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IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 3, NO. 3, JULY 2018

Toward Closed-Loop Control of Pneumatic Grippers
During Pack-and-Deploy Operations
Michelle Ching-Sum Yuen

, Trevor R. Lear, Henry Tonoyan, Maria Telleria, and Rebecca Kramer-Bottiglio

Abstract—Soft robotic grippers can grasp objects of many different shapes while tolerating misalignments. However, many current
soft systems lack proprioception and the ability to sense the position and orientation of the object to be grasped. Understanding
the configuration of a gripper’s fingers and the contact forces on a
grasped object is crucial for manipulating an object and for interacting with the external environment. In this letter, we instrumenta fabric-based pneumatic gripper with strain and pressure (force)
sensors. Using sensors affixed to removable sleeves fit over eachgripper finger, we identify the finger position and contact force on
a grasped object. We show that we are able to completely deflate
and pack the gripper, and then deploy the gripper and sensors tofull operation. Finally, we demonstrate closed-loop control of thegripper configuration using the strain sensors to reconstruct pose.
Index Terms—Soft material robotics, grippers and other endeffectors, hydraulic/pneumatic actuators.

I. INTRODUCTION
OFT robotic grippers operate by deforming and conforming
around objects to grasp them securely. This adaptability
is made possible by using soft and flexible materials such as
silicone elastomers [1]–[3], stretchable polymers [4], polymerbacked fabric air bladders [5], and hydrogels [6]. In this work, we
focus on pneumatic grippers constructed of fabric-strengthened
air bladders (Fig. 1). When compared to rigid robotic grippers,
two clear benefits emerge: 1) the gripper fingers can conformpassively to differently-shaped objects, and 2) the gripper canbe deflated, packed into a smaller volume, and then re-inflatedwith no loss in functionality. These benefits are advantageous

S

Manuscript received September 10, 2017; accepted December 23, 2017. Dateof publication January 31, 2018; date of current version February 22, 2018. Thisletter was recommended for publication by Associate Editor M. Rakotondrabe
and Editor Y. Sun upon evaluation of the reviewers’ comments. This work
was supported in part by the National Aeronautics and Space Administration(NASA), in part by the National Science Foundation (NSF), in part by a NASA
Small Business Technology Transfer Grant NNX16CA54P, and in part by aNASA Small Business Innovation Research Phase II Grant NNX15CA22C,
which supported the development of the gripper used in this work. The work of
M. C.-S. Yuen was supported by the NSF Graduate Research Fellowship underGrant DGE-1333468. (Corresponding author: Rebecca Kramer-Bottiglio.)
M. C.-S. Yuen and T. R. Lear are with the School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 USA (e-mail: yuenm@
purdue.edu; lear.trevor@gmail.com).
H. Tonoyan and M. Telleria are with the Otherlab Pneubotics, San Francisco,
CA 94110 USA (e-mail: henry@otherlab.com; mtelleri@mit.edu).
R. Kramer-Bottiglio is with the School of Mechanical Engineering, Purdue
University, West Lafayette, IN 47907 USA, and also with the School of Engineering and Applied Science, Yale University, New Haven, CT 06520 USA(e-mail: rebecca.kramer@yale.edu).
Digital Object Identifier 10.1109/LRA.2018.2800079

Fig. 1. The fabric-based pneumatic gripper conforming to grasp differently
shaped objects. Strain sensors are attached along the outside of each finger. Twopressure sensors are affixed to the inside of each finger near the tip and in the
middle.

in particular for volume- and weight-limited conditions, such as
space exploration missions and assistive technologies.
Soft grippers are a relatively well-developed field [7]–[9] andmany prototypes and even commercial products (Soft Robotics,
Inc.; SuperReleaser) have been developed. Most of these grippers are controlled in an open-loop mode, where the gripper iscommanded simply to either open or close, without feedback [9].
Similarly, the field of soft sensing is well-developed [10], [11],and commercial products (StretchSense, Inc.) have begun toemerge. The intersection of these two fields is less explored. It
has been argued that soft grippers may operate using openloop control because their intrinsic softness allows them toconform to different objects for a secure grasp, regardless ofobject shape, size, or mass [1]. However, this approach leads toa notable disadvantage: while an object may be securely grasped,
the orientation of the object within the grasp is not known tothe operator, limiting the utility of soft grippers in applications
requiring precision grasping and manipulation of objects. Furthermore, challenges in accurately modeling the behavior ofsoft pneumatic actuators limit the performance of model-based
open-loop control of such systems [9]. By adding sensors toa soft gripper, the configuration of the gripper fingers and thegrasped object can be determined, enabling closed-loop controlof the gripper.
Various researchers have begun to incorporate sensing alongside pneumatic actuation [2], [3], [12]–[20]. The sensors in these
prototypes have been used to detect strain for state reconstruction and contact or pressure for environmental knowledge. Strain
or bend sensors have been created using liquid metal resistive
strain sensors [2], [3], [12]–[15], commercial piezoresistive flex

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YUEN et al.: TOWARD CLOSED-LOOP CONTROL OF PNEUMATIC GRIPPERS DURING PACK-AND-DEPLOY OPERATIONS

sensors [16], magnetic Hall effect sensors [17], coiled wire forinductance-based sensing [19]–[21] and flexible optical sensors[22]–[24]. Touch-sensitive sensors have used capacitive sensors
with conductive fabric electrodes [18], capacitive sensors withionic hydrogel electrodes [25], and liquid metal resistive pressure sensors [15]. The large majority of the works cited here
utilize silicone elastomer to create a bending pneumatic actuator, allowing sensors to be embedded or adhered in place easily.
While this securely affixes the sensors to the actuator, failureof a single component results in system failure because components cannot be replaced without tearing the silicone elastomer
comprising the system.
The works cited above are focused on incorporating sensing
to enable closed-loop control of soft systems. However, few
systems have demonstrated robust feedback control strategies
for soft grippers and soft pneumatic actuators. External cameras
have been utilized to track various points on a continuum actuator to perform proportional-integral-derivative (PID) control of
the actuator configuration [26]. Pneumatic pressure sensors havebeen used to demonstrate proportional feedback control of finger
curvature in a soft glove orthosis [27]. This work was extended
through the addition of optical fiber curvature sensors on eachfinger of the glove to perform hierarchical PID control on thefinger curvature magnitude [23]. Inductance sensing integrated
onto bellows actuators has been used to perform proportional
control on the bend angle of the actuator platform [20]. Embedded piezoelectric transducers have been used for closed-loop
PID control of the force output from a pneumatic vibrotactile
skin [28]. These approaches utilize a variety of sensing modalities to perform PID feedback control of soft actuator systems,suggesting that with sufficient sensing capabilities, relatively
simple control strategies may be used to perform closed-loop
control of soft systems.
In this work, we instrumented a fabric-based pneumatic
gripper with highly deformable, capacitive strain and pressure(force) sensors to determine the position of and contact forcesalong each of the gripper’s fingers, respectively. Taking a modular approach, the sensors were adhered to removable fabricsleeves that fit over each gripper finger to facilitate sensor replacement upon design changes or sensor failure. We characterized the system to determine the strain sensors’ response to
the gripper configuration and the pressure sensors’ response
to load. Using this information, we performed several demonstrations to showcase the capabilities of the instrumented gripper: 1) pack-and-deploy operation, 2) grasp reconstruction, and3) closed-loop position control. Through the pack-and-deploy
operation, wherein the gripper was fully deflated, packed down,
and then re-deployed with no change in operational capabilities, we demonstrated the resilience of the instrumented gripper despite the large deformations incurred in this task. We
then showed that we can reconstruct the position of and loadon each of the gripper fingers during a grasping task. Finally,
we used the strain sensor information to perform proportional
closed-loop control of the gripper finger position. With the instrumented gripper system presented here, we progress towards
more precise grasps and finer manipulation capabilities without
sacrificing the benefits of soft grippers.

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Fig. 2. Photos of the sensors. A strain sensor is shown in the left column anda pressure sensor is shown in the right column. The full sensors are shown in the
top row and a close-up of the interface is shown in the bottom row. The signal
conditioning electronics (blue circuit boards) are interfaced to the sensors via
copper-clad polyimide strips. White polystyrene tabs hold the copper strips onto
the surface of the sensors’ electrodes and also serve as the mechanical interface
region between the sensor body and the gripper. The scale bars indicate 3 cm.

II. EXPERIMENTAL SETUP
A. Sensors
We fabricated two types of sensors in this work: strain andpressure sensors (Fig. 2). The sensor technology presented hereis closely based on our previous work [29]. The sensors wereconstructed as deformable parallel-plate capacitors that change
in capacitance with applied strain or pressure. In [29], we
demonstrated that the sensors have no change in performanceover tens of thousands of cycles and can withstand up to 375%
strain before failing due to degradation of the electrode material.
These characteristics make the sensors well-suited for repeateduse applications and allow for the sensors to be kept affixed to
the gripper during pack-and-deploy situations.
Both the strain and pressure sensors were fabricated using primarily silicone elastomer (DragonSkin 10 Slow, Smooth-On).
The two conductive layers were a composite material of 10wt%
expanded intercalated graphite dispersed into silicone elastomer[29]; the dielectric layers were composed of non-conductive
silicone elastomer. The strain sensors were fabricated by firstrod-coating a film of the composite material, followed by native
silicone to form the dielectric layer once the first layer had cured.
The film was then folded in half, bonding the silicone dielectric
layer to itself, to form the capacitive structure. Sensors were cut
out of the larger film with a laser cutter (VLS 2.30, Universal
Laser Systems).
For the pressure sensors, the electrode and dielectric layers
were made separately. The electrode layer was made as before
by rod-coating conductive composite material into a film. To
fabricate the dielectric layer, silicone foam was made by mixing in sugar pellets (Colorcon Suglets) of four size regimes
into uncured silicone elastomer. The quantities by mass were:
44 g silicone, 40 g 1400–1700 µm, 20 g 850–1000 µm, 30 g
500–600 µm, and 40 g 250–355 µm sugar pellets. The mixture
was cast into a ≈5 mm thick sheet, followed by dissolving out
the sugar pellets with hot water in a bath sonicator after curing.
This process resulted in a very soft, porous silicone foam with avoid volume fraction of 57.7% ± 2.6% (95% confidence). The

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IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 3, NO. 3, JULY 2018

Fig. 3. Results of the calibration tests for the strain sensors and pressure sensors. (a) and (b) Strain sensor gauge factors (n = 3) during gripper opening andclosing both before (a) and after (b) packing and re-deploying the sensors. (c) Pressure sensor gauge factors (n = 6). All gauge factors were calculated from a
linear regression fit across the captured data. The notch indicates the mean gauge factor; the upper and lower bounds of the box indicate the 95% confidence about
the mean. (d)–(f) Representative plots of calibration data and their regression lines: (d) plot of strain sensor outputs with respect to the ground truth joint angles
while closing the gripper, (e) plot of strain sensor outputs with respect to the ground truth joint angles while opening the gripper, and (f) plot of a single pressure
sensor’s output with respect to the masses of various wooden blocks placed onto the sensor; the cloud around the fit line indicates 95% confidence bounds of theregression.

conductive film was then glued onto both sides of the dielectric
layer using a 50 µm layer of uncured silicone elastomer.
After fabricating the sensor bodies, copper film was secured
onto the electrodes by sewing polystyrene tabs on either sideof the sensor. The copper film was then soldered onto signal
conditioning boards [29] used to interrogate the sensors’ capacitance. Finally, we affixed the sensors to off-the-shelf compression sleeves using adhesive-backed Velcro at the polystyrene
tabs. This modular system enabled us to replace sensors quickly
without compromising the entire system.
B. Gripper
The gripper consisted of three fingers attached to a rigid base.
There were two sets of bladders in the gripper: one for closing
the gripper by causing all the fingers to curl inwards, and one toopen the gripper by forcing all the fingers to extend straight. The
bladders were made by encasing airtight vinyl pouches inside of
ripstop nylon for strength. They were sewn to the inner surface ofthe fabric comprising each finger: the closing bladder was sewn
to the back of the fingers, and the opening bladder was sewn
to the inside of the finger. The closing and opening bladders
were connected via tubing such that for the entire gripper, there
was a single inlet for closing and a single inlet for opening
the gripper. At any one time, only a single bladder is inflated.

The gripper valves were plumbed such that when transitioning
from one state to another, one bladder fills as the other vents.
III. IN SITU SENSOR CHARACTERIZATION
We characterized the strain and pressure sensors after installing them on the gripper (Fig. 3). We compared the strain
sensor output to truth data generated by a motion capture systemand the pressure sensor output to truth data generated by placing known weights on the sensors. Both types of sensors had alinear relationship with respect to their inputs and thus, we report their gauge factors (Fig. 3(a)–(c)). These linear regression
results were then used to perform gripper proprioception andgrasped object localization in the applications discussed further
in this letter.
A. Strain Sensors
For the strain sensors, we found the relationship between
the sensor output and joint angle of each finger (approximatedas a 1DoF hinge joint) (Fig. 3(a), (b), (d), (e)). Truth data
(finger joint angles) were generated using a motion capturesystem where three motion capture markers were placed oneach finger: near the base, in the middle, and at the finger tip.
We compared the results across two conditions, for a total offour categories: 1) during opening and closing phases, and 2)

YUEN et al.: TOWARD CLOSED-LOOP CONTROL OF PNEUMATIC GRIPPERS DURING PACK-AND-DEPLOY OPERATIONS

before and after deflation and packing of the gripper (pack-anddeploy). Fig. 3(a) reports gauge factors from data captured prior
to packing; Fig. 3(b) reports gauge factors from data captured
after deflating, compacting the gripper down, and re-deploying.
The representative datasets plotted in Fig. 3(d) (closing) and
Fig. 3(e) (opening), show that the sensor response to joint angle can be reasonably approximated as a linear relationship (alllinear regression R2 values were between 0.963 and 0.998).
We first compared the gauge factors of each sensor during the
opening and closing phases in Fig. 3(a) and (b) and found that
there was little difference between the gauge factors during theclosing and opening phases for each sensor, particularly prior
to packing. We hypothesize that the differences in gauge factor
between the opening and closing phases are due to reduced contact between the sensor and the finger during the opening phaserelative to the closing phase. The difference in sensor behavior
can also observed in the plots in Fig. 3(d) and (e). Because thesensors are capacitors, changes in the near electric field, including contact with other bodies, will affect the sensor reading.
Shielding the sensors would aid in mitigating the effect of nearelectric field on the sensor signal.
We also compared the gauge factors of the strain sensors
found before (Fig. 3(a)) and after (Fig. 3(b)) a pack-and-deploy
sequence. One benefit of inflatable fabric grippers that distinguishes them from rigid grippers is their ability to be packed
into a much smaller volume and then deployed without change
in function. In order for the sensors to be compatible with thepacking and deployment of the gripper, they needed to withstand large amounts of deformation outside their normal operating range. The metric for this test was change in gauge factor
before and after packing. While two of the sensors clearly remained stable throughout the packing event, the sensor on finger
2 appeared to display different behavior. We do not believe that
this is due to damage to the sensor itself because the confidencebounds on the gauge factor are of comparable width before andafter the packing. Rather, we hypothesize that this change isdue to shifted and altered mechanical and electrical interfaces.
Work towards directly integrating the sensors into the fabriccomprising the gripper is a potential solution to this interfacing
problem. The fabric would hold the sensor with more stability
over large deformations, and would serve as an anchor point towhich electrical interfaces may be secured more robustly.
B. Pressure Sensors
The pressure sensors were calibrated by placing wooden
blocks of known weights onto them, yielding a highly linear
relationship between the pressure sensor response and applied
load. The gauge factors of all pressure sensors are reported inFig. 3(c) and a representative dataset is shown in Fig. 3(f). The
sensors were all tested in situ in their respective positions on
the inflated gripper to account for the stiffness of the gripper
finger in the pressure sensor output. Sensors 1, 4, 6 were located in the middle of the finger; sensors 2, 3, 5 were located at
the finger tip. We hypothesize that the differences in the gauge
factors, reported in Fig. 3(c), are due to minor differences inthe stiffness of the dielectric layers in different sensors. Further,
we hypothesize that the variation in width of the confidence

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Fig. 4. State reconstruction during a pack-and-deploy sequence. (a) Photos of
the gripper in the packed (left) and open state (right). (b)–(d) Plots corresponding
to the gripper first in a packed condition (0–5 s), followed by deployment into
a closed position (5–8 s), and then actuating to an open position (8–14 s).
(b) Sensor output filtered with a noncausal zero-phase digital filter. (c) Joint
angle of each gripper finger determined using the calibration equations foundpreviously (Section 3). (d) Ground truth joint angle of each gripper finger asfound using a motion capture system. The plots in (c) and (d) can be compared
to assess the performance of the sensory sleeves against ground truth.

bounds is due to experimental variation in placing the blocks
in the same location on the sensors, changing the effective areathat was compressed by the load, thus changing the measured
capacitance.
IV. APPLICATIONS
A. State Reconstruction During Pack-and-Deploy SequenceFig. 4 demonstrates a full pack-and-deploy sequence. Representative states during the pack-and-deploy sequence are shown1406

in Fig. 4(a). In the plots shown in Fig. 4(b)–(d), t = 0–5 s, t = 5–
8 s, and t = 8–14 s correspond to a packed state, a closed gripper
state, and a fully open gripper state, respectively. Fig. 4(b) shows
the raw sensor outputs during the sequence, Fig. 4(c) shows the
expected finger position calculated from the raw sensor data andpost-packing calibration curves, and Fig. 4(d) shows truth data
for the entire sequence generated via motion capture.
In comparing Fig. 4(c) and (d), the reconstructed finger position matches well to the joint angles from the motion capturesystem after the gripper is re-deployed. During the transition
from a packed state to a deployed state, the sensor readings are
not correlated with the actual finger position. However, this is
not a problem as the goal of this test was to ensure that we canstill use the sensor readings when the gripper is re-deployed
after having been deflated and packed down. More importantly,
after the packed phase, we found that the reconstructed joint angle tracks well to the ground truth joint angle (Fig. 4(c) and (d),respectively), reinforcing the result that the gripper and strain
sensors are resilient to the larger strains that occur while thegripper is in a deflated and packed configuration.
B. State Reconstruction During Grasping Tasks
Using the calibration results, we tested the sensors in a grasping scenario. We directed the gripper to grasp various objects
to simultaneously verify that the pressure sensors could detectan object, and that the strain sensors could be used to estimate
the state of the gripper fingers. As in previous tests, the pressure
sensors were placed on the inside of the fingers at the tip andat the center of each finger, and the strain sensors were affixedalong the outside of the fingers.
Fig. 5 shows state reconstruction of the gripper fingers during
a grasping task. In this example, the gripper grasped and thenreleased a water bottle. Fig. 5(a) shows the raw pressure sensor
data and Fig. 5(b) shows the expected normal load (presented
as mass) of the grasped object calculated from the raw sensor
data and calibration curve. The pressure sensors clearly detect
the water bottle being evenly gripped at the tips of the gripper
fingers. The sensors at the tips of the fingers (black, red, and blue
traces) show an increase in load corresponding to the grippingof the water bottle. Upon releasing the water bottle, these same
sensors show a decrease in load that corresponds to the waterbottle sticking to the sensors as the gripper opens, resulting in theelectrode layers being pulled further apart and the capacitanceto decrease momentarily before the object is fully released.
Fig. 5(c) shows images of the gripper in an open state andclosed state around the object.
Fig. 5(d) shows the raw strain sensor data, Fig. 5(e) shows the
expected gripper finger bend angles calculated from the raw datausing the post-packing calibration curves, and Fig. 5(f) shows
the actual figure bend angles as found from motion capture(truth data). While the reconstructed joint angle values do notmatch perfectly to the truth data, the strain sensors do track therelative motion of the fingers across the opened and closed states,
therefore yielding useful information about the general state of
the fingers and overall gripper. We believe that discrepancies
between the measured state and true state are due to the fingers

IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 3, NO. 3, JULY 2018

bending very close to the base, which is recorded by the motion
capture system, but is outside the ideal range of the sensor
measurement in the current configuration.
C. Closed-Loop Control
Using the strain sensors mounted on the fingers, we demonstrated closed-loop control of the fingers. The control system
consisted of nested proportional controllers (Fig. 6). The outer
position control loop generated the desired air pressures in thefinger bladders based on the difference between the commanded
and measured finger position, determined via the strain sensor
value. The positional set-point was based on a single sensor
value because all three fingers were plumbed together. We used
two different gains to calculate the air pressure commands
depending on whether the gripper was closing or opening
because much less pressure was need for closing compared to
opening the gripper due to biases in the actual construction of
the finger. The pressure commands were then fed into the inner
loops to independently control the air pressure in the opening
and closing bladders.
The plots in Fig. 7 show two representative runs: column (a)shows the gripper cycling through a closed and opened state,
and column (b) shows the gripper gradually stepping from aclosed state to an opened state. In Fig. 7, the top row shows
the setpoint (blue), and the sensor output from a single finger(red), and the bottom row shows the corresponding air pressure inside the bladders of the gripper. It is important to notethat the gripper was originally designed to have two states:
open and closed. Therefore the pressure-to-position relationship is poor, which makes it difficult to control the positionof the fingers using only air pressure data from the two chambers. By introducing strain sensors into the control loop, weovercame this limitation in achieving positional control of thegripper.
For this portion of the work, we changed the approach we
took to measure capacitance. The signal conditioning boards
previously used exhibited a very low frequency oscillation ataround 2.5 Hz which made them challenging to use for real-timeclosed-loop control. In non real-time tests the oscillation wasremoved by using a noncausal zero-phase digital filter. In realtime control however, low-pass filtering the signal at such a low
frequency resulted in unacceptable phase lag and delay. For realtime control, capacitance measurement was re-implemented directly on the gripper’s control board. The capacitive sensors
were charged using larger currents which resulted in less susceptibility to noise caused by small external electrical fields. In
addition, measurements could be made directly on the gripper’s
control board, instead of being transmitted over an analog channel as they previously were, thus eliminating the need for an
additional analog-to-digital conversion.
After improving the electronics, we used the capacitive strain
sensors for position feedback and pneumatic valves to control
actuation. As before, the system was plumbed so that all threefingers were controlled as one. Enfield LS-V25s 5 port proportional valves were used to control the pressure in the gripper
bladders. One valve controlled pressure to the bladder that opens
the finger and the other valve was connected to the bladder that

YUEN et al.: TOWARD CLOSED-LOOP CONTROL OF PNEUMATIC GRIPPERS DURING PACK-AND-DEPLOY OPERATIONS

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Fig. 5. State and contact estimation during an object grasping sequence. (a) Raw pressure sensor responses. (b) Reconstructed mass experienced by each pressure
sensor, as calculated from the calibration curves. (c) Photos of the gripper in its open state and closed state around a water bottle. (d) Raw strain sensor responses.
(e) Reconstructed joint angle of each finger, as calculated from the calibration curves. (f) Angles of each finger as determined by the motion capture system. The
spikes in the trace arise from loss of line of sight to one or more markers.

Fig. 6. Block diagram of the control scheme. As shown in (a), the controller consists of nested proportional controllers. The outer loop controls the finger
positions, and the inner loops (b) control the pressure supplied to the gripper’s two bladders. Gain1, Gain2, and GainValve are proportional gain constants. Ina), Calib. refers to the linear regression equation relating finger angle to sensor strain value found in the calibration of the sensors. In b), Pcalc. refers to the
manufacturer-specified conversion from voltage to pressure value.

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IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 3, NO. 3, JULY 2018

Fig. 7. Closed-loop control of the gripper. Column (a) shows the gripper performing two cycles of closing and opening, then finally closing. Column (b) shows
the gripper stepping from a closed state gradually to an open state. The top plots show the reference setpoint for the strain sensors in blue, and the measured strain
sensor values in red. The error generated between these two values are used to trigger the pneumatic valves to fill or vent as needed. The bottom plots show thepressure inside the opening and closing bladders.

closes the finger. The pressure of the inner and outer bladders
were measured using Honeywell 0-15psi range pressure sensors.
We note that the system does not fully match the setpoint values, however the gripper is clearly responding to changes in the
setpoint. We believe that further work in four areas will improve
the performance of the system: (i) using a more sophisticatedcontrol strategy, for example, using a PI or PID controller rather
than just a proportional controller [20], (ii) implementing thesensors on a joint which is designed to have a better pressure-toposition relationship, (iii) further improving the signal-to-noise
ratio of the sensors through the signal conditioning electronics,(iv) improved interfacing of the sensors with the host system.
V. CONCLUSION AND FUTURE WORKIn conclusion, we have demonstrated progress towards
closed-loop control of soft robotic grippers using highly deformable capacitive sensors on a fabric-based pneumatic gripper
for state measurement and object detection. The sensors wereconstructed as parallel plate capacitors with expanded graphiteand silicone composite serving as the electrode material andnative silicone or silicone foam serving as the dielectric material. By using a silicone-based construction, the sensors wereresilient to large strains encountered during pack-and-deploy
operations wherein the gripper was fully deflated and then redeployed. The strain sensors maintained a linear response underclosing and opening operations and the pressure sensors had alinear response to applied normal load, both before and after deflation and re-deploying. Using these sensors, we demonstrated
three unique applications of fabric-based pneumatic grippers:
pack-and-deploy operations, grasping of oddly-shaped objects,
and closed-loop position control of the gripper.

Future work on this system includes improvement of the signal conditioning electronics and sensor design, for an increased
signal-to-noise ratio. Further shielding on the sensors by wrapping a grounding layer around the entire sensor body will reduce
the effect of near electric field on the sensor signal. As mentioned previously, increasing the charge-discharge current used
to interrogate the sensors will also improve the signal-to-noise
ratio, as well as enable higher measurement frequency. Anotherimprovement would be to increase the spatial density of both thestrain and pressure sensors, so as to measure compound curvatures and more points of contact, respectively. Most crucially, an
additional strain sensor spanning the junction between the finger
and the base will improve the accuracy of the state reconstruction of the gripper. Increasing the density and areal coverage ofpressure sensors along the inside of the fingers will yield a better
understanding of what the gripper has grasped. Finally, underlying the need for more sensors is the need for better physicalinterfaces. In order to accommodate more sensors, the sensors
may be directly fabricated into the fabric itself. Not only will
this allow the sensing density to increase, the repeatability ofthe sensor output should increase as the sensors will not change
position during operation.
We will also explore more sophisticated control strategies toimprove on the performance of the closed-loop control shown
here. Our first step here is to implement integral and derivative
terms for PID control, as demonstrated by other researchers.
Another possibility is to control the pressure differential between the opening and closing bladders, such that they serveas antagonistic actuator pairs, similar to the bicep-tricep muscle
pair on the human body.
With this work, we have advanced the capabilities of soft
robotic grippers. Previous gripper devices have relied upon

YUEN et al.: TOWARD CLOSED-LOOP CONTROL OF PNEUMATIC GRIPPERS DURING PACK-AND-DEPLOY OPERATIONS

passive conforming to securely grasp objects. By instrumenting soft grippers with soft strain sensors and pressure sensors,
we can achieve closed-loop control during a grasping task togrip with known position, object orientation, and contact force,thus furthering the capabilities of soft robotic grippers.
ACKNOWLEDGMENT
Any opinion, findings, and conclusions or recommendations
expressed in this material are those of the authors and do notnecessarily reflect the views of the National Aeronautics andSpace Administration nor the National Science Foundation.
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