2017 IEEE International Conference on Robotics and Automation (ICRA)Singapore, May 29 - June 3, 2017

Fabric Sensory Sleeves for Soft Robot State Estimation
Michelle C. Yuen1 , Henry Tonoyan2 , Edward L. White1 , Maria Telleria2 and Rebecca K. Kramer1

Abstract— In this paper, we describe the fabrication andtesting of a stretchable fabric sleeve with embedded elasticstrain sensors for state reconstruction of a soft robotic joint.
The strain sensors are capacitive and composed of graphitebased conductive composite electrodes and a silicone elastomer
dielectric. The sensors are screenprinted directly into the fabricsleeve, which contrasts the approach of pre-fabricating sensors
and subsequently attaching them to a host. We demonstrate
the capabilities of the sensor-embedded fabric sleeve by determining the joint angle and end effector position of a soft
pneumatic joint with similar accuracy to a traditional IMU.
Furthermore, we show that the sensory sleeve is capable ofcapturing more complex material states, such as fabric bucklingand non-constant curvatures along linkages and joints.

Keywords: soft material robotics, hydraulic/pneumatic actuators, ﬂexible robots
I. I NTRODUCTION
Pneumatically-actuated robotic systems have the potential
to ﬁll gaps left open by traditional rigid robots. The materials
comprising pneumatic actuation systems, such as fabrics andelastomers, are lightweight and ﬂexible. As a result, pneumatic systems are inherently soft, well-suited for operation
alongside similarly soft humans, and easily compacted for
efﬁcient packing and transport. Because of their ability towithstand much larger deformations and intrinsic damping
properties, pneumatic robots are robust to large impacts andare able to adapt to complex, poorly-deﬁned environments.
These are signiﬁcant challenges for rigid robotic manipulators because interaction with their surroundings requiresthat the manipulator either actively or passively match its
compliance to each physical interaction [1].
While there has been a great deal of work on the designand open-loop control of pneumatic actuators, proprioceptive
feedback onboard the pneumatic systems has seen limited
development [2] [3]. The compliance of a pneumaticallydriven system makes it inherently under-actuated (i.e., thesystem has more degrees of freedom (DoF) than it has controlover). As a result, it is a challenge to identify the state of
the system for closed-loop control of pneumatically-driven
robots.
To address this challenge, highly deformable soft robotic
systems require equally deformable sensors for state estimation. Recent work in the area of high-deformation elastomerbased strain sensors has shown that they are capable of
1 MCY, ELW, and RKK are with the School of Mechanical Engineering,Purdue University, West Lafayette, IN, USA. 2 HT and MT are with
Otherlab Pneubotics, San Francisco, CA, USA. email: {yuenm,

white335, rebeccakramer}@purdue.edu; {henry,maria}@otherlab.com

978-1-5090-4633-1/17/$31.00 ©2017 IEEE

Fig. 1. Grub joint as it ﬂexes to lift 15lbs. Wrinkling is observed in
the fabric near the base of the Grub, resulting in buckling of the linkage
and causing unintended vertical deﬂection of the end effector. Compoundcurvature is observed along the length of the actuation bellows.

measuring the large strains that occur on the surface ofpneumatic actuators. In this paper, we bring together softsensing with pneumatic actuation to perform joint angle
proprioception. We embedded silicone elastomer-based sensors into Spandex fabric to form a sensory sleeve, which
was secured around a pneumatic joint. The test platform
used in this paper, henceforth referred to as the Grub, wasdeveloped by Otherlab Pneubotics. It is a single DoF roboticarm composed of two passive linkages on either side of anantagonistic pneumatic actuator pair. Constructed of highstrength fabrics, the Grub is capable of lifting eight times its
own weight. The sensory sleeve wraps around the bellows of
the Grub to measure the change in longitudinal length of thebellows as they are inﬂated and deﬂated to cause joint ﬂexionand extension (Figure 1). By fabricating the sensors onto
removable sleeves, we were able to balance ease of iterative
design by testing different sensor layouts on the Grub with
progress towards a large-scale manufacturing process, which
reduces the complexity and cost of incorporating sensing into
the structure of the robotic platform.
The contributions of this work are: 1) manufacture ofa sensory array into a stretchable fabric, 2) demonstrationof a sensor-embedded fabric sleeve in reconstructing thejoint angle of a pneumatically-actuated joint, the results ofwhich are veriﬁed through comparison of this sensory sleeveto inertial measurement unit (IMU) and motion capture
measurements of joint state, and 3) application of discrete
sensors towards detecting smaller-scale and more complex
system states, such as buckling in passive links.
II. P REVIOUS W ORKSoft strain sensors, particularly for strains greater than
50%, can be divided into two categories: resistive andcapacitive. Resistive strain sensing transduces strain into5511

a change in resistance. In one approach, microchannels
containing a conductive ﬂuid such as a liquid metal or ionicliquid are embedded into elastomer materials [4] [5] [6].
Strain applied to the material causes a change in lengthand cross-sectional area of the conductive ﬂuid, resulting
in a change in resistance. Another approach to a resistive
strain sensor uses conductive silicone elastomer composites.
In these devices, conductive particles such as graphite [7],carbon nanotubes [8], and silver nanowire [9], are dispersed
within a silicone elastomer matrix. As strain is applied tothe material, the particles decrease contact with one another,resulting in a higher resistance. Yet another approach toresistive strain sensing uses conductive yarns and fabrics. The
ﬁbers comprising the yarn or fabric are made of conductive
material, such as stainless steel thread, or are coated in
conductive particles [10] [11]. As strain is applied, theconductive ﬁbers comprising the yarn increase contact with
one another, decreasing the resistance of the yarn.
Capacitive strain sensing transduces strain into a change
in capacitance. These sensors are composed of parallel
electrodes separated by a dielectric layer. Merritt et al.
used sliding electrode plates to convert changes in chest
diameter during respiration to a change in capacitance [12].
Alternatively, researchers have made capacitive sensors thatuse a parallel-plate electrode construction that rely upon
the Poisson effect in the dielectric material. As the sensors
are stretched, the electrode area increases and the dielectric
thickness decreases, resulting in an increase in capacitance.
The stretchable electrode layers have been made using thinmetal ﬁlms [13], or more commonly, silicone embedded withcarbon nanotubes [14] [15], carbon black [16] [17], or silver
nanowires [18]. Researchers have demonstrated the application of discrete elastomer-based capacitive strain sensors
towards capturing human motion [17] [18] and microrobotic
linkage displacement [15].
Various researchers have demonstrated control of a 1DoF
pneumatically actuated joint using IMU data to capture joint
angle. Best, et al. determined the efﬁcacy of using model predictive control and a single pressure input to control the joint
angle of a predecessor to the Grub used in this work [19].
Gillespie et al. demonstrated the ability to simultaneously
control the stiffness and the angle of the Grub joint [20].
Both [19] and [20] relied upon IMUs placed on the linkage
furthest away from the ﬁxed base to capture joint angle data.
However, IMUs are susceptible to drift over time, requiring
frequent re-calibration [21]. Additionally, the use of IMUs tomeasure joint angle implies that the linkages are treated as
rigid elements, whereas, in our system, they are susceptible
to buckling and drooping, as seen in Figure 1. As the numberof soft joints and linkages in series increases, these small
errors from drift and passive linkage displacement will add
up to large errors when estimating the location of the end
effector.
Other approaches to state estimation of a pneumatic actuator have used visual feedback. Marchese, et al. relied upon
the use of multiple external cameras [22], whereas Wanget al. performed visual servoing by afﬁxing a camera to

the end of the robotic arm to track the position of the end
effector relative to a ﬁxed point on a reference frame [23].
Although visual-based proprioception has been demonstrated
in a controlled setting, it is limited by the infrastructure
required to hold cameras or reference frames in place. In
contrast, a sensory sleeve integrated into the structure of thepneumatic joint allows the system to operate more freely, as
it does not require additional environmental infrastructure.
The application of soft sensing devices to proprioception
and potential closed-loop control of soft inﬂatable robotic
structures remains an emerging ﬁeld of research. Park, et al.
demonstrated the use of embedded liquid metal strain gauges
to detect the state of a McKibben actuator [24]. This work
was extended towards an all-elastomer sleeve with distributed
modules of penumatic actuators with embedded sensing [25].
Farrow, et al. and Bilodeau, et al. demonstrated integration ofliquid metal-based sensors directly into a pneumatic gripper[26] [27]. The structures of these devices are composed
primarily of elastomers, easing the integration of elastomerbased sensing devices. In contrast, the Grub penumatic joint
is composed of fabrics. We therefore focused our integration
approach on compatibility with fabric substrates, which lends
well to both fabric-based soft robots and wearable sensory
garments.
III. H ARDWARE
A. Sensor-embedded Sleeve
The sensors embedded in the sleeve are ﬂexible andstretchable parallel-plate capacitors that consist of a dielectric
layer sandwiched between two conductive electrode layers.
The electrode layers are a conductive composite made up
of silicone elastomer (DragonSkin10, Smooth-On Inc.) andexpanded intercalated graphite (10wt%). During fabrication of the sensors, the conductive composite is processedwith 2.36wt% graphite, 21.26wt% silicone elastomer, and76.38wt% cyclohexane to ensure homogeneous dispersionof graphite within the silicone elastomer. Cyclohexane is avolatile organic solvent that does not affect the ﬁnal properties of the silicone elastomer nor the conductive composite
once it vaporizes and the elastomer cures. The dielectricmaterial is composed of a mixture of 4 parts DragonSkin
10 Slow and 1 part Silicone Thinner (Smooth-On Inc.). The
fabric is stretchable Lycra material (Payless Fabrics).
The sensory sleeve is fabricated using a screenprinting
technique. The schematics for the sensors are shown in
Figure 2(a,b). Masks for each layer are made by laser-cutting
the pattern out of polyethylene terephthalate (PET) ﬁlm. The
fabric is secured with tape to a ﬂat surface. First, a base layercomposed of the dielectric material is screenprinted onto thefabric by scraping a threaded rod across the surface. Oncethe base layer is cured, the mask for the bottom electrodelayer is aligned over the base layer. The bottom electrodeis then screenprinted over the base layer and the mask isimmediately removed to prevent the electrode material fromwicking under the mask and spreading over the base layer.
After the bottom electrode cures, the dielectric layer and
top electrode layers are screenprinted in the same manner as

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Fig. 3. Response of screenprinted sensors due to applied displacement
causing strains from 0-50% for 20 cycles. The average and conﬁdence
interval is taken from data for 3 individual sensors. ΔV is calculated as
ΔV = V − V0 , where V is the sensor output and V0 is the initial sensor
reading.

B. Signal Conditioning Board

Fig. 2. (a) Top-down view of the dual-sensor sleeve wrapped around the
Grub and a zoomed in view of the sensors, copper interface and signal
conditioning boards. The dual-sensor pair is mirrored on the bottom of the
Grub. (b) Schematic of the screenprinted layers for dual-sensor designs,
shown in the order they are patterned in, from top to bottom. (c) Schematicof the screenprinted single-sensor design. (d) Schematic of the cross-sectionof a sensor and the relationship between the sensor’s capacitance and thegeometry of the layers.

The signal conditioning board transduces the change incapacitance of the sensors to an analog voltage. The boards
were custom-designed to minimize the amount of rigid materials used in the sensory sleeve. To measure the capacitance,each board charges up the capacitive sensor to 2.262V andmeasures the time it takes to discharge back to 0.238V. The
frequency at which the charging occurs is tuned to matchthe time constant of the capacitive sensor. Changing thecharging frequency allows us to tune the sensitivity of thesignal conditioning board. For the sensors on the joint, thecharging and discharging cycle period is 8.4ms. The analog
voltage output is linearly related to the capacitance.
C. Pneumatic Arm

the bottom electrode. The dielectric layer is designed with a
2.5mm margin offset outward from the bottom electrode toprevent the top electrode from wicking down the edge of thesensor and contacting the bottom electrode. For interfacing,
the bottom electrode is not fully covered by the dielectric
layer at one of the ends of the sensor, leaving a 10mm longportion exposed for interfacing. Looking at the cross-sectionof the sensors, the approximate thicknesses of the elastomerinﬁltrated fabric base, bottom electrode, dielectric layer, andtop electrode are 500, 150, 300, and 150μm, respectively.
Copper-clad polyimide sheets (Pyralux, Adafruit Industries) were sewn onto the end of the sensor, making contact
with both the top and bottom electrode layers. In order toprevent the thread from electrically bridging layers at the sew
holes, a coring punch (1.75mm, Harris UniCore) was used
to punch out holes in the sensor before sewing. Wires werethen soldered to the copper, linking the sensor to the signal
conditioning board.

The Grub is a pneumatic arm composed of two passive
linkages on either end of an active 1DoF joint. The passive
linkages are composed of air bladders inside of reinforcing
PVC fabric cylindrical shells. The passive linkages are pressurized to approximately 10psi throughout operation. The
active joint consists of two antagonistic bellows-like bladders
reinforced by ballistic nylon. Inﬂation of these bellows
is controlled by valves regulating the ﬂow of compressed
air. Changing the pressure differential between antagonistic
bellows causes the change in joint angle.
IV. C HARACTERIZATION AND M ODELING
A. Sensor Characterization
The sensors were characterized individually to determine
their response to linear extension. As the sensor is stretched,
the area of the electrodes increases while the thickness of thedielectric layer decreases, causing a change in capacitancethat is transduced to a change in voltage (Figure 2(d)). The
voltage output is linear with respect to applied displacement,5513

the sensor.
Substituting ΔL into Eq. 1, the joint angle can be calculated as a function of the sensor output:α=

L0 +C1 · ΔV
R + D2(3)

We note here that changing the boundary conditions during
sensor extension changes the sensor response. For example,
the response of the sensor extended in free space differs fromthe response of the sensor extended across the joint [28].
This is due to the addition of the surface normal force thatarises as the joint bellows are inﬂated, which compresses thedielectric layer more than when applying axial strain in freespace. However, the sensors response is linear regardless ofboundary conditions, and calibration in a speciﬁc operating
condition will yield the value of C1 in Eqs. 2 and 3.
When the Grub is lifting larger payloads, wrinkling in the
fabric of the proximal linkage will cause buckling near thebase (Figure 4(b)). The base angle, β , is the angle between
the proximal linkage and horizontal:ΔLP2
β = sin−1(4)
D
Fig. 4. (a) Schematic showing the relationship between joint curvature andsensor length. (b) Schematic showing buckling of the proximal linkage near
the base. (c) Schematic combining proximal joint buckling with actuation.

with no discernible cycle-to-cycle hysteresis (Figure 3).
However, this linear trend breaks down at low strains (<5%),
due to the polymer-based Spandex fabric plastically deforming and therefore causing the sensor to be slack at low strains.
This ”break-in” length is typical of a polymer system (stressrelaxation) and can be accommodated by pre-straining thesensor beyond what it will encounter in operation.
B. Grub Modeling
We generated models of the Grub’s geometry for constantcurvature joint ﬂexion and for combined joint ﬂexion andlinkage buckling. As the bellows on one side of the joint
are inﬂated, the arc length along the outside of the joint,
L1 , will increase linearly with respect to the joint angle, α(Figure 4(a)):D(1)(R + )α = L1 = L0 + ΔL
2
where R is the radius of curvature to the midline of the Grub,
D is the diameter of the Grub, L0 is the length of the midline
seam, L1 is the length along the outside of the joint, and ΔL
is the change in length from L0 to L1 .
Using the characterization of the sensors (Figure 3), thechange in length of a sensor placed along the outside of
the joint, ΔL, can be calculated as a function of the sensor
output:
ΔL = C1 · ΔV(2)
where C1 is the slope of the linear regression of the sensor
characterization and ΔV is the change in voltage output of

where LP2 is the contracted length of the bottom of theproximal linkage due to buckling, and ΔLP2 is the difference
between the nominal length of the proximal linkage LP1 andthe contracted length LP2 . As a result of this buckling, thevertical and horizontal displacements of the end effector, Δz
and Δx, are a function of the base angle β and the distal
linkage angle γ:
Δz = (L0 + LP1 + LD1 )sinβ + L je sinγ
Δx = (L0 + LP1 + LD1 )(1 − cosβ ) + L je (1 − cosγ)

(5a)
(5b)

where LD1 is the nominal length of the distal linkage, γ isthe angle of the distal linkage relative to horizontal and L je
is the length between the joint center and the end effector.
We also note the length between the joint center and thebase, Lb j , as shown in Figure 4(c). Depending on the loading
condition, the joint center may be located anywhere along
the joint midline seam L0 .
V. S TATE R ECONSTRUCTION
We ﬁrst calibrated the sensors in situ and then performed atest for state reconstruction. In particular, we were interested
in capturing the joint angle and the end-effector positionof the Grub. The test platform was composed of a Grub
positioned horizontally with 1) single sensors on the top
and bottom of the joint to measure joint angle, 2) a sensor
laid along the bottom of the proximal linkage to measure
buckling, 3) an IMU placed on the top of the distal linkage
near the end effector, and 4) two sets of LED motion capture
markers wrapped around each of the proximal and distal
linkages (Figure 2(a)). The voltage signals from the sensors
were captured using the controller unit that also housed the
IMU. The motion capture data served as the ground truth
data, which we used to both calibrate the system and quantify
the accuracy of other measurement methods.

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The experimental procedure consisted of two calibration
phases followed by a validation phase. The two calibration
phases were performed to determine the strain sensors’
responses on the Grub in operation and to identify theeffective joint center and linkage lengths. The center ofrotation of the joint can change under different loading
conditions and is not easily identiﬁed. By performing thesetwo calibration steps, the vertical displacement and the angleof the joint can be calculated using solely the sensor arrayon the surface of the Grub. To validate this, we compared thestate reconstruction from sensor array data to IMU data andthe ground truth generated with the motion capture system(PhaseSpace Systems).
A. Joint Angle Calibration

Fig. 5. First calibration phase for measuring the joint sensor response
as a function of joint angle and determining the effective length of the
distal linkage. (a) Experimental procedure where the Grub is swept fromthe light gray trace to the black trace. (b) Data from the PhaseSpace motion
capture system and strain sensors while sweeping the Grub distal linkage
downwards. (c) Plot of the measured joint angle, θ , vs. the voltage responseof the sensors. The linear regression equations are listed. (d) Vertical positionof the end effector as a function of the angle of the distal linkage. The slopeof this linear regression is the effective length between the apparent joint
center and the end effector. For both plots, the dashed line indicates raw
data and the solid line indicates a linear regression.

The ﬁrst calibration phase was performed to determine thestrain sensors’ voltage response to joint angle and to identify
the effective length of the distal linkage (i.e. the length
between the apparent joint center and the end effector).
Because the response of the sensors on the Grub differsfrom the response when pulling the sensors in free space,
we calibrated the sensors on the Grub. In order to generate
this data, the Grub was actuated in an unloaded state sothat a constant curvature bend was achieved throughout therange of motion and the proximal linkage stayed parallelto the ground (Figure 5(a)). In this test, the angle β from
Figure 4(c) is 0◦ , and therefore, the angle of the distal linkage
with respect to the ground, γ, is equal to the joint angle
θ . The data from the PhaseSpace motion capture system,
IMU and strain sensors are shown in Figure 5(b). Using thisdata, we extracted the angle γ = θ and used it to calibrate
the sensors on the joint. The results, shown in Figure 5(c),demonstrate that the sensors indeed have a linear responsewith respect to joint angle. The regression equations for the
top and bottom sensors are shown on the plot and are used
for the second calibration phase to ﬁnd the joint angle θwhen the Grub is loaded.
In addition to ﬁnding the relationship between the sensor
response and the joint angle, we determined the effective
location of the center of the joint relative to the end effector.
This information yields the apparent length of the distal
linkage, L je , which is composed of the entire length of thepassive distal linkage and part of the length of the bellows.
To ﬁnd L je , we constructed a right triangle with L je as
the hypotenuse and z − zEE (γ = 0◦ ) as the leg opposite γ,
where z is the height of the end effector and zEE (γ = 0◦ ) isheight of the end effector when both linkages are horizontal(Figure 5(a)). Using z = L je ∗ sin(γ), we found the slope ofthe linear regression in Figure 5(d) to determine L je .
B. Base Angle Calibration
The second calibration phase simulated a buckling event
wherein the fabric on the bottom of the proximal linkage
near the base wrinkles inward, causing the entire pneumatic
arm to bend downwards. We determined the response of thebuckling sensor, which was fastened along the bottom of theproximal linkage, to the change in angle of the proximal

5515

linkage due to buckling near the base. We also identiﬁed
the effective length of the proximal linkage (i.e. the length
between the base and the apparent joint center). After relating
the joint sensor outputs to the joint angle, we performeda base angle calibration. To do this, we ﬁrst actuated theunloaded Grub to an upright position (θ ≈ 50◦ ) and thenmanually forced the end effector downwards, extending thejoint and forcing the proximal linkage to buckle near thebase (Figure 6(a)). The motion capture system was used tocapture γ while the bottom joint sensor and buckling sensor
responses were recorded (Figure 6(b)). Using solely thebottom joint sensor, we determined the joint angle θ using
the corresponding calibration equation shown in Figure 5(c).
Using the relationship β = γ −θ , we determined β , the angleof the proximal linkage due to buckling near the base. We
then plotted the response from the buckling sensor against theestimated base angle and ﬁt a line to determine the buckling
sensor’s response with respect to β (Figure 6(c)). We also
determined the base-to-joint length, Lb j , by ﬁnding the slope
to the linear regression of the joint height plotted against sinβ
(Figure 6(d)). The joint height was determined by subtracting
the vertical rise due to the distal link bent at angle γ fromthe height of the end effector measured relative to ground.
C. Comparison of Sensory Array to IMU

Fig. 6.
Second calibration phase for measuring the buckling sensor’s
response to linkage buckling near the base and determining the effective
length of the proximal linkage. (a) Experimental procedure where the Grubis forced downward from the light gray trace to the black trace. (b) Data
from the PhaseSpace motion capture system and joint angle strain sensors
while forcing the Grub distal linkage downwards and causing buckling tooccur near the base. The estimated base angle, β is the difference between
γ and θ . (c) Plot of β vs. the voltage response of the buckling sensor. The
linear regression equation is listed. (d) Plot of the vertical position of thejoint center vs. sin(β ). The slope of this linear regression is the effective
length between the apparent joint center and the base. The height of the joint
center when no buckling at the base occurs is marked. For both plots, thedashed line indicates raw data and the solid line indicates a linear regression.

After these two calibration procedures, the combinationof the joint sensors and buckling sensor generates sufﬁcient
data to estimate the angle of the distal linkage and thevertical position of the end effector relative to the ground. To
test the accuracy of the state information from the sensors,
we completed a test in which we loaded the upright distal
linkage with 5lbs and gradually actuated the Grub to lower
the weight in a controlled manner (Figure 7). The motion
capture system and IMU both recorded the angle and positionof the distal linkage. The joint sensors captured the joint
angle, θ , and the buckling sensor captured the base angle,
β . These two values were combined to generate the estimatedangle and vertical position of the distal linkage. From Figure 7(a), we see that the link angle generated from the strain
sensor, IMU, and motion capture are in good agreement.
Furthermore, when capturing the z-displacement of the end
effector (Figure 7(b)), the sensory array matches better to the
motion capture data than the IMU data. We believe that thisis a result of the conformal sensory sleeve’s ability to capture
the more complex states and distributed deformations seen insoft systems. We note that a multi-IMU-based system would
yield a more accurate representation of the Grub; however,
the addition of more rigid components could be detrimental
to the overall performance of the system.
D. S-curve Detection
Under higher loads, the constant curvature assumption of
the link is no longer valid. The load at the end effector results
in bending moments and shear forces throughout the Grub.
Because the active joint is the most compliant portion of
the system, the end effector loading causes the bellows todevelop a second curvature at the distal end, which we refer5516

Fig. 8. (a) Diagram of the active joint of the Grub showing the twocurvatures comprising the S-curve that appears due to the applied load. The
two sensors used in this experiment are located on the bottom side of thejoint. (b) Plot of the response of two sensors, as a function of downward
vertical displacement that occurs as the end effector of the Grub is loaded
while the bellows are inﬂated to maintain a constant 0◦ angle between the
distal link and the ground.
Fig. 7. Comparison between the sensory sleeve, an IMU, and motion
capture data to determine joint angle and end effector height. (a) Inset:
Experimental procedure where the Grub begins in an upright position andis gradually lowered while holding 5lbs. The plot shows the distal link angle,γ, as measured by the motion capture system, an IMU, and the combinationof multiple strain sensor measurements. The sensor link angle, shown in asolid blue line is the sum of the two angles θ and β which were foundusing the regression equations for the bottom joint sensor and buckling
sensor, respectively. (b) Comparison of the height of the end effector as
measured by the motion capture system, an IMU and the sensors during thecourse of the actuation.

to as the “S-curve”, shown in Figure 8(a). Using two sensors
on the bottom of the joint, we were able to detect when thisS-curve develops. We collected the data shown in Figure 8(b)by incrementally loading the Grub while simultaneously
keeping the proximal and distal links approximately parallelto the ground; this required increasing inﬂation of the bellows
with increasing load. At the start of the test, the Grub isunloaded and the end effector has no vertical displacement(Δz=0). Hence, the sensors are both unstrained and showsame response. At relatively low loads (resulting in verticaldisplacements between 3mm and 25mm in Figure 8(b)),
the joint develops the S-curve. Comparing the two sensor
responses, the bottom distal sensor has a sharper response,
indicating more strain, and thus larger curvature, relative
to what the bottom proximal sensor experiences. However,as the Grub is loaded further, the bellows are inﬂated tohigher pressures to maintain the horizontal orientation of
the distal link. Therefore, the stiffness of the bellows region
increases, which moves the inﬂection point of the S-curve
towards the base until it reaches the junction between theproximal link and the bellows. This causes the compoundcurvature of the joint to transition towards a single curvature,
resulting in the convergence of the sensor responses. In
Figure 8(b), this convergence is observed at displacements
beyond 25mm. Detection of compound curvatures is crucial
for proprioception in continuum or quasi-continuum joints
such as the Grub. The results generated here using the dualsensor design demonstrate the ability of the sensory sleeve tocapture complex state information beyond simple joint angle.
VI. C ONCLUSION AND F UTURE W ORK
In this work, we demonstrated the use of a fabric-based
capacitive sensing array for state reconstruction of a 1DoF
pneumatic arm. The sensing array was composed of highdeformation capacitive strain sensors that were manufactured
by screenprinting dielectric silicone elastomers and conductive silicone elastomer composites into Spandex fabric. We
then demonstrated that these sensors exhibit a linear change
in capacitance due to applied linear strain or curvature. Usinga sleeve composed of a single sensor for each side of thepneumatic arm and a buckling sensor, we tracked not only
the joint angle of a soft pneumatic arm, but also the vertical5517

displacement due to buckling in passive linkages. Comparedto the performance of a single IMU, the sensor sleevematched the IMU’s accuracy in determining joint angle andoutperformed the IMU in determining the vertical position of
the end effector. Finally, we demonstrated that a dual-sensorper-side sleeve can be used to track the compound curvature
deformation in the joint of a pneumatic arm.
Future work may be focused in several directions: improvement of the sensor signal conditioning, integrating thesensory sleeve directly into the pneumatic joint, expanding
the sensory array, and multimodal sensing. The sensor signal
conditioning can be improved by reducing noise, increasing
the sensitivity of the sensors to the operating range of strain.
Integrating the sleeve directly into the fabric comprising
the pneumatic joint would have multiple beneﬁts, including
ensuring that the sensors are ﬁxed to the surface of the robot
and eliminating slackness of the sensory sleeve. Expansion of
the sensory array would increase the resolution of the surface
strain mapping of the pneumatic manipulator. However, thismust be balanced with the increased number of interface
electronics. While the sensors developed in this work weresolely used for surface strain measurement, capacitive sensors have been used for pressure and contact sensing. Integration of these sensing modes alongside the strain sensors
would result in a data-rich soft sensing system. Further
progress in sensory skins and fabrics will enable accurate
state estimation, and therefore closed-loop control of soft
systems without the limiting conﬁnes of visual feedback.
VII. ACKNOWLEDGMENTS
This work was supported by a Robotics Fast Track program (GS10F0-247U) from the Defense Advanced Research Projects Agency.
The Grub utilized for testing was designed and built under an
Otherlab NASA SBIR Phase 1 Contract (NNX14CA56P). MCY and
ELW are supported by the National Science Foundation Graduate
Research Fellowship (Grant DGE-1333468). Any opinion, ﬁndings,
and conclusions or recommendations expressed in this material arethose of the authors and do not necessarily reﬂect the views of the
Defense Advanced Research Projects Agency, National Aeronautics
and Space Administration or the National Science Foundation.

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