transmission systems that can be used to transmit tactile information from tactile sensors
to human operators. Most systems comprise tactile displays and sensors. Tactile
transmission systems that can be used for palpation in minimally invasive surgery
have been proposed for medical purposes [1–3]. In such systems, tactile sensory
equipment is installed on the tips of probes. These probes can be inserted into thehuman body to scan tissues such as tumors and blood vessels. Then, the tactile information sensed by the sensors is presented to physicians through tactile displays. For
master–slave-type robots, some tactile transmission systems aim to support robotic
teleoperations such as gripping. Some systems target high-frequency vibrations of
end-effectors triggered by actions such as making contact with objects; such systems present vibratory stimuli to the operator’s finger [4, 5]. Kuchenbecker andNiemeyer developed a system whose force display also functions as a cutaneous
stimulator to transmit high-frequency vibrations [6]. Shimojo et al. transmitted
the pressure distribution within a slave-robot hand holding an object to an operator by stimulating the nerves of the operator using microelectrodes [7]. Murray
and Klatzky proposed a system that transmits information on the intensity of thegripping force of a slave robotic gripper to the operator through vibrotactile stimuli [8]. Some tactile transmission systems were developed such that the operators ofrobotic systems perceive the texture of materials being maneuvered by the robots [9,10]. Fearing et al. proposed and numerically analyzed a strain-matching method inwhich the normal strain of the finger touching real objects through an elastic layer
is replicated in tactile transmission systems [11]. Prostheses with onboard tactile
sensors and displays are important applications of tactile transmission technology.
They provide users with tactile information such as contact points or the magnitudeof pressure applied on artificial limbs [12–14].
As described above, tactile transmission systems have been implemented in various ways. However, tactile transmission systems with a communication time delay
have not yet been studied. One of the problems induced by time delays is a temporalgap between the exploratory movements of the operator and corresponding tactile
feedback presented back to him/her. The allowable time delay is approximately 40–
60 ms [15]. Users of tactile displays notice the existence of a delay above 60 ms.
In this case, noticeable temporal delay deteriorates the validity of tactile feedback.
When the delay is as large as 40 ms, users find that the textures perceived throughdelayed feedback are different from those perceived through non-delayed feedback.

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Therefore, the temporal gap between the users’ hand movements and tactile feedback to the users needs to be compensated.
When tactile transmission systems are extended to remote environments, a communication time delay occurs between the tactile sensor and display. In addition,
in some cases, tactile sensing requires time. For example, because roughness information such as asperity or texture is spatially distributed information, sensing suchinformation requires time, which results in a time delay. Thus, although the amountof delay depends on the situation, time delay inevitably exists in tactile transmission
systems.
Local or virtual models of the remote environments were used to compensate
for the delay in sensory feedback systems. The master-side system produces sensory feedback on the basis of the physical interaction between the local model andusers’ operating movements. Since the sensory feedback is synchronized with theoperators’ movements, the temporal gap between the sensory feedback and operators’ movements is eliminated. However, the local model has not yet been extended
to tactile feedback and its effectiveness for tactile feedback has not been confirmed.
The objective of this study is to extend the idea of the local model to tactile transmissions. Then, it is experimentally confirmed that the developed local model transmits
the perceived roughness of textures to the operators. Finally, it is shown throughexperiments that the local model cancels the perceived time delay when the communication time delay is simulated.
The remainder of this paper is organized as follows. The extension of local models to tactile roughness is proposed in Section 2. The roughness transmission systemdeveloped on the basis of the proposed local model is described in Section 3. The
physical properties of the local model are updated by the tactile sensor installedon the slave system. The estimation of physical properties of the texture using thetactile sensor is described in Section 4. Finally, the experiment on roughness transmission using the developed system is described in Section 5.
2. Tactile Transmission System with Time Delay
2.1. Proposals: Tactile Feedback on the Basis of a Local Model
For teleoperation systems with sensory feedback, in order to compensate for transmission time delays, local models for remote environments or robots were adopted.
For instance, Bejczy et al. developed a master system that could construct a predictive image of a remote robot and show the image to the operator [16]. Kotoku etal. developed a system in which the force feedback presented to the operator was
simulated on the master side through a local geometric model of remote environments [17]. These models eliminated the time delay in visual or force feedback fortheir systems. The local model can be extended to a virtual space where physical interactions involving sensory feedback take place [18]. In these studies, master-sidesystems constructed models of remote environments or robots and sensory feedback was synchronized with the operators’ movements. Local models have yet to1274

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Figure 1. Block diagram of the developed tactile transmission system.

be adopted for tactile transmission systems. However, as well as the case of visualand force feedback, the idea that the local model produces sensory feedback in synchronization with operators’ hand movements is considered to function for the caseof tactile feedback. This is the first study in which the local model is developed forcompensating for the perceived delay of tactile transmission systems.
Figure 1 shows the concepts of the developed system. In this system, tactile information sensed by the tactile sensor is not presented to the operators directly,
because it includes time delays. The master-side system constructs a local model oftarget textures that are placed in the slave environment. The deformations applied
to the finger skin of operators are computed through the local model and the kinetic information of operator’s hand movements. Since the tactile stimuli computed
through the local model are synchronized with the operators’ hand movements, thetemporal gap between the hand movements and tactile feedback is eliminated. The
local model of the target textures is continuously estimated and updated by the tactile sensor.
When xm (t) and xs (t) are, respectively, the operator’s hand position at the masterside and that of slave hand as shown in Fig. 1, the outputs of the tactile sensor aredescribed as:ys (t) = G(xs (t))(1)

xs (t) = xm (t − Td ),(2)

where G and −Td are the interaction between the tactile sensor and target texture
and the communication time delay, respectively.
In the case of tactile transmission system with no delay compensation method
applied, ys (t) is directly delivered to the master-side system. The tactile feedback
presented to the operator is:ym (t) = ys (t − Td ) = G(xm (t − 2Td )).

(3)

If the time delay does not exist, then ym (t) is equal to G(xm (t)). However, due tothe time delay, ym (t) includes the delay of 2Td compared with xm (t).
In the case that the local model is applied, the tactile feedback presented to theS. Okamoto et al. / Advanced Robotics 25 (2011) 1271–1294

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ym (t) = g(p(t − Td ), xm (t))p(t) = E(ys (t)),

(4)
(5)

operator is:where g, p and E are the interaction between the operator’s hand position andtactile textures within the local model, physical properties of target textures, andthe process to estimate the physical properties from the tactile sensor’s outputs,
respectively. Since ym (t) is determined using xm (t), no temporal gap between ym (t)and xm (t) exists. Note that p(t − Td ) includes the time delay. Hence, the physicalproperties used for determining ym (t) are those estimated by the tactile sensor Td
time ago.
Although the local model provides advantages that the temporal gap between theoperator’s movements and tactile feedback is canceled, the local model does not
resolve all the problems of tactile transmission systems with communication timedelay. The biggest problem may be a delay of texture information that is transmitted. In (4), p(t − Td ) includes the time delay. This happens because, as is the natureof local models, spatial changes in the textures being explored require time to be reflected in the local model due to the communication time delay. Due to the temporal
cost of tactile sensing, which is the cost of sensing process E in Fig. 1, the transmission of texture information includes larger delay for the local model-based feedback
than the system with no time compensation method applied. In order to reduce thetemporal cost of tactile sensing, real-time sensing methods are recommended forthe implementation. Owing to this delay of transmission of physical properties, thesystem is effective when the target texture is relatively uniform rather than for textures with non-uniform physical properties.
2.2. Design of a Local Model for Tactile Roughness
In the case that the local model is adopted for sensory feedback, the design of local
models is the most important problem. Since local models have not been adopted
for tactile feedback so far, there is no guideline for their design. As well as the
feedback of visual and force stimuli, the local model for tactile transmission systems synchronizes the tactile feedback presented to the operator with his/her hand
movements. In order to do this, the local model needs to produce tactile stimuli
by combining the physical parameters of target textures and operators’ hand movements. However, in general, the physical properties necessary for computing tactile
feedback are unknown. In addition, many physical properties affect the deformationof finger skin and these properties cannot all be reflected in the local model. This
study proposes to construct a local model such that it includes the texture properties
significantly effective for human perception of roughness, and replicates the characteristic physical interactions between the operator’s hand movements and texture.
We selected the physical properties of textures and types of physical interactions
that significantly affect roughness perception from the literature on psychophysics
and neurophysiology. The perceived roughness of the grating scales used in this1276

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study strongly depends on the surface wavelength or groove width of the scales[19–21]. The vibratory frequency of cutaneous deformation determined by the handvelocity affects the perceived roughness [22, 23]. From this literature, the surface
wavelength of the texture, and the frequency and amplitude of cutaneous vibratory
deformation that occurs when the finger scans the texture are considered to be effective for roughness perception.
Taking into account the above studies, the following model is employed. The
model delivers roughness sensations by controlling the fundamental frequencies
and amplitude of vibratory stimuli presented to the operator’s finger skin when
the finger scans a texture. When the operator explores a texture of which surface
wavelength components are λ1 , λ2 , . . . , and their amplitudes are A1 , A2 , . . . , thedeformation that is applied to the finger skin is determined by:y(t) =

N

i=1



x(t)
Ai sin 2π
λi

Ai > Aj(i < j ),(6)(7)

where x(t) and N denote the finger position on the texture and the number ofwavelength components that the local model replicates. λi and Ai of textures are
estimated by computing the short-time Fourier transform (STFT) of the tactile sensor’s outputs, which is described in Section 4. The instantaneous frequency of thevibratory stimuli presented to the operator is given by f (t) = v(t)/λ, where v(t)denotes the velocity of the operator’s hand.
The above-mentioned local model is close to the models used for displaying
virtual textures. Okamura et al. [24] and Konyo et al. [25] proposed a method topresent virtual textures using a vibrotactile stimulus whose frequency and amplitude are controlled on the basis of the surface wavelength and information aboutuser movements. The objectives of these studies are the presentation of virtual textures. Their systems were not connected to tactile sensory systems and they did not
validate whether the feelings of remote textures were conveyed to the operators ornot.
The above-described local model represents the tactile roughness of texture by
replicating abstract cutaneous deformations similar to those that occur when a human finger scans a texture. One question is whether the set of fundamental frequencies can present the tactile roughness of textures. Confirming this question throughexperiments is one of the objectives of this study, as is described in Section 5. In
Section 5, the participants select roughness textures that are felt to be closest to
the textures perceived through the tactile transmission system. However, even if theoperators select exactly the same textures through the developed system, this does
not mean that the quality of tactile roughness is completely transmitted to the operators. The quality depends on the number of replicated wavelength components(N ), for example. The authors preliminarily confirmed that when the larger N is
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the operators. This study does not address the quality of tactile feelings that dependon N . Since it is possible to conduct the experiments for the sample textures usedin this study even when N is 1, the local model used in this study replicates a single
wavelength component: N = 1.
3. Setup of the Tactile Transmission System
The developed master–slave system comprises the tactile display and sensor-sidesystems described in Sections 3.1.2 and 3.2.2, respectively. Both systems are connected by ethernet cables. The position of the tactile sensor at the slave-side system
is position controlled on the basis of the position of the linear slider at the masterside system. The communication rate for the position information between themaster and slave systems is set to 250 Hz.
3.1. Master Side
3.1.1. Tactile Stimulator
The developed system employs vibrotactile stimuli to present tactile roughness to the operators. The vibratory actuator used is a piezo-stack-type actuator(ASB510C801P0; NEC Tokin). The maximal output displacement of the vibrator
is approximately 55 µm for the peak-to-peak amplitude when the applied voltage
is 150 V. The output displacement changes approximately linearly with the applied
voltage. Its frequency response reaches −3 dB at 310 Hz when the peak-to-peakvoltage is 150 V. The response is relatively flat up to a frequency of 300 Hz, which
is used for the tactile stimuli. The output force of the actuator is approximately
800 N and is considerably higher than the finger force.
3.1.2. Tactile Display System
A block diagram of the tactile display system [15] functioning as the master-sidesystem is shown in Fig. 2. The vibrator is mounted on a linear slider. The position of the slider on a linear guide is measured using an optical encoder. A control
computer receives the information on the position of the slider and determines thevoltage supply to the vibrator. A single-axis force sensor installed beneath the vibrator senses the pressing force of the operator’s finger. Drastic changes in this force
affect the roughness perceived by the participants [19]. During the experiments, thesensed force is monitored to ensure that it does not exceed 2 N.
Figure 3 shows an image of the tactile display. The participants place their finger
on the vibrator and move their hand along the linear guide (X-axis). The vibrator
produces deformations along the Z-axis. The participants receive vibrotactile stimuli according to their hand movements. They are instructed to touch the equipment
only with their right middle finger, as shown in Fig. 4. The participants agreed thatthe vibratory stimuli transmitted by the tactile display were similar to the ones they
perceived when they explored rough surfaces such as grating scales using a stylus.

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Figure 2. Block diagram of the master-side system.

Figure 3. Master-side system.

Figure 4. Contact between the participant’s finger and vibrator.

3.2. Slave Side
3.2.1. Tactile Sensor
The tactile sensor of the developed tactile transmission system needs to estimate
the surface wavelength of texture surfaces. The estimation method is described in
Section 4.1. Also, the sensor should have elasticity similar to that of human fingers because the developed system determines the amplitude of vibratory stimuli
presented to the operators from the magnitude of the sensor deformation. As a tactile sensor that satisfies above conditions, the developed system employs a humanfinger-like tactile sensor [26].
Figure 5 shows the structure of the tactile sensor. It has a strain gauge (KFRS02-120-C1-13; Kyowa) embedded in silicone rubber layers; the gauge functions as
a transducer. The silicone rubber has two layers of which the outer layer is harderthan the inner one. Each layer is designed such that its Young’s modulus is similar
to that of the epidermis and dermis of human fingers. The surface of the sensor has
ridges and the width of each ridge is 0.6 mm. The strain gauge is beneath the ridges.
The tactile sensor is 3-times larger than the average middle finger of a human handand its thickness is 10 mm.
Simple tactile sensors can also be used for the experiments in this study. However, using the human finger-like tactile sensor has some advantages. Since its
layered structure prevents large deformations of its outer layer, the sensor outputs
are not saturated even with a large pressing depth (up to 2.5 mm). The sensor is

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Figure 5. Tactile sensor for the tactile transmission system.

Figure 6. Schematic diagram of the slave-side system.

Figure 7. Slave-side system: tactile sensor and slave arm.

also capable of estimating the elasticity of target objects and the friction between
the target objects and sensor [26]. The applications of the sensor can be extended toa transmission system for multiple tactile modalities, including tactile friction andsoftness. However, the dimensions of the sensor are different from those of actual
human fingers, which gives rise to problems (described in Section 4.2). Therefore,
it needs to be downsized in the future.
3.2.2. Slave-Side System
A schematic diagram and image of the slave-side system are shown in Figs 6 and 7,
respectively. The sensor is attached to a single-axis arm (MR12T; Yamaha). The
arm moves along the X-axis; it is position-controlled by a controller and computer.
The output signals of the strain gauge of the tactile sensor are transmitted to the
AD board of the control computer using a strain amplifier (MCD-8A; Kyowa). The
amplifying level of the strain amplifier is set to 0.01 V/micro-strain. The sampling
frequency for the gauge outputs is 1 kHz. The Z-axis of the sensor is fixed. The

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Figure 8. Trapezoidal grating scale: overall and cross-sectional view.

movement along the Z-axis is not used in this study. In order to extend the experimental setup to the Z-axis movements for a more realistic tactile transmission
system, the local model described in Section 2 should be modified to involve thepressing force of the tactile sensor. This is because the pressing force of the tactile
sensor affects the magnitude of deformation of the sensor. Since this study does not
aim at the construction of a realistic local model for improving the quality of tactile
roughness, the experimental equipment does not have the degree of freedom in the
Z-axis.
The tactile sensor scans grating scales with alternating grooves and ridges, as
shown in Fig. 8. The ratio of the ridge width (RW) and groove width (GW) of thescales is 1. Hence, the scales are characterized by λ = RW + GW. The tactile sensor
is inserted into the specimens to a depth of 1 mm with a static reaction force in thenormal direction of approximately 0.6 N.
4. Estimation of Surface Wavelength and Vibratory Amplitude of the Tactile
SensorAs mentioned in Section 2, the remote texture’s surface wavelength λ and amplitude of the sensor deformation need to be estimated to generate roughness stimuli
through the local model. The STFT-based method used to estimate λ is describedin Section 4.1. The amplitude of the sensory outputs is used as information relating
to the magnitude of sensor deformation. The amplitude is defined by the functiongA (λ(t), v(t)), which is empirically defined in Section 4.2.
4.1. Estimation of the Surface Wavelength by STFT
4.1.1. STFT-Based Wavelength Estimation Method
When the tactile sensor scans target textures, it vibrates because of collisions. The
λ value can be estimated from the frequency of this vibration using the relationshipλ = v(t)/f (t), where f (t) is the vibratory frequency of the sensor at t and v(t) is
the scanning speed of the sensor.
STFT is computed from the time-sequential outputs of the strain gauge of thetactile sensor. The power spectrum density obtained through STFT indicates thevibratory frequencies of the sensor. STFT-based estimation is suitable for the developed tactile transmission system because STFT deals with the frequency components of sensory vibration in real-time. In order to extract a single fundamental
frequency, the frequency whose signal power is maximal in the spectrum is definedas f (t). The window function is the Hamming function.

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In order to balance the temporal and frequency resolutions of STFT experimentally, the window size of STFT was experimentally determined to be 32 ms. The sizeof the window was selected from among 16, 32, 64 and 128 ms; this was so thatthe estimation error would be minimal when the sensor scanned the grating scaleswith λ ranging from 0.5 to 3.0 mm at the average human exploratory velocity. The
average velocity was determined from a previous study in which 13 participants
conducted a tactile exploration of virtual textures using the same tactile display device as the one used in this study [15]. The average peak velocity of exploratory
movements was 197.0 ± 43.9 mm/s and the average reciprocating frequency was
1.07 ± 0.40 Hz. The wave profile of the velocity was approximated to be sinusoidal.
Therefore, the average velocity was defined to be v(t) = 197.0 sin (2π1.07t) mm/s.
4.1.2. Experimental Validation of the Estimation Method
Experimental validation of the STFT-based estimation method was performed. Figure 9 shows the surface wavelengths estimated by the method when the sensor
scanned the roughness specimens at the average exploratory velocity mentioned
above. The wavelength was estimated on the basis of the data acquired for fivecycles (i.e., 4.665 s). The diamonds and error bars in Fig. 9 indicate the averagevalue per millisecond and standard deviation, respectively. The dashed line indicates the match with reference values. The estimation was approximately correct
except for wavelengths ranging from 2 to 3 mm. This deterioration in the validityof the method is considered to be due to the frequency resolution of the STFT. The
vibratory frequency of the sensor becomes relatively smaller with larger surface
wavelengths. The lower the vibratory frequency, the more significant the effect of
the resolution on the estimation error. In terms of the accuracy of estimation, there
is no requirement for the developed roughness transmission system. Since the accuracy of estimation is relatively good for surface wavelengths smaller than 2 mm, inthe experiment of roughness transmission discussed in Section 5, roughness specimens with those wavelengths are used to evaluate the efficiency of the local model.
4.2. Experimental Determination of the Amplitude of Sensory Signals as a
Function of Wavelength and Scanning Speed
The gauge output of the tactile sensor during the scanning of textures reflects its vibrations. The function gA (λ(t), v(t)) determines the amplitude of sensory outputs

Figure 9. Surface wavelength of roughness samples estimated by the STFT-based method.

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from the surface wavelength of roughness specimens and the scanning speed of thesensor. gA (λ(t), v(t)) was determined experimentally. The amplitude of the output
signals was obtained from STFT of the signals. The peak value in the power spectrum was used as the amplitude of the fundamental component in the signals. In
order to determine the function, a scanning experiment was conducted by changing
λ and v(t).
It was predicted that when the sensor scans the roughness specimens, the deformation of the sensor increases as the groove widths of the specimens increase
with λ while the amplitude of gauge outputs increases with the magnitude of deformation of the sensor. Furthermore, as the scanning speed increases, the vibratory
frequency of the sensor increases and the sensor deforms to a lesser extent due to its
viscosity. Therefore, gA (λ(t), v(t)) was predicted to increase with λ and decrease
with v(t).
The signal power of the gauge outputs was experimentally found to be strongly
affected by λ, but hardly affected by v(t). Therefore, the amplitude of the gauge
outputs was determined to be a function of λ. The experimental conditions, results
and determined function are described in detail in the following sections.
4.2.1. Experimental Conditions
In the experiment, the specimens were scanned using the sensor at constant velocities. Then, the STFT of the output signals generated by the strain gauge during
scanning was computed and the maximal value in the power spectrum was recorded.
The scanning speed was in the range of 50–230 mm/s, and the wavelength was varied in the range of 0.8–2.0 mm. The data for computation were obtained while thesensor moved from the 50-mm point to the 150-mm point on the specimens; thedata for the first 50 mm were not used for computation. This was to avoid transientphases of the sensory outputs at the beginning of the sensor motion.
4.2.2. Experimental Results
Figure 10 shows the relationship between the acquired signal powers and scanningvelocity for different surface wavelengths. The plots show the average signal powers per millisecond. Contrary to the predictions, the signal power was not strongly

Figure 10. Power versus scanning velocity plot: relationship between signal power and scanningspeed.

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Figure 11. Power versus spatial wavelength plot: relationship between signal power and spatial wavelength of roughness specimens.

affected by the scanning velocity. Within the investigated range, the signal power
did not exhibit a specific trend with respect to the scanning velocity and the plot wascomparatively flat. Hence, the effect of the scanning velocity on the signal power
was ignored. However, from a dynamic point of view, it should be noted that thepower spectral densities were found to vary due to the stick–slip effect.
Figure 11 shows the relationship between the signal power and surface wavelength of the roughness specimens. The error bars indicate the standard deviation.
The relationship between the signal power and surface wavelength of the specimens was nonlinear, and the power exhibited a local maximum for wavelengths of
1.2–1.6 mm. The width of the distal ridges of the tactile sensor is 0.6 mm, which
was similar to the groove width of the roughness specimens. The local maximum
is observed only if these two parameters matched. A different data set also produced the local maximum around the same position. In the future, to avoid thislocal maximum and improve the reality of the tactile feedback, the sensor needs
to be downsized so that the width of the distal ridges of the tactile sensor is equal
to that of the human finger’s epidermal ridges. The relationship between the signal
power and surface wavelength was approximated (R 2 = 0.94) using the following
fourth-order function:gA (λ) = 0.734λ4 − 3.88λ3 + 7.38λ2 − 5.935λ + 1.723(V2 s).

(8)This equation is used to determine the amplitude of vibrotactile stimuli presented
to the operators in the roughness transmission experiment described in Section 5.
The equation is valid for λ from 0.8 to 2.0 mm and the later experiments is carriedout within this range for λ.
5. Experiment: Transmission of Tactile Roughness
5.1. Experimental Design
Experiments to transfer the tactile roughness of grating scales are performed using
the developed system. The participants explore the grating scales placed at the slave
side through the developed master–slave system and select the ones they felt to beclosest among comparison scales placed at the master side.

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In order to confirm that the tactile roughness of textures is transmitted to theoperators through the developed local model, Experiments 1 and 2 are conducted.
Experiment 1 is on the direct feedback and in Experiment 2 the roughness stimuli
are fed backed through the local model. In the case of direct feedback, the output voltages of the tactile sensor are directly input to the tactile display. Hence,the cutaneous deformations applied by the tactile display basically have the sameprofile as the voltage outputs of tactile sensor. In the case of feedback through thelocal model, the cutaneous deformations presented to the operator’s finger are determined by the local model. The details of tactile stimuli for each feedback condition
are described in Section 5.2. In order to evaluate the roughness feelings transmitted
to the operators in each experiment, the errors between the textures explored at theslave side and those answered by the operators are calculated. If the local modeltransmits tactile roughness of textures as well as the direct feedback, the errors are
expected to close between Experiments 1 and 2. Also, the introspective reports areinterviewed after each experiment. In the interviews, the qualitative differences oftactile feedback between Experiments 1 and 2, and criteria for their answers are
asked to the participants.
In order to confirm the validity of the local model toward communication timedelay, Experiments 3 and 4 are conducted. The validity means that the local model
cancels the perceived time delay between the exploratory movements and tactile
feedback, and the error of explored and perceived textures is smaller for the caseof the local model than the system with no delay compensation method applied.
Experiment 3 is on the direct feedback with a simulated delay inserted. In Experiment 4, the delay is compensated by the local model. The manner in which thesesimulated time delays are generated is described in Section 5.2. The errors between
the textures explored and answered by the operators are calculated for each of Experiments 3 and 4. If the time delay degrades the capability of tactile transmission
in the case of direct feedback and the local model compensates the delay, then theerrors for Experiment 4 are expected to be smaller than those for Experiment 3.
After each experiment, the introspective reports are interviewed.
The schematic view of the above four types of experiments is shown in Fig. 12.
Experiment 1 is on direct feedback with no simulated delay. Experiment 2 is on thelocal model feedback with no simulated delay. Experiment 3 is on direct feedback

Figure 12. Schematic view of the four types of experiments.

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with a simulated delay. Experiment 4 is on the local model feedback with simulateddelay. The participants are eight staff members and students of Tohoku University
in their 20s and 30s. All eight participants participated in the four types of experiments.
5.2. Vibrotactile Stimuli
In Experiments 1 and 3, the experiments are on the direct feedback. In the case of
direct feedback, the tactile sensor’s outputs are directly input to the tactile display.
The sensory outputs are captured by the slave-side computer through the strain
amplifier. The captured values are transferred to the master-side computer by the
ethernet at the rate of 1 kHz. The master-side computer amplifies the transferredvalues by a scale constant and inputs them to the tactile display. Therefore, theoutput displacement that the tactile stimulator generates is given by:
hd (t) = ad · ys (t),(9)

where ad and ys (t) are the amplifying scale of the output voltage and output ofstrain gauge, respectively.
In Experiments 2 and 4, the tactile feedback is determined by the local model.
The displacement that the vibratory stimulator applies to the participants’ finger
skin at the master side is given by:


x(t)
,
(10)
hl (t) = al (λ(t)) sin 2πλ(t)where al (λ), x(t) and λ(t) are the amplitude of displacement, the participants’ hand
position along the linear slider and the surface wavelength that the slave-side systemestimates, respectively. The amplitude of the vibratory displacement is given by:gA (λ),(11)
Pmax
where B and Pmax are the maximum displacement that the stimulator can generate
and a constant value used to adjust gA (λ) to the output displacements, respectively:
B = 55 µm. The amplitude of the displacement is determined by gA (λ), which isthe amplitude of the tactile sensor outputs given by (8).
For both the direct and model-based feedback, scaling constants ad and Pmax
that determine the amplitudes of vibrotactile stimuli were tuned by people who
had experienced the developed master–slave system. They determined the scaling
constants such that the roughness of texture perceived through the master–slave
system was felt to be close to that acquired when scanning the same texture by barefingers: ad = 23.8 µm/V, Pmax = 0.143 V2 s.
A simulated time delay for Experiments 3 and 4 is described here. As shown in
Fig. 12, the time delay is simulated at the master side and inserted into the tactile
feedback. A delay generator stores data transferred from the slave side into a FIFO
queue and outputs data from the queue according to the amount of simulated delay.
In Experiments 3 and 4, the simulated delay is subjected to a normal distribution
al (λ) = B

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whose mean and standard deviation are 120 and 40 ms, respectively. The meanvalue of delay is twice the detection threshold of the time delay [15]. The simulateddelay is sampled every 200 ms (i.e., 5 Hz).
5.3. Tasks and Procedures
Figure 13 shows a schematic of the roughness transmission experiment. The participants explored the roughness samples at the slave side through the developed
master–slave system. At the same time, they were allowed to explore the grating
scales with their bare fingers as comparison stimuli, as shown in Fig. 14. The comparison stimuli comprised of 12 grating scales and their wavelengths varied from
0.4 to 2.6 mm. The comparison stimuli include wider wavelengths than those of
the test stimuli (0.8–2.0 mm), such that the participants could choose their answers
from a wide region. These comparison stimuli were placed at intervals of 0.2 mm.
The participants selected the comparison stimulus that they felt was closest to theperceived tactile roughness through the master–slave system. They could state awavelength as their answer if they felt the delivered roughness fell between one
comparison stimulus and another (e.g., 0.95 or 1.5 mm). They touched the comparison stimuli with their right or left hand. Some of the participants simultaneously
touched the vibrator with their right middle finger and touched the comparison
stimuli with their left hand. The others touched the vibrator and comparison stimuli alternately with their right hand. At the slave side, seven types of stimuli wereused. Their wavelengths varied from 0.8 to 2.0 mm. The developed tactile transmis-

Figure 13. Roughness transmission experiment.

Figure 14. Roughness samples for comparison: surface wavelengths increases from bottom left to top
right.

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sion system can be applied to finer textures whose surface wavelengths are below
this range. The sensor can be applied to a texture whose wavelength is as long as
0.2 mm [26]. However, for fine textures, the vibratory stimuli presented to the operators are of high frequency and the outputs from the voltage amplifier attenuate.
Therefore, the surface wavelengths of the specimens were selected to be within arange in which the attenuation of the amplifier was not significant.
The trial was limited to 20 s and the stimuli were presented in random order. Eachstimulus was presented 3 times, and the answers from the second and third trials
were employed for computing the final results. In total, each participant participatedin 21 trials. Before the experiment, the participants practiced the task for as long as
they wanted. Each participant practiced for approximately 5 min. The participants
heard pink noise through headphones during the experiments in order to shut out
the sounds that the tactile stimulator generates. They were not blindfolded, so theyknew where the tactile display and comparison stimuli were.
5.4. Experimental Results
5.4.1. Evaluation of the Transmission Capability of the Local Model for Tactile
Roughness
In order to confirm that the local model transmitted the tactile roughness of samples
as well as the direct feedback method did, the results of Experiments 1 and 2 arecompared. Experiment 1 was on the direct feedback. In Experiment 2, the tactile
feedback was provided by the local model. Figure 15 and Tables 1 and 2 show theaverage and standard deviations of the surface wavelengths that the participants answered in Experiments 1 and 2. The standard deviations in Fig. 15 are calculated
among the participants. Figure 15 shows the relationships of surface wavelengthsof explored and perceived textures. The dashed line indicates the match of explored and perceived textures. The average error of explored and perceived texturesin Experiment 1 was 0.224 ± 0.104 mm in wavelength. In Experiment 2, it was
0.265 ± 0.086 mm. Although the average error for Experiment 2 seems larger thanthat for Experiment 1, there is no significant difference between the average errorsof Experiments 1 and 2 (Welch’s t-test, t0 = 0.857, p > 0.05, two-tailed). Since

Figure 15. Results of Experiments 1 and 2.

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Table 1.
Individual results in Experiment 1 (direct feedback): average ± standard deviation (mm)
λ

P1

P2

P3

P4

P5

P6

P7

P8

0.8
1.0
1.2
1.4
1.6
1.8
2.0

1.05 ± 0.35
0.85 ± 0.21
1.25 ± 0.07
1.55 ± 0.78
1.80 ± 0.28
2.05 ± 0.07
2.00 ± 0.28

0.90 ± 0.00
1.00 ± 0.00
1.20 ± 0.14
1.55 ± 0.07
1.55 ± 0.49
1.85 ± 0.07
2.05 ± 0.35

0.44 ± 0.17
0.41 ± 0.01
0.58 ± 0.25
1.30 ± 0.42
1.40 ± 0.28
2.00 ± 0.28
2.18 ± 0.03

0.65 ± 0.07
0.65 ± 0.49
0.75 ± 0.35
1.65 ± 0.21
1.03 ± 0.24
1.50 ± 0.42
1.65 ± 0.35

0.40 ± 0.00
0.95 ± 0.07
1.20 ± 0.14
1.50 ± 0.14
1.75 ± 0.07
1.35 ± 0.49
1.95 ± 0.07

0.70 ± 0.14
1.10 ± 0.14
1.20 ± 0.28
1.30 ± 0.14
1.40 ± 0.57
1.20 ± 0.00
2.00 ± 0.28

0.52 ± 0.17
0.78 ± 0.03
0.70 ± 0.14
1.70 ± 0.42
1.10 ± 0.14
1.70 ± 0.14
2.14 ± 0.08

0.30 ± 0.00
0.90 ± 0.14
0.70 ± 0.00
2.10 ± 0.14
1.50 ± 0.28
1.80 ± 0.28
2.20 ± 0.07

Table 2.
Individual results in Experiment 2 (local model): average ± standard deviation (mm)
λ

P1

P2

P3

P4

P5

P6

P7

P8

0.8
1.0
1.2
1.4
1.6
1.8
2.0

0.65 ± 0.07
1.20 ± 0.00
1.60 ± 0.00
1.60 ± 0.14
1.70 ± 0.14
1.90 ± 0.00
2.15 ± 0.07

0.80 ± 0.57
0.70 ± 0.14
1.70 ± 0.42
1.70 ± 0.14
1.40 ± 1.13
1.80 ± 0.00
2.20 ± 0.28

0.80 ± 0.28
0.70 ± 0.14
1.20 ± 0.00
1.50 ± 0.42
1.40 ± 0.00
1.60 ± 0.57
2.50 ± 0.14

0.85 ± 0.21
0.95 ± 0.49
1.60 ± 0.00
2.20 ± 0.57
1.60 ± 0.28
2.20 ± 0.00
2.50 ± 0.14

0.50 ± 0.14
0.80 ± 0.00
1.50 ± 0.42
1.50 ± 0.14
1.20 ± 0.00
1.20 ± 0.00
1.90 ± 0.14

0.55 ± 0.21
0.70 ± 0.28
1.60 ± 0.28
2.00 ± 0.28
1.90 ± 0.42
1.55 ± 0.07
2.40 ± 0.28

0.85 ± 0.07
1.15 ± 0.35
1.25 ± 0.35
1.70 ± 0.14
1.70 ± 0.14
1.50 ± 0.14
2.30 ± 0.14

0.90 ± 0.42
1.30 ± 0.70
1.90 ± 0.99
1.90 ± 0.70
1.40 ± 0.28
2.20 ± 0.00
2.60 ± 0.00

there is no significant difference between the direct feedback (Experiment 1) andlocal model (Experiment 2) in terms of the errors of values reported by the participants, the local model-based feedback transmitted tactile roughness as well as the
direct feedback did.
However, according to the introspective reports from the participants, there werequalitative differences between Experiments 1 and 2. The participants commented
that the tactile feedback of Experiments 1 and 2 were felt to be similar; however, thelocal model-based feedback was smoother than the direct feedback. This is because
the local model produced tactile stimuli based on the unique wavelength component
while the direct feedback was composed of multiple wavelength components in addition to the major one. As described in Section 2.2, the local model can produce
tactile stimuli involving multiple wavelength components. The multiple components are expected to bring the local model-based feedback qualitatively closer tothe direct feedback. Since this study does not aim at improving the quality of local
model-based feedback by involving multiple components, these qualitative differences among Experiments 1 and 2 are not discussed further.
In Fig. 15, the participants’ answer values exhibited a local maximum around
1.4 mm. The local maxima were significantly influenced by (8) (Fig. 11). As mentioned in Section 4.2.2, these local maxima were considered to be determined by
the width of distal ridges of the tactile sensor. Downsizing of the tactile sensor is
expected to avoid the local maxima of transmitted tactile roughness.

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5.4.2. Evaluation of the Validity of the Local Model for Communication Delay
In order to examine the validity of the local model toward the time delay, the resultsof Experiments 3 and 4 are compared. In both Experiments 3 and 4, a simulated
time delay was inserted into the tactile feedback. Experiment 3 was on the direct
feedback. In Experiment 4, the delay in tactile feedback was compensated by the
local model. Figure 16 and Tables 3 and 4 show the results of Experiments 3 and 4.
The average error of perceived and explored textures for Experiment 3 was 0.287 ±
0.086 mm while it was 0.255 ± 0.067 mm for Experiment 4. There is no significant

Figure 16. Results of Experiments 3 and 4.
Table 3.
Individual results in Experiment 3 (direct feedback with delay): average ± standard deviation (mm)
λ

P1

P2

P3

P4

P5

P6

P7

P8

0.8
1.0
1.2
1.4
1.6
1.8
2.0

0.38 ± 0.03
0.44 ± 0.08
0.42 ± 0.11
1.52 ± 0.25
1.68 ± 0.17
1.64 ± 0.34
1.80 ± 0.28

0.55 ± 0.07
0.80 ± 0.14
1.25 ± 0.07
1.45 ± 0.49
1.70 ± 0.14
1.50 ± 0.14
1.55 ± 0.35

0.70 ± 0.42
0.70 ± 0.42
1.10 ± 0.42
1.30 ± 0.99
1.50 ± 0.99
2.30 ± 0.14
2.10 ± 0.42

0.57 ± 0.04
0.70 ± 0.28
0.45 ± 0.07
0.95 ± 0.07
1.15 ± 0.07
1.60 ± 0.57
1.40 ± 0.14

1.10 ± 0.14
1.20 ± 0.28
1.50 ± 0.14
1.40 ± 0.28
2.00 ± 0.00
1.20 ± 0.57
2.00 ± 0.00

0.37 ± 0.04
0.70 ± 0.14
0.90 ± 0.42
1.75 ± 0.21
1.75 ± 0.35
1.45 ± 0.21
1.95 ± 0.35

0.40 ± 0.14
0.90 ± 0.42
1.20 ± 0.28
1.12 ± 0.03
1.35 ± 0.35
1.25 ± 0.07
1.90 ± 0.14

0.40 ± 0.00
0.90 ± 0.42
1.08 ± 0.45
2.20 ± 0.00
0.95 ± 0.07
1.60 ± 0.28
2.40 ± 0.00

Table 4.
Individual results in Experiment 4 (delay compensated): average ± standard deviation (mm)
λ

P1

P2

P3

P4

P5

P6

P7

P8

0.8
1.0
1.2
1.4
1.6
1.8
2.0

0.90 ± 0.42
1.10 ± 0.14
1.40 ± 0.00
1.95 ± 0.21
1.75 ± 0.35
2.05 ± 0.07
2.15 ± 0.07

0.75 ± 0.21
1.10 ± 0.14
1.50 ± 0.00
1.65 ± 0.07
1.50 ± 0.42
1.90 ± 0.00
2.05 ± 0.35

0.40 ± 0.14
0.45 ± 0.07
1.65 ± 0.21
1.75 ± 0.07
1.40 ± 0.28
2.00 ± 0.28
2.25 ± 0.07

0.65 ± 0.07
0.75 ± 0.49
1.50 ± 0.14
1.70 ± 0.14
1.00 ± 0.28
1.50 ± 0.42
2.30 ± 0.14

0.80 ± 0.28
1.05 ± 0.21
1.55 ± 0.21
1.35 ± 0.21
1.40 ± 0.57
1.25 ± 0.07
2.25 ± 0.07

0.45 ± 0.07
0.90 ± 0.28
1.55 ± 0.07
2.05 ± 0.07
1.40 ± 0.14
1.60 ± 0.14
2.25 ± 0.07

0.45 ± 0.07
0.85 ± 0.07
1.55 ± 0.07
1.60 ± 0.00
1.75 ± 0.07
1.35 ± 0.49
2.15 ± 0.07

0.55 ± 0.21
1.25 ± 0.07
1.65 ± 0.07
1.60 ± 0.57
1.20 ± 0.00
1.65 ± 0.35
2.20 ± 0.14

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difference between the errors of Experiments 3 and 4 (Welch’s t-test, t0 = 0.826,
p > 0.05, two-tailed). In terms of the error values reported by the participants,
no significant difference was observed between the direct feedback and the local
model-based feedback when the time delay was simulated.
According to the introspective reports, in terms of the perceived time delays, differences between Experiments 3 and 4 were reported. In the case of direct feedback(Experiment 3), all participants noticed the delay of tactile feedback. They reported
that the tactile feedback was delayed compared with their hand movements. On theother hand, in the case of the local model (Experiment 4), no participants reported
the perceived delay. Thus, by applying the local model, the perceived delay between
the exploratory hand movements and tactile feedback was canceled.
The introspective reports also indicated that there were some differences in themanner that the participants conducted the tasks between Experiments 3 and 4.
When the delay was compensated (Experiment 4), the participants reported thatthey evaluated the textures on the basis of perceived intensity and vibratory frequency of tactile feedback. For instance, they evaluated the fineness of texturesfrom the perceived vibratory frequency. These evaluation criteria for rough textureson the basis of perceived intensity and frequency of cutaneous stimuli were consistent with the reports in the experiments with no simulated delay (Experiments 1
and 2). Also, these criteria are consistent with the perception of rough textures(e.g., Refs [21, 27]). On the other hand, in the case of direct feedback with time delay (Experiment 3), the participants reported that they did not rely on the temporal
information and mainly on the perceived intensity of tactile feedback because thefeedback was apparently delayed. Thus, due to the communication time delay, theparticipants altered their criteria for roughness perception.
In case that there was a perceived delay in Experiment 3, some participants reported that they attempted move-and-wait actions in which they waited until thetactile feedback was given before they moved their hand. In the reciprocating hand
movements, they stopped their hand for a while at the point where the moving
direction turned. Four of eight participants were observed to have taken these actions. Therefore, due to the perceived time delays, some participants employed an
exploratory strategy that had not been observed when the perceived delay did notexist.
Summarizing the above analyses and introspective reports, concerning the tasks
to select the textures that were felt to be closest, no significant difference in theperformance was observed between the direct feedback and local model-based feedback when the simulated delay was inserted. If the communication time delay islarger than the delay simulated in the experiments, the local model is expected tohave the merit of improving the performance of texture recognition. However, as
to the clear advantages of the local model, when the local model was applied to
the feedback, the time delay perceived by the participants was eliminated. In addition, due to the noticeable time delay, the participants took evaluation criteria or
exploratory movements that are different from those for the non-delayed condition.

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When the local model was applied, these countermeasures for noticeable time delay were not necessary. Collectively considering these results, the local model was
effective for the operators to explore the remote textures without being distracted
by the perceived time delay and be able to carry out tactile exploration with theevaluation criteria that they usually employ in the no-delay condition.
6. Conclusions
This study addressed tactile transmissions in a manner applicable to systems with
communication time delays. The use of the local model for textures placed in aremote site was proposed in order to compensate for the delayed tactile feedback.
The local model can eliminate the temporal gap between the operators’ hand movements and the corresponding tactile feedback. However, so far, the local model has
yet to been extended to tactile feedback. The objectives of this study were to develop the local model for tactile feedback and to test its effectiveness for the tactile
transmission systems.
The local model was designed such that the model replicates the amplitude andvibratory frequency of cutaneous deformations similar to those that occur whena human finger explores rough textures. The model included physical propertiesof textures, which were surface wavelengths of textures. The wavelengths were
estimated and updated by the tactile sensor installed on the slave-side system inreal-time. In order to confirm the effectiveness of the developed local model andtactile transmission system, two-phase experiments were performed. The objectiveof the first experiment was to confirm that the local model transmits the tactile
roughness of textures. The objectives of the second experiment were to confirm
that the developed tactile transmission system eliminates the time delay perceived
by the operators and transmits the roughness better than the system whose timedelay was not compensated for.
First, the experiments were conducted for examining whether the developed system transfers the roughness of textures. In the experiments, the participants exploredroughness samples placed at the slave side through the master–slave system. The
participants selected the roughness samples that were felt to be closest to the roughness perceived through the system. The experimental results were compared withthose of direct feedback in which the outputs of the tactile sensor were directly input
to the tactile display. As a result of the comparison, no significant difference wasobserved in the participants’ answers between the local model-based feedback anddirect feedback. Thus, it was confirmed that the developed local model transmitted
the tactile roughness of sample textures as well as the direct feedback did.
Second, the experiments were conducted to examine the effectiveness of the local
model toward the communication time delay. In the experiments, a simulated timedelay was inserted into the tactile transmission system. The experimental results
were compared between the local model-based feedback and the direct feedbackof which time delay was not compensated for. As a result of comparison, in terms

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of the errors of explored and perceived textures, no significant difference was observed between both the feedback methods. However, according to the introspective
reports of the participants, the perceived time delay was eliminated when the local
model was applied. Also, when the local model was applied, the participants did
not need to employ the exploratory strategies or evaluation criteria that were taken
for delayed tactile feedback.
Summarizing the effects of the developed local model, the model transmits theperceived roughness of textures as well as the direct feedback. As for the advantages, the local model eliminates the time delay perceived by the operators andenables them to explore the remote textures without requiring them to employ
countermeasures for delayed tactile feedback, such as move-and-wait actions or
the change of evaluation criteria for tactile roughness. Owing to the above reasons,the usage of the local model is recommended for tactile transmission systems withsignificant communication time delay.
Acknowledgements
This work was partially supported by a grant from the Ministry of Internal Affairs
and Communications SCOPE (082102006), Grant-in-Aid for Scientific Research B(19360120) and Grant-in-Aid for JSPS Fellows (191804).
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About the Authors
Shogo Okamoto received the BS degree in Engineering from Kobe University, in
2005, and the MS and PhD degrees in Information Sciences, in 2007 and 2010,
from the Graduate School of Information Sciences, Tohoku University. He wasa JSPS Research Fellow for Young Scientists during 2007–2010. Since 2010, he
has been an Assistant Professor in the Mechanical Department of Nagoya University. His research interests include haptics and man–machine interfaces. He
received the Dean’s Award, GSIS, Tohoku University, in 2007 and 2010, the Best
Poster Award at WorldHaptics’07, the IEEE RAS Japan Chapter Young Award(ICRA’08) in 2008, the Award for the Best Hands on Demo at EuroHaptics’08, JSME ROBOMEC
Award in 2007, 2008 and 2009, and the Young Investigation Excellence Award from the Robotics
Society of Japan in 2009. He was a Co-Chair of the IEEE RAS SAB in 2009. He is a Member of
the IEEE, Robotics Society of Japan, Japanese Society of Mechanical Engineers and Virtual Reality
Society of Japan.
Masashi Konyo received the BS, MS and PhD degrees in Engineering from Kobe
University, in 1999, 2001 and 2004, respectively. In 2004, he was a Postdoctoral
Fellow at the BioRobtics Laboratory, Keio University. From 2005 to 2009, he wasan Assistant Professor at the Graduate School of Information Sciences, Tohoku
University. Since 2009, he has been an Associate Professor at the Graduate Schoolof Information Sciences, Tohoku University. His current research interests include
haptic perception, tactile interfaces and human cooperative robotics. He received
the Best Paper Award from the Virtual Reality Society of Japan, in 2002 and 2007,
the Best Poster Award at WorldHaptics’07, and the Best Hands on Demo Award at EuroHaptics’08.
Takashi Maeno received the BS and MS degrees in Mechanical Engineering fromthe Tokyo Institute of Technology, Tokyo, Japan, in 1984 and 1986, respectively.
From 1986 to 1995, he worked for Canon, Inc., Tokyo, Japan. He received his PhD
degree in Mechanical Engineering from the Tokyo Institute of Technology, Tokyo,
Japan, in 1993. From 1995 to 2008, he was with the Department of MechanicalEngineering at Keio University, Yokohama, Japan. Since 2008, he was with the
Graduate School of System Design and Management, Keio University as a Professor. His research interests include human–machine systems, social systems andeducation.
Satoshi Tadokoro received the BE degree in Precision Machinery Engineering, in
1982, the ME degree, in 1984, from the University of Tokyo, and the DE degree,
in 1991, from Kobe University. He was a Professor of the Graduate School of Information Sciences, Tohoku University, since 2005. He was a Project Leader ofthe MEXT DDT Project on Rescue Robotics, in 2002–2007, having the contribution of more than 100 professors nationwide. He established RoboCupRescue, in
1999, TC on Rescue Engineering of SICE, in 2000 (the first chair), IEEE Robotics
and Automation Society TC on Safety, Security and Rescue Robotics, in 2001 (thefirst chair), and International Rescue System Institute (IRS), in 2002. He was IEEE RAS Japan Chapter Chair, in 2003–2005. He is at present President of the IRS, Trustee of The RoboCup Federation,
Chair of the JSME Robotics Mechatronics Division, and IEEE Robotics and Automation Society AdCom member. He received The Robot Award 2008, FDMA Commissioner Highest Award in 2008,
JSME Funai Award in 2007, Best Book Author Award from the AEM Society in 2006, RMD Academic Achievement Award in 2005, etc. His research interests include rescue robotics, virtual reality
and new actuators. He is an IEEE Fellow and JSME Fellow.