2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Congress Center Hamburg
Sept 28 - Oct 2, 2015. Hamburg, Germany

Monolithic Fabrication of Sensors and Actuators in a Soft Robotic Gripper
R. Adam Bilodeau1 , Edward L. White1 and Rebecca K. Kramer1∗

Abstract— In this paper, we present a fluidically functionalized soft-bodied robot that integrates both sensing and actuation. Rather than combining these functions as an afterthought,
we design sensors and actuators into the robot at the onset, both
reducing fabrication complexity and optimizing component interactions. We utilize liquid metal strain sensors and pneumatic
actuators embedded into a silicone robotic gripper. The robot‘s
body is formed by curing the silicone in complex 3D printed
molds. We show that the liquid metal strain gauges provide
a repeatable resistance response during robotic actuation. We
further show that, given sufficient control over other timedependent variables, it is possible to determine when the robot
begins gripping an object during actuation.

I. I NTRODUCTION
Researchers in robotics are beginning to diverge fromthe traditional rigid robots that are so prominent in manufacturing. Engineers in the emerging field of soft robotics
are working on solving problems that cannot be solved
easily with rigid robots, including improving human-robot
interactions, developing formable robots that can adapt tovarying geometric constraints, and robots that can absorb
impacts/shocks by deforming their main body. Siliconebodied pneumatic robots have helped to shape the idea ofa truly “soft” robot, since most of the body is formed out ofa soft, elastomeric material with an extremely low stiffness[1]. Many of the silicone elastomers used in soft robotics arecapable of sustaining strains anywhere from 100-500% [2].
Since these elastomers are highly deformable, engineers can
inflate internal pneumatic channels to provide rigidity andstability to the robot. With the proper configuration, suchrobots can use their pneumatically actuated components togain form, function, and even locomotion [1], [2], [3].
One of the challenges associated with soft robotic designis adding sensors that can close the robotic control loop.
Researchers are actively working on soft-bodied control
methods [4], [5], [6], but lack general proprioceptive feedback sensors for soft robots. Since soft robots undergo
extremely high strains during actuation, traditional feedback
systems either break or are difficult to attach to the robot.
One solution to high deformation strain sensing has been to
create a strain gauge made out of a room-temperature liquid
metal embedded in an elastomeric matrix [7], [8]. Thesestrain gauges can be an effective way of gathering data insome robots [9], but the issue of attaching the gauges to therobot remains an open problem.
∗ Authortowhom

correspondence

shouldbe

addressed

rebeccakramer@purdue.edu
1 Departmentof Mechanical Engineering, Purdue University, West
Lafayette, IN 47907, USA

978-1-4799-9994-1/15/$31.00 ©2015 IEEE

Fig. 1. Pneumatic robot with liquid metal sensors embedded into thebody. Above: the robot in its relaxed state. Below: the robot after pneumatic
actuation. Scale bars are 1 cm.

In this paper, we present a manufacturing process thatincorporates liquid metal strain sensors directly into thebody of a 4-arm, pneumatically-actuated, soft robotic gripper(see Figure 1). This is in contrast to previous work where
the sensors and actuators are built separately and integrated
afterwards. In the robot, each strain sensor is independent of
the others and capable of providing real-time feedback about
the actuation of the arm of the robot. The feedback fromthe sensors can be characterized in a way that distinguishes
activated states of the robotic gripper and, given control overother actuation parameters, makes it possible to determine if
the robot is gripping an object or not.
II. P REVIOUS W ORK
A pneumatically-actuated soft robot was described indetail by Shepherd, et al. [1]. This robot was actuated throughpressurization of internal cavities called “pneu-nets” formed
within a highly elastic polymer body. This work has been
extended by researchers who have all primarily focused2324

on improving actuation. Great strides have been made in
improving the speed of pneumatic actuation [2], [10] and in
untethering pneumatic robots from an external compressed
air source [3], [4]. Other researchers have created soft-hardhybrids that take advantage of the strengths associated withboth rigid robots and soft pneumatically-actuated robots toperform tasks that neither can do alone [11].
Sensors for soft robotic systems have also been an areaof recent research, as they provide state reconstruction thatallows for control feedback. One approach to sensing insoft-bodied systems is with eutectic gallium indium alloy(eGaIn) encased in microchannels within a highly elastic
substrate. Gallium indium alloy is a room-temperature liquid
metal that is non-toxic and highly conductive [12], [13]. The
basic principle behind these high displacement strain gauges
is simple: as the elastomer is stretched, the length of thechannel increases and the cross-sectional area is reduced.
Changing the geometry of the channel alters the resistanceacross the channel (following basic resistance laws), providing sensory feedback on the physical deformation. Since
the conductor inside of the channel is liquid, and the matrix
surrounding it is highly elastic, the gauges are ideal for large
strain applications. Park, et al. investigated the sensitivity ofthis class of devices to variations in channel geometry [14].
Gallium indium alloy has been used in a number of different soft robotic applications. Examples of sensing elements
fabricated from eGaIn-filled microchannels include curvature
sensors [15], [16], stress sensors [17], [18], and strain sensors[19], [20]. The sensors we have fabricated into our robot relyon the same mechanism as those previously discussed. Whatis unique about our approach is that the sensor is fabricateddirectly into the robot instead of as a separate structure.
III. E XPERIMENTAL
A. Fabrication
The initial pattern for the pneumatic robot is a derivativeof a simplified pneumatic robot designed by Finio et al.
[21]. We started with this concept because it is extremely
simple, and allowed us to focus on adding sensory elements
to the robot instead of focusing on the pneumatic robot bodydesign. The robot is a composite of two silicones: Ecoflex
00-30 (Smooth-On, with hardness below the Shore A scale)to form the pneumatic channels and the main body, andSilgard 184 (PDMS, DowCorning, Shore A hardness of 50)
to seal the channels. The design has a single air input port
and is formed with a 3D printed mold. We kept the single
fill port design as it allowed us to simultaneously activate
and control all arms of the robot from the same air source,
but we modified the fabrication method so that we could
incorporate sensory channels into the robot. Because themolds were simple and easy to form in a 3D printer, we wereable to quickly iterate though different methods to integrate
the sensors.
Our modified process includes a 2-step mold (Figure 2)
for the main body that embeds the pneumatic channels andthe sensory channels into the Ecoflex body. As demonstrated
in Figure 3a-3d, we first filled the primary mold (depicted in

(a)

(b)
Fig. 2. 3D modeling representations of the molds that were printed to cast
the robot. (a) The primary mold, initially filled with liquid elastomer, withan inset showing the embossed channels that create the sensor channels.
(b) The channel mold that is placed in the primary mold to create thepneumatic channels in the robot.

Figure 2a) with uncured Ecoflex. This section of the moldhas embossed features to create channels for the liquid metal
sensors. The mold for the pneumatic channels (depicted
in Figure 2b) was then lowered into the Ecoflex, creating
hollows on the opposite side of the robot body from theliquid metal sensor channels. The Ecoflex was allowed tocure completely and was removed from the mold.
Figures 3e-3f show the next two steps of the process. We
filled the open serpentine channels with room temperatureliquid metal (eGaIn, Sigma-Aldrich). Once the channels werefilled, a small amount of liquid Ecoflex was placed over thechannels to seal them. Most soft strain sensors are sealed
before being filled with liquid metal [19], but in our case wefound that it was easier to seal them afterward. When sealing
the channels, we took care to prevent the liquid elastomer
from running across the top of the robot and building up a
thick layer of polymer. Because the curling actuation motion
is a result of the stiffness difference between the bottom andthe top of the robot, both material properties and geometric
design can change the stiffness and therefore change theactuation motion. A thicker layer of Ecoflex on top of therobot would increase stiffness and result in less curl in the
robotic arm. Once the silicone cured, we punctured the thinlayer with the electrical wiring, making a contact with theliquid metal. The wires were then folded down and sealed
onto the robot with a few more drops of liquid elastomer,
preventing the wires from pulling out while the robot isundergoing large deflections. The pneumatic tube was also2325

Fig. 4. A preliminary design with the liquid metal sensors directly overthe pneumatic channels. The image on the left is the top of the channels
before the arm is inflated (the surface is flat), and the image on the rightis the same surface, at the same scale, after it is inflated. The liquid metalchannels undergo a large increase in both length and width causing an
extreme collapse in the channel’s third dimension. The box in both photos
is surrounding the same section of the sensor. The inset is a magnification of
the inflated channel with an outlined area that experienced eGaIn withdrawl
due to the channel collapse. The scale bars on both images are 2mm, andthe inset has 2.5x magnification.

Fig. 3. (a) The bottom half of the mold is printed and (b) filled with liquid
elastomer. (c) The channel mold (previously printed) is embedded inside theliquid elastomer to create the pneumatic channels. When the elastomer has
cured (d) the mold can be removed from around it. To finish the sensors,(e) the eGaIn channels are filled with liquid metal and (f) sealed with liquid
elastomer. Finally, (g) the pneumatic channels are sealed by bonding the
body to a thin layer of Silgard 184.

punctured into the robot through a central hollow (created in
the mold) and sealed in the same manner. To finish building
the pneu-nets, we prepared and cured a thin, flat layer ofSilgard 184. We then sealed the two layers together by adding
a small amount of liquid elastomer on top of the Silgard 184
and placing the body of the robot down onto the layer. Whenthe liquid elastomer cured, it bonded the two halves of therobot together (Figure 3g).
B. Sensor Design
Our initial, unsuccessful, sensor design placed the sensors down the center of the pneumatic arm, the area thatexperiences the greatest strain during actuation. Although
we did this to maximize the response from the gauges,
the strain gauges would either lose conductivity or breakcompletely. Loss of conductivity occurred during inflation,
when the outside of the pneu-nets experienced biaxial strains(along the length and width of the robotic arm). These strains
induced channel collapse, forcing the eGaIn to withdraw
from sections of the channel and breaking conductivity(see Figure 4). After actuation, the channel would need
to be massaged to push the eGaIn back into the pinched
area, removing any potential automation from this design.
Furthermore, the channels did not have a high fatigue life.
Previous demonstrations have shown the silicone in thesetypes of pneumatic robots will endure many cycles of actuation without failure [1], [10]. However, when we placed thesensors over the areas of highest strain, the silicone (Ecoflex)
sealing the liquid-metal channels was unable to sustain therepeated strains and the sensors would burst after 5-10 cycles.
Adding a thicker layer of elastomer mitigated this issue, but
it did so by significantly impacting the actuation dynamics
of the robot.
We studied the robot during actuation to find a location
for the sensors that could result in appropriate dynamic
response without failure. Pneumatic channels are typicallydesigned with regions that have a thicker cross section thanother locations (see Figure 3). This helps create a specificmotion in the arm during pneumatic actuation [2]. Althoughthese sections deform, they are not subjected to the same
magnitude of strains as the original location we selected forthe sensors. In the final design of our robot, we embedded
the serpentine liquid metal pattern into these sections (seeFigures 3 and 5), preventing many of the problems associatedwith previous design locations. Furthermore, finite element
analysis by Mosadegh et al. suggests that our selected
location still experiences moderate strains and stresses during
actuation [10]. Though we do not claim that this location isperfectly optimized, identifying a location that experiences
strain, but is not excessively stressed, is key to successfully
integrating feedback strain sensors directly into the bodies
of soft robots.
C. Sensor Characterization
Our sensor characterization tests were designed to provide
evidence of the success of our proof-of-concept robot. We
performed a simple test on one of the robot’s arms to ensurethat the strain gauge location provides a measurable response
during actuation. We measured the sensor’s response to two
different modes of strain: linear extension and pneumatic
actuation (Figure 6). For the linear extension tests, we pulled
the tip of the robot arm linearly away from the body of therobot, duplicating a load similar to that of a normal strain
gauge [22]. We then used a hand pump to pneumatically2326

Fig. 5. The pneumatic robot gripping a cylindrical container. The serpentine
liquid-metal sensor channels are clearly visible around the outside edges ofthe pneumatic channels (dark against the white background).

actuate the robot in steps while taking profile photographsof the arm, which we analyzed to measure the length changeof the liquid metal sensors.
D. Sensor-Actuator Interaction
To test the robot’s potential for automated control, we
measured the resistance of the strain sensors in each arm
in real time while actuating the robot using a constant air
flow. To measure the resistance in the gauges, we wired eachsensor as a resistor in a voltage dividing circuit. Becauseof the low resistivity of eGaIn (∼ 29.4 × 10−6 Ω-cm [23]),a small 2 Ohm resistor was added as the second resistor
in each circuit. Using a KORAD KA3005D DC power
supply we limited the voltage across the whole system to
0.7V to prevent excessive current draw through the low
resistance devices. To activate the robot, we attached it to
a compressed air source with a flow-valve control systemthat limited the gauge pressure to between 5 and 6 KPa
during the tests. We chose a low air pressure for the input
line to limit the air flow velocity and slow the actuationof the robot to the timescale of the test. During actuation,
we measured the voltage across the strain gauges using aTektronics TDS 2014C 4-channel input oscilloscope limited
to a 50 Hz measuring frequency. Although the oscilloscope
had 4 channels for input, one of the channels was dedicatedto measuring the voltage across an electronic gauge-pressure
sensor (Honeywell 001PDAA5, DigiKey), which provided
a digital marker that was used to synchronize the different
tests. This limited us to characterizing only three of the fourarms, which were picked at random from the robot.
At the beginning of each test we recorded the initialresistance for several seconds with the robot in its restingstate. Each characterization test then consisted of filling therobot with air for 5 seconds while measuring the voltage
drop across the arms, and releasing the air in the robot.
After the robot returned to its resting state, we recorded

Fig. 6. A comparison of the strain gauge’s percent increase in resistancefor one of the robot arms experiencing two modes of strain. The linear
extension tests were an application of unidirectional strain to the arm. The
error bars are a 90% confidence interval, and the red dotted line is a fit ofthe data. The other four tests were performed by pneumatically actuating
the robot and recording the resistance change, with the solid magenta line
representing a linear fit and the blue dashed line representing a curved fitof all the tests.

another brief dwell period before actuating the robot again.
We tested the robot actuating under two test conditions: therobot actuating in empty air (see Figure 1) and the robot
clamping a cylindrical object (see Figure 5) with a radius of
about 4.5cm. We filmed each test so that we could compare
the recorded data to events that the robot was experiencing.
The pressure sensor was connected to the robot’s input
valve, and indicated when the pressure in the infill tubechanged. There was a nearly instantaneous change after thevalve was opened, and the pressure remained constant during
the tests. The recorded initial pressure spike acted as a time
marker, enabling us to overlay all the tests we ran on the
robot under both testing conditions.
IV. R ESULTS AND D ISCUSSION
Change in resistance as a function of strain for two loading
conditions is shown in Figure 6. Engineering strain was
calculated from the extended length of the strain gauge. In
the case of the linear extension tests, this was measured
during the test. The extended length of the strain gauge
during pneumatic actuation was considered to be the arclength of the gauge, as seen from a profile view. The percent
change in resistance was calculated by dividing the actuatedresistance by the initial resistance.
These tests demonstrate our success in integrating thesensors into the robot’s body. Figure 6 shows that thestrain gauges are appropriately placed and return resultsthat correlate strain to a resistance change. There is a highdegree of repeatability in the results from both the uniaxial
tests and the pneumatic actuation tests. The curve fit to
the linear extension data is characteristic of uniaxial strain
gauges and agrees with the literature [20], [24], [25], [26].
The pneumatic actuation data also increases monotonically,2327

however, it does not display the quadratic response typicalof uniaxial strain loading. For clarity, we have included two
possible fits to the data: a linear fit and an inverse quadratic
fit. We note that a more in-depth model and expanded data
range would be needed to verify either trend. Nonetheless,the pneumatic actuation data has sufficient contrast fromthe linear extension data to indicate that the strain gauges
undergo a complex strain state that is not purely uniaxial
during actuation.
To analyze the data gathered during the gripping actuation
tests, we first applied a 20 point moving average filter toremove the noise. We then used basic electrical circuit theory
to calculate the resistance in each strain gauge based on
the measured voltage. We noticed that each of the robot’s
arms had a different initial resistance and electrical responseto actuation. To permit easy comparison between the threedifferent arms, we calculated the percentage rise of resistance based off of the total overall change that the sensor
underwent when gripping the cylinder. We present the resultsof our 3 tests with a 90% confidence interval in Figure 7.
Though a 50 Hz signal with a 20 point filter slows down
the response time of a control system, the inherently highcompliance in the soft robot tolerates imprecision in thecontrol while preventing damage to the robot or the object
being gripped, ideally simplifying feedback control.
Each arm’s strain gauge demonstrates a similar response
during the tests. When gripping air, there is a consistentincrease in resistance throughout the whole test. During theobject gripping tests, each sensor initially experiences thesame increase as the control, with a sudden departure fromthe trend when the robot begins to contact and grip theobject. The videos of the tests validate that at about 3s therobot begins to contact and grip the object, matching therecorded data (Figure 7). By keeping the inflow pressure controlled, the strain gauges provide feedback in a predictably
linear manner until boundary conditions change (an object
is encountered). Since the slopes of each section of the data
are constant, it is possible to distinguish the time that the
robot begins to grip an object. This performance is consistent
with the observations performed by Kramer et al. with theirliquid metal curvature sensors. By changing the boundary
conditions of their curvature sensors (from air to a rigid
joint), they experienced a magnification of the resistancechange by over an order of magnitude [16]. This means thatthe integrated strain gauges can be used to mark events (suchas gripping or other boundary changes) during the actuationof the robot.

(a)

(b)

V. C ONCLUSION(c)

We prototyped a pneumatically actuated soft robot withliquid metal strain gauges built into the soft robot’s body thatprovide real-time feedback during actuation of the robotic
system. Two simple tests on the robot provided strong
evidence of the validity of this integrated sensor design.
Under controlled conditions, the resistance in the strain
gauges changes in a predictable manner relating directly toboth the strain in the robot’s arms and the rate of actuation.

Fig. 7. The resistance change in three of the robot’s strain sensors during
the 5s actuation tests. Each graph (a,b,c) represents a different arm on the
robot. The test setup only permitted gathering simultaneous data from 3
arms, though the sensors on all 4 arms were fully functional. The solid redline is the average of the baseline tests (the robot inflating in empty air).
The dashed blue line is the average of the tests with the robot gripping
the test cylinder at approximately 3 seconds. The shaded area surrounding
each line represents a 90% confidence interval. The percent change wascalculated using the initial and final resistances of the tests with the robot
gripping an object.

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We also show that, under a constant input air pressure, we
can sense if and when the robot is gripping an object. The
modified fabrication method of these robots is outlined sothat this work can be easily expanded on in the future.
VI. F UTURE W ORKThis proof of concept robot demonstrates significant potential for many future projects that integrate sensors into thebody of a pneumatically actuated robot. It has the possibilityof finally closing the control loop and opens up a greater
potential to automate soft robots. Each sensors’ responseto actuation can be characterized independently, eventually
allowing for computer control to optimize the actuation ineach arm. Furthermore, since each arm provides actuationand gripping feedback independently of the other arms,
there is redundancy in the gripping information provided
to the robotic controller. Regardless of the complexity ofthe eventual model, the repeatability of our simple tests
demonstrate the potential to use the resistance response in
the gauge to generate a state reconstruction model for control
of a pneumatic robot.
We also believe that there are more possibilities for sensor
locations and types on a pneumatic robot like that presented
in this paper. In the future, we would like to integrate pressure sensors into the Silgard 184 bottom layer of the robot(which is the side that grips objects). This will provide asecondary sensory system that a computer controller can use
to determine if an object is being gripped and potentially how
much force is being applied to grip an object. These sensors
may also be able to provide a faster feedback response toa control system than our currently heavily filtered system.
This would free up the strain sensors to be primarily used
for arm position and location control.
VII. ACKNOWLEDGMENTS
ELW is supported under the National Science Foundation
Graduate Research Fellowship program (DGE-1333468).
Any opinions, findings, and conclusions or recommendations
expressed in this material are those of the author and do
not necessarily reflect the views of the National Science
Foundation.
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