2014 IEEE/RSJ International Conference onIntelligent Robots and Systems (IROS 2014)
September 14-18, 2014, Chicago, IL, USA

Development of an Upper Limb Exoskeleton Powered via PneumaticElectric Hybrid Actuators with Bowden Cable
Tomoyuki Noda, Tatsuya Teramae, Barkan Ugurlu, and Jun Morimoto
Abstract— In this paper, we introduce our ongoing work on
the development of an upper body exoskeleton robot, driven bya pneumatic-electric hybrid actuation system. Since the limb ofan exoskeleton robot needs to have small inertia to achieve
agility and safety, using a heavy actuator is not preferable.
Furthermore, we need to use backdrivable actuators that cangenerate sufficiently large torques to support user movements.
These two requirements may seem contradictory. In order tocope with this development problem, we use a hybrid actuation
system composed of Pneumatic Artificial Muscles (PAMs) andsmall-size electromagnetic motors. Although we and otherresearch groups have already presented the advantage of thehybrid actuation system, we newly propose the usage of Bowden
cable in a hybrid actuator to transmit the force generated by the
PAMs to joints of our exoskeleton robot so that we can design
a compact upper limb with small inertia. In addition, small size
electric motors are mechanically connected to joints in order tocompensate uncertainty generated by the PAM dynamics andthe Bowden cable. We demonstrate that the proposed joint isbackdrivable with the capability of large torque generation for
the gravity compensation task both in One-DOF system witha dummy weight and right arm of the upper body exoskeleton
with a mannequin arm. We also show the right arm exoskeleton
can be moved using a torque input, extracted from sensory
information via a goniometer.

I. INTRODUCTION
Development of exoskeleton robots is an important research topic [1], [2] because such systems can be used toassist movements in supporting elderly people and can bea useful device for rehabilitation to help recovery of stroke
patients or spinal cord injury patients [3]–[7].
Since an exoskeleton robot needs to simultaneously support the user’s body weight and the weight of the robot
itself, the limbs of the exoskeleton robot need to have small
inertia but actuators need to be sufficiently strong. To satisfy
these requirements, using Bowden cables to transmit forces
generated by actuators, where the actuators are located apartfrom limbs of a robot, were proposed in several studies [8]–[13].
In this paper, we introduce our exoskeleton development
for upper limbs with Bowden cables that transmit forces generated by actuators to joints of the robot. As the actuator system, we use pneumatic-electric hybrid actuators that we havebeen developing in our previous studies [14]–[17] but with
PAMs force/displacement transmitted via Bowden cables inthis paper. Pneumatic-Electric Hybrid Actuator (PEHA) is
composed of a pair of Pneumatic Artificial Muscles (PAMs)
and a small-size electromagnetic motor, where the PAMs can
The authors are with Dept. of Brain Robot Interface, ATR Computational
Neuroscience Labs, 2-2-2 Hikaridai, Seikacho, Souraku-gun, Kyoto, Japan,
619-0288 E-mail: {t noda,t-teramae,barkanu,xmorimo}@atr.jp

978-1-4799-6934-0/14/$31.00 ©2014 IEEE

be used to generate large and low frequency forces, andthe small-size electric motor can generate additional small
but high frequency torque. Since the developed exoskeleton
robot has force sensors at the tip of each PAM, joint torques
can be measured. In this study, we also introduce a modeland a calibration method to explicitly consider force loss
and wire extension when the force generated by the PAM
is transmitted via Bowden cable while conventional studies
neglected these significant problems by assuming that inner
wires are infinitely stiff.
We demonstrate the superiority of the joint powered via
PEHA with Bowden cables using the implemented controller: backdrivability, and possessing the capability of large
torque generation. The practical calibration is also described.
For proof of concept evaluation, we use the prototyped
exoskeleton arm to compensate the gravitational force. (Seethe multimedia attachment.) To start with, we show OneDOF testing system that can be moved up and down by
a finger. Similarly, the exoskeleton right arm demonstrates
sustaining the mannequin arm with a passive elbow joint at
different postures using exoskeleton’s gravity compensatedshoulder and active elbow joints.
The rest of the paper organized as follows. Section
II describes the hardware design concept using the basic
One-DOF PEHA architecture with/without Bowden Cable
transmission system and discusses control problems withcomparison of conventional researches. Section III describes
mechatronics design, especially related to PAMs with Bowden Cable force transmission system. The upper body arm
exoskeleton is prototyped in this section. Section IV considers a torque generation model, concerning the PAM withcompensation for friction in Bowden cable to provide atorque controller which is calibrated in the system identification phase. Section V validates our design concept, using
the upper right limb exoskeleton, both for a gravity compensation as passive movement and a torque input converted
using a sensory information via subject’s elbow goniometeras active movement. See also the multimedia attachment of
this paper, demonstrating these tasks visually. We finallyconclude in Section VI.
II. C ONCEPT AND DESIGN POLICY
A. Actuator design and Torque Control Problem
1) PEHA One-DOF system: Fig. 1 shows the basic designof the PEHA One-DOF system. The paired PAMs generate
antagonistic contraction force, transmitted via wires. The
total torque,τ , is the sum of torques generated by PAMs

3573

(τP AM ) and a motor torque (τmotor ).
τ = τP AM s +τmotor = (fP AM 1 − fP AM 2 ) r0 +τmotor , (1)where fP AM 1 is upper PAM contraction force, and fP AM 2
is lower PAM contraction force. r0 is the pulley radius which
can be a design variable using an ellipse pulley, but this paper
considers it constant. The motor provides additional torque,τmotor , combined with PAM torque.
This paper mainly considers τP AM because controllingτmotor usually has an easier solution in motor driver. As wedeveloped in our previous studies [14]–[17], τP AM covers
a large torque for gravity compensation for low frequency
torque generation. The motor covers high frequency torquebut it can be small with low reduction gear that is aimed topossess backdrivability. Similar approaches have already proposed in [18] as air cylinder with bowl screwed motor, andare conceptually known as Distributed Macro-Mini(DM 2 )[19].
2) PEHA combined with Bowden Cable: Fig. 2 shows
the proposed PEHA One-DOF system with Bowden cables.
The PAM forces transmitted via Bowden cables consistingof an inner wire and a flexible outer housing. The meritof this approach is to reduce weight and to save spaceon the robot’s skeletal structure. Compared to DM 2 [19],while mini actuators (motors) are distributed, the macro
actuators (PAMs) are not distributed but can be centralized
in a controller unit. The merit is reducing the required spacearound the joint. The PAM bases, drawn as the left sideedge in Fig. 1, limit the extra joint to be implemented only
outside from the PAM base to the joint. The implementationin Fig. 2 moderates the limitation because the outer edge
can be allocate closer to the joint. However, practical useof this system on a torque controller requires total systemcalibration, since the inner wire may be extended by a creep
in use of long term duration. Additionally, the length can bechanged if the wire holder attachment slips when large force
is applied to the joint.
Fig. 3 (a) shows One-DOF system to test the PEHA with
Bowden cable, where the PAM is located apart from the joint
system (lower of the frame in the picture) and contraction
force is transmitted through an inner cable and an outerflexible housing. Fig. 3 (b) shows the block diagram of theinterfaces.
Wire(Tendon)
Load cell
PAM

f PAM
PulleyM

Motor

Control Unit

Load cellmotorFig. 1.
One-DOF PEHA System: Two PAMs generate antagonistic
contraction force transmitted to the joint by the wires and the pulley. Motortorque is applied to the same joint.

Bowden Cable(Inner)(Outer)Contraction rate

f

Link

PAM

PulleyMechanical velt
or gear

M

PAM
Motor

motor

Fig. 2. One-DOF PEHA System with Bowden Cable: The two PAMs arelocated apart from the joint and link, and generate antagonistic contraction
force transmitted to the joint by Bowden Cable and the pulley. In the rangeof the outer, the inner cable can be bendable/placed on curved line. Motortorque is also applied to the same joint.

(a)Bowden Cable(Outer) (Inner)

Weight(5.0kg)

PAM (used forupper angle limit)(b)

Air Tank(0.8MPa)

PAM( 40)(b)
AD/DA
UDP(RTnet)
Realtime PC
MFB(Debian 7+Xenomai)

EnergyValve

Torque

PAM1

MD+Motor +LC AMPLCEC

Quadrature encoded pulse

Fig. 3. Structure of essential PEHA One-DOF with Bowden Cable: (a)The PAM contraction force moves the One-DOF arm system to upperdirection. The PAM contraction force is transmitted through Bowden Cableand connected to pulley at joint. In the picture, 5kg weight hung on the armand PAM contraction force holds the weight (equilibrium to the gravity).
The lower PAM was used as safety limit (instead of mechanical limit). (b)The block diagram of the system architecture.

III. M ECHATRONICS DESIGNThis section introduces the hardware prototype of a rightarm exoskeleton.
3) Right Arm kinematic structure: Fig. 4 (a) shows the
kinematic design of the exoskeleton for an upper right limb.
There are three active flexion-extension (FE) joint at theshoulder, the elbow, and the wrist, powered via PEHA with
Bowden cable described in Section II except for the wrist.
WFE is powered only via antagonistic pair of PAMs to makethe robot tip light. (See Fig. 4 (a) caption for abbreviations.)
Each link length and surrounded space of user arm wasdefined from the database provided in [20] (1991–1992),
using average and covariance statistics.
Fig. 4 (b) shows the 3D CAD assembly of the exoskeletonarm. Fig. 4 (c) enlarges the shoulder structure. There are two
Bowden cables connected to antagonistically paired PAMs.
A similar architecture is implemented at the elbow and thewrist.
Based on the task holding a typical user’s arm itself(suppose the mannequin in the picture as the simulated
weight) and 5kg weight at (SF E, EF E) = (0, 0) and10[kg] weight at (SF E, EF E) = (−π/2, 0) with both
hand, PAM diameters and pulley radii are chosen (40mm3574

for upper direction and 10mm for lower direction). The peaktorques are 100 Nm at the shoulder and 76 Nm at the elbow
without the Bowden cable coefficient. Nominal maximum
forces of a 40mm PAM is 5000 N but limited up to 2000 N
in this paper (see later subsection III-B). Nominal maximum
force of 10mm PAM is 630 N as the antagonistic muscle
for SFE and EFE. The motor (Maxon inc., EC–4pole 30
100W with low reduction gear (14:1) and a built-in opticalquadrature encoder) torque is additionally delivered throughthe bevel gear (2:5). The nominal torque is approximately 2.5
Nm (maximum continuous without the gear coefficient). For
WFE, 10mm PAMs are used for both directions without any
motor to make the arm tip light, as already discussed above.
Note that although the joint is backdrivable with small inertia
thanks to the low gear reduction (35:1), the motor torque is
not enough to sustain user’s arm at all. PAM torque could
be sufficiently large but its response is slow and prone tomodeling errors. The combination of both PAM and motor
torques is reasonable for the target tasks concerning gravity
compensation for the robot and user arm.
Fig. 5 (a) shows the prototyped right arm and the overview
of the case that the user arm is attached to the upper body
exoskeleton arm. To connect the exoskeleton to the user’sarm, the belts at the link both in Elbow–Wrist and Shoulder–
Elbow can be installed. The total weight from shoulder to
tip was within 5 kg, excluding controller units.
A. Controller Unit
Fig. 5 (b) and Fig. 5 (c) show the controller unit whichis mechanically independent from exoskeleton arm structure,but connected via Bowden cable and electric cables. Fig. 5(b) shows the mechanism to prevent loosening of inner wires
by a passive movement at the pulley side. As the shoulder
angle decreases, the inner wire pushes down itself toward
the PAM side. The PAM is always required to control not toloosen wire, otherwise the wire may be buckled and comeoff from the pulley nest. The illustrated structure to prevent
loosening of wire to below, avoiding the interference tothe PAM edge. To our best knowledge, this problem was
unconsidered in conventional research. Fig. 5 (c) illustrates
the alignment of PAMs and valves. The multifunction board(MFB) and load cell amplifier are also located beneath motor
drivers. MFB has 16bit 16ch AD, 16bit 8ch DA, 8bit IO, and8ch of quadrature decoder for joint encoder. All the I/O pinsof the system goes through the MFB communicating withrealtime system (Xenomai PC) using Ethernet (RTnet UDP
protcol, see also Fig. 3 (b))
B. Maximum Allowable PAM Force Determination andsafetyHaving very large power-to-weight ratios, the specific
PAMs utilized in our exoskeleton arm are capable of producing forces up to 5000 [N]. On the contrary, Bowden cables orwire stoppers may not be operable when they are subject tosuch large forces. Keeping this in mind, a series of tests are
conducted in which a single muscle is placed to an aluminum
frame and mechanically connected to a fixed pulley via a(a)Sholder

R-SFE

R-EFE

R-WR
R-WAA
(R-WFE)(c) Inner

Outer

Elbow

R-WR

Wrist
Palm

R-SFE
MotorsR-WFE

Passive Joint

R-EFE (b)Active Joint

Fig. 4.
The kinematic structure and the 3D CAD assembly of rightarm exoskeleton prototype: (a) Kinematic model: Each abbreviation is[FE: Flexion Extension]; [AA: Adduction Abduction]; [R: Rotation]; [S:Shoulder]; [E: Elbow]; and [W: Wrist]. Before hyphenation, [R: Right] and[L: Left] indicate the side respect to user’s view point. For example, R-SFE
denotes the right shoulder Flexion Extension. (b) Agonist/Antagonist innercables driven by two differential PAMs are connected to the joint pulley.
(c)

(a)

(b)

Outer

Motor drivers (2ch)

Inner

Load cell
Amps

Load cells
Belt

PAM( 40) PAM( 10)Wire stoppers

Pressure servo valve (6ch)

Fig. 5. Prototyped exoskeleton right arm and the controller unit: (a) The
arm exoskeleton are attached to a human right arm. (b) The antagonistic
pair of inner cables comes from the pulley side throughout the outer. The
diameters, ϕ, are 40mm for agonist and 10mm for antagonist directions ofshoulder and elbow joint. 10mm for agonist/antagonist direction of wrist. (c)Controller unit includes PAMs and pneumatic valves, located apart from theexoskeleton’s kinematics, are to be mounted on a wheel chair or a weightbearing system. Motor drivers and load cell amplifier are also mounted in
the control unit.

Bowden cable and a stopper. Fig.6 displays this setup andits elements. The diameter of the inner wire was 1.7 mm.
Subsequently, the muscle is activated with gradually increasing ramp inputs in a way to determine the force value
at which the cable or stopper breaks. Fig. 7 shows load cellmeasurements from 4 consecutive experiments with different
initial tension values. As may be observed in Fig. 7, thesystem is safely operable until 2250 [N]. Beyond this range,
inner bowden cable (not the stopper) is broken. Considering
an 11% of safety margin, it is determined that 2000 [N]is the maximum allowable PAM force; the controllers areprogrammed in a way not to produce forces larger than thislimit. This limitation may be increased by replacing thicker
Bowden cable, and we can also use thinner cables to limit
force mechanically.
IV. M ETHOD : T ORQUE BASED CONTROLLER
The torque controller for a joint driven by multiple torquesources has to solve the redundant solution. In this paper, weconsider only the PAM force control toward upper direction,e.g., supposed; antagonistic muscle desired force is constant3575

Load cell

PAM

LC AMP

A. Compensating Bowden Cable Friction

Valve

Bowden cables have considerable force transmission
losses. We expand the friction coefficient model of static[22] to continuous. The static model can be written as

MFB(AD/DA)
UDP

Wire
Wire stopper
Pulley(Fixed)

Fig. 6.
system.

F = F+ exp (µψ) (0 < v),

PC
Wire

Wire stopper experiment and block diagram of the experiment

Applied Force [N]

2250
1500
750
0
-750
t

2t

3t
Scaled Time

4t

5t

Fig. 7. Wire stopper evaluation: To evaluate the maximum allowable force
to inner cable and the stopper, we applied tension using one PAM graduallyfrom 4 different initial length.

and compliance of joint is subject to this constraint. For
example, movement of the arm upper direction, desired
antagonistic muscle force (fP∗ AM 2 ) is controlled as constant;the simpler case 0[N]. In this case, the torque control
problem becomes one PAM pressure control.
This simplified problem is still non-trivial. Primarily, PAM
force is highly nonlinear [21] caused by its contraction
rate. What is more, the operation in large force causes
considerable cable extension between the encoder and PAM,
resulting in the estimation error of PAM contraction rate.
Secondly, the conventional researches [8]–[13] does not
explicitly consider force losses between outer and inner in
Bowden cable transmission system.
The force generation principle of PAM is that the bladderradial expansion causes the thrust contraction of wire surrounded or embedded over the rubber spirally. This provides
very large contraction force but only at one direction. This
paper considers 2 model combination consists from parametric and non-parametric model for practical and instantcalibration instead of increasing number of parameter. The
controller input is desired force:()−1p∗ = g −1 (FP∗ AM ; D, ε) + f FP∗ AM , ε; gerr(D) , (2)∗where FP∗ AM is desired force of PAM (FP∗ AM = τr0 ), f (·)−1
is non parametric model for the model error of gerr(D),p∗ is the desired pressure as valve input, g(·) is PAM force
parametric model from blabber pressure to muscle force, andg −1 (·) is inverse model of them with given contraction rate εand calibration dataset D. The asterisk (∗ ) indicates desired
value.
Although this model is provided at equilibrium point toignore pressure dynamics, we include the Bowden cablehysteresis to consider its velocity.

(3)

where F+ is the applied force and F is the force afterloss. If the movement direction alters, the force loss relation
is opposite. Switching two models generate discontinuity
at zero velocity. We expand this model to a continuous
definition using a sigmoid function σ (v; α) = 1−exp1(−αv)
F ′∗ = q(F ∗ ; µ, α) = F ∗ [1 − (2σ (v; α) − 1) exp (µψ)] ,(4)
where F ′∗ is the compensated controller input for PAM force
discussed above, v is velocity of the wire estimated from ajoint encoder, and α is gain of sigmoid function. Increasingalpha decreases non-sensitve range to angle velocity. The
compensated torque is same as original torque if angle
velocity is zero.
B. PAM force model
The PAM force generation is process of pressure tocontraction force conversion. The basic model-based force
generation is described in literature [23], [24] as a 2ndorder polynominal function at the equilibrium point. The
parameters are provided conversion from PAM diameter andthe angle of the embedded spiral-wire at the atmospherepressure. In large force operations, we have to consider also
inner wire extension and mechanical deformation. Here we
consider Tendon-spring model for original PEHA system
proposed in our previous work [15]. Consequently, the parameters are five valuables (λ = (a, b, c, k, ε0 )); (a, b, c) for
the PAM nonlinearity and (k, ε0 ) for inherent stiffness andinitial slack of inner the wire. These parameters are calibrated
in the system identification phase.
Thanks to Tendon-spring model, the inverse model canestimate dataset distribution with small number of parameter.
However, there would be still remaining error that affectthe torque controller. To our best knowledge, there is no
theoretical model to support these errors and successful
conventional approaches using heuristic non-linear model[21] or Neural Network [25]. Increasing the number offunction parameters may require larger variational calibration
dataset and may cause over fitting. We, thus, insist on
using the theoretical parametric model to avoid the risk.
Instead, we consider the error fitted by Sparse Pseudo-input
Gaussian Process (SPGPs) [26]. Although Gaussian Process
is a strong framework with Bayesian probabilistic model inbehind to fit noisy sensor data, the computational cost is high;O (N ) for the predictive mean estimation. After several precomputation, SPGPs can reduces this cost to O (M ) using
sparse M pairs of pseudo-inputs x̄m and pseudo-target f¯m(M ≪ N )∑αm k(x; x̄m , f¯m ),(5)f (x) =

3576

m

where x is two dimensioned single point input( of current)
state x = (ε, F ). Note that the pseudo pair of x̄m , f¯m corresponds to pseudo-input and pseudo pressure error. Hyperparameters are decided by Automatic Relevance Determination (ARD) [26].
V. E XPERIMENTS
A. PAM calibration

Force [N]Fig. 8 shows the instance of the calibration dataset used forthe upper PAM (40 mm) at EFE. 5 parameters of the PAM
parametric inverse model are optimized from this experiment
within the dataset respect to the error of the pressure using
Levenberg–Marquardt algorithm. The forces are measured bywell calibrated load cells. The best parameter was selectedamong 300 trials from random initialized parameter. The restof the error is modeled by a nonparametric way using SPGPs.

and the operator can move the link very easily because
gravity force was successfully compensated by the proposed
torque controller.
In the multimedia attachment, we also demonstrate that the
elbow joint via a replacement torque signal that is acquired
using inverse dynamics and goniometer. We sampled thesubject’s elbow joint using the attached goniometer by realtime process, sampled at 200 Hz. The estimated elbow
joint torque, using a standard inverse dynamics with Butterworth low pass filter, was sent to the torque controllerof exoskeleton’s elbow as an input signal. The shoulder
was controlled in the same way as done in the first task,e.g., gravity compensation of the total arm COM (using
DOF joint kinematic model). In both cases, the shoulder andthe elbow joint are controlled. The wrist of two PAMs arecontrolled by a constant pressure (0.25[Mpa]), resulting in
keeping the joint (WFE) with inherent compliance. In bothdemonstrations, the passive rotation joint of the wrist (WR)was restrained by a mechanical clamp.

Upper

(a)

Upper

(b)

Fig. 8. The data set example for PAM calibration: triangle wave has sent
to upper-body arm and the angle and thef load cell reading were preserved
at 250Hz sampling.

(c)
Joint

link

B. Torque based control: Gravity compensation and sensory
based torque input
To compensate gravity force, the joint torque that corresponds to vertical assistive force against gravity is generatedas:τ = JT Gr+h ,(6)where J is the COM Jacobian matrix, Gr+h is the desired
virtual forces, and τ is the desired torque at each joint of
the exoskeleton robot. We validate the feed-forward torquecontroller for this task if the joint angle is kept when theoperator moves and releases the system. Note that we did
not use any position feedback.
Fig. 10 (a) shows the experiment setup for upper limb
exoskeleton prototype. The mannequin arm with a freeelbow joint was equipped on the exoskeleton arm, fasten
by stretchable belts, and a flexible electro-goniometer (Biometrics Ltd.) was attached to the subject elbow joint formeasurement.
Fig. 10 (b) demonstrates the gravity compensation task
both for the robot arm and the mannequin arm using
shoulder/elbow flexion/extension. The snapshots show theexoskeleton/mannequin arm can be held at different postures,

5 kg weightFig. 9. Angle trajectories in gravity compensation task using One-DOF
testing system: (a) 2.5kg weight gravity compensation task (b) 5.0kg weightgravity compensation task (c) Snapshots from 5.0 kg gravity compensation
task

(a)

(b)

Band
Goniometer

Mannequin arm

Fig. 10. Experimental setup for the arm exoskeleton: (a) A mannequin arm
with passive elbow joint is attached to the upper limbs. The goniometer is
attached to the subject elbow to conduct torque feedback control. (b) Two
pictures demonstrate the exoskeleton with mannequin arm weight can besustained at two different postures.

VI. C ONCLUSIONSIn this paper, the challenges related to the developmentof the upper limb exoskeleton arm powered via pneumatic-

3577

electric hybrid actuation with Bowden cables are described.
Large torques are transmitted via Bowden cable from offboard Pneumatic Artificial Muscles (PAMs), combined withthe small torque of the low-geared motor that mechanicallycoupled within the joint. This approach can reduce theexoskeleton arm weight and the required space around thejoint to address a multi degree of freedom design. The active
joints of the prototyped exoskeleton arm are backdrivablewith the capability of large torque generation using safe
energy sources (pneumatic and small electric current). To
control the joint using a torque controller, we considered a
PAM force model with Tendon-spring under the equilibrium
assumption that is combined with a Gaussian process. In thecalibration phase, the parameters are calibrated from shortterm experiments to control PAM pressure based on torqueinput.
The gravity compensation task using both the One-DOF
testing system and the prototyped exoskeleton arm demonstrated the backdrivable joint with large torque generation.
We also show that the active movement using an electrogoniometer, the estimated joint torque of elbow angle with thestandard inverse model was used as an input for the torquecontroller to move the exoskeleton elbow joint equippedwith a mannequin arm. While further study is feasible toinvestigate the torque bandwidth of the hybrid approach,the implementation provided practical performance for theassistive applications.
As a future work, we consider developing the full upper
body exoskeleton based on this mechanical design and thecontrollers.
VII. ACKNOWLEDGMENT
We thank Nao Nakano, Jun-ichiro Furukawa, and Akihide Inano for hardware maintenances and helping our
experiments. This research was supported by a contract
with the Ministry of Internal Affairs and Communications
entitled, ”Novel and innovative R&D making use of brain
structures”, and SRPBS by the MEXT. T.N. was partially
supported by JSPS KAKENHI (#24700203). J.M. was partially supported by MEXT KAKENHI (#23120004). A partof this research was supported by Strategic International
Cooperative Program, JST, JSPS and MIZS: Japan-Slovenia
research Cooperative Program, and MIC-SCOPE.
R EFERENCES[1] S. Jacobsen, “On the Development of XOS, a Powerful Exoskeletal
Robot,” in 2007 IEEE/RSJ International Conference on Intelligent
Robots and Systems, Plenary Talk, 2007.
[2] H. Kazerooni, A. Chu, and R. Steger, “That Which Does Not Stabilize,
Will Only Make Us Stronger,” International Journal of Robotics
Research, vol. 26, no. 1, pp. 75–89, 2007.
[3] K. Suzuki, M. G. Kawamoto, H. Hasegarwa, and Y. Sankai, “Intensionbased walking support for paraplegia patients with Robot Suit HAL,”
Advanced Robotics, vol. 21, no. 12, pp. 1441–1469, 2007.
[4] S. K. Au, P. Dilworth, and H. Herr, “An ankle-foot emulation system
for the study of human walking biomechanics,” in IEEE International
Conference on Robotics and Automation, 2006, pp. 2939–2945.
[5] H. Kobayashi, A. Takamitsu, and T. Hashimoto, “Muscle Suit Development and Factory Application,” International Journal of Automation
Technology, vol. 3, no. 6, pp. 709–715, 2009.

[6] G. Yamamoto and S. Toyama, “Development of Wearable-Agri-RobotMechanism for Agricultural Work,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009, pp. 5801–5806.
[7] T. Kagawa and Y. Uno, “Gait pattern generation for a power-assist
device of paraplegic gait,” in The 18th IEEE International Symposium
on Robot and Human Interactive Communication, 2009, pp. 633–638.
[8] R. Ham, T. Sugar, B. Vanderborght, K. Hollander, and D. Lefeber,“Compliant actuator designs,” IEEE Robotics and Automation Magazine, vol. 16, no. 3, pp. 81–94.
[9] J. F. Veneman, “A Series Elastic- and Bowden-Cable-Based Actuation
System for Use as Torque Actuator in Exoskeleton-Type Robots,” TheInternational Journal of Robotics Research, vol. 25, no. 3, pp. 261–
281, Mar. 2006.
[10] H. Vallery, J. Veneman, E. van Asseldonk, R. Ekkelenkamp, M. Buss,and H. van Der Kooij, “Compliant actuation of rehabilitation robots,”
IEEE Robotics and Automation Magazine, vol. 15, no. 3, pp. 60–69.
[11] D. Caldwel, N. Tsagarakis, S. Kousidou, N. Costa, and I. Sarakoglou,“”soft” exoskeletons for upper and lower body rehabilitation-design,
control and testing,” International Journal of Humanoid Robotics,
vol. 04, no. 03, pp. 549–573, 2007.
[12] J. F. Veneman, R. Kruidhof, E. E. G. Hekman, R. Ekkelenkamp,
E. H. F. Van Asseldonk, and H. van der Kooij, “Design and Evaluationof the LOPES Exoskeleton Robot for Interactive Gait Rehabilitation,”
IEEE Trans. Neural Syst. Rehabil. Eng., vol. 15, no. 3, pp. 379–386.
[13] K. Kiguchi, “Active Exoskeletons for Upper-limb Motion Assist,” pp.
1–18, Oct. 2007.
[14] S.-H. Hyon, J. Morimoto, T. Matsubara, T. Noda, and M. Kawato,“Xor: Hybrid drive exoskeleton robot that can balance,” in Intelligent
Robots and Systems (IROS), 2011 IEEE/RSJ International Conference
on. IEEE, 2011, pp. 3975–3981.
[15] T. Noda, N. Sugimoto, J. Furukawa, M. Sato, S. Hyon, and J. Morimoto, “Brain-controlled exoskeleton robot for bmi rehabilitation,”
Proceedings of IEEE-RAS International Conference on Humanoids(Humanoids), 2012.
[16] T. Matsubara, T. Noda, S.-H. Hyon, and J. Morimoto, “An optimal
control approach for hybrid actuator system,” in Humanoid Robots(Humanoids), 2011 11th IEEE-RAS International Conference on.
IEEE, 2011, pp. 300–305.
[17] T. Teramae, T. Noda, S.-H. Hyon, and J. Morimoto, “Modeling andcontrol of a pneumatic-electric hybrid system,” in Intelligent Robots
and Systems (IROS), 2013 IEEE/RSJ International Conference on.
IEEE, 2013, pp. 4887–4892.
[18] H. Higo, Y. Sakurai, T. Nakada, K. Tanaka, and K. Nagayama, “Dynamic characteristic and power consumption on an electro-pneumatic
hybrid positioning system,” Proceedings of the 6th JFPS International,
vol. 2, p. 4, 2005.
[19] I. Sardellitti, J. Park, D. Shin, and O. Khatib, “Air muscle controller
design in the distributed macro-mini actuation approach,” in Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International
Conference on. IEEE, 2007, pp. 1822–1827.
[20] M. Kouchi and M. Mochimaru, “Aist research information database(http://riodb. ibase. aist. go. jp/riohomee. html),” H16PRO287, 2005.
[21] A. Hildebrandt, O. Sawodny, R. Neumann, and A. Hartmann, “Cascaded control concept of a robot with two degrees of freedom driven
by four artificial pneumatic muscle actuators,” pp. 680–685, 2005.
[22] L. E. Carlson, B. D. Veatch, and D. D. Frey, “Efficiency of Prosthetic
Cable and Housing,” JPO: Journal of Prosthetics and Orthotics, vol. 7,
no. 3, p. 96, 1995.
[23] K. Inoue, “Rubbertuators and applications for robots,” in Proceedingsof the 4th international symposium on Robotics Research. MIT Press,
1988, pp. 57–63.
[24] D. Caldwell, A. Razak, and M. Goodwin, “Braided pneumatic muscle
actuators,” in Proceedings of the IFAC Conference on Intelligent
Autonomous Vehicles, 1993, pp. 507–512.
[25] K. K. Ahn and H. P. H. Anh, “Comparative study of modeling andidentification of the pneumatic artificial muscle (PAM) manipulator
using recurrent neural networks,” J Mech Sci Technol, vol. 22, no. 7,
pp. 1287–1298, Oct. 2008.
[26] E. Snelson and Z. Ghahramani, “Sparse gaussian processes using
pseudo-inputs,” in Advances in Neural Information Processing Systems
18. MIT press, 2006, pp. 1257–1264.

3578