2019 International Conference on Robotics and Automation (ICRA)
Palais des congres de Montreal, Montreal, Canada, May 20-24, 2019

A Simple Electric Soft Robotic Gripper with High-Deformation Haptic
Feedback
Lillian Chin1 , Michelle C. Yuen2,3 , Jeffrey Lipton1,4 , Luis H. Trueba1 ,Rebecca Kramer-Bottiglio2 , and Daniela Rus1
Abstract— Compliant robotic grippers are more robust to
uncertainties in grasping and manipulation tasks, especiallywhen paired with tactile and proprioceptive feedback. Althoughconsiderable progress has been made towards achieving proprioceptive soft robotic grippers, current efforts require complex
driving hardware or fabrication techniques. In this paper, we
present a simple scalable soft robotic gripper integrated withhigh-deformation strain and pressure sensors. The gripper iscomposed of structurally-compliant handed shearing auxetic
structures actuated by electric motors. Coupling deformablesensors with the compliant grippers enables gripper proprioception and object classiﬁcation. With this sensorized system,
we are able to identify objects’ size to within 33% of actual
radius and sort objects as hard / soft with 78% accuracy.

I. INTRODUCTION
Robot grasping and manipulation are critical to futureinteraction with humans in real world situations, such ashome assistance or elderly care. However, everyday tasks canbe quite difﬁcult for traditional rigid manipulators becauseof the unstructured environments and diversity of objects
encountered in everyday operation. The intrinsic complianceof soft robots allows them to tolerate these uncertainties.
Their lower stiffness and continuous deformation allow soft
manipulators to more deftly grasp a wider range of objects
without complex control schemes or obstacle avoidance
measures [1, 2]. Soft grippers can further exploit their environmental interaction with the addition of soft haptic sensing,allowing greater understanding of the grasped object andexternal deformations of the hand itself [3].
Considerable progress has been made in creating theseproprioceptive soft robotic grippers [4]. Current approaches
include adding off-the-shelf components to the rigid components of a compliant gripper [5], embedding sensors
within the soft body of the hand [6, 7], or directly building
sensors through the manufacturing process [8, 9]. However,these solutions do not fully deliver on the potential of soft
grippers due to their bulky actuation schemes and fabrication methods. Most of these grippers rely on pneumatic
actuation due to its fast response time and high strengthweight ratio. However, the need for a pump, compressor
1 Computer Science and Artiﬁcial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. email: {ltchin,jlipton, trueluis, rus}@csail.mit.edu
2New

School of Engineering and Applied Science, Yale University,
email: {michelle.yuen,
Haven,
CT,USA.

rebecca.kramer}@yale.edu

3 School of Mechanical Engineering, Purdue University, West Lafayette,
IN, USA.
4 Mechanical Engineering Department, University of Washington, Seattle,
WA, USA.

978-1-5386-6027-0/19/$31.00 ©2019 IEEE

Fig. 1. Overview of the overall gripper design. Each ﬁnger of the gripper
is actuated by a servo to a pair of handed shearing auxetic cylinders(A). An elastic strain sensor along the outer curve of the ﬁnger provides
proprioceptive feedback (B). A pressure sensor along the inner curve
provides haptic sensing (C). We demonstrate how these components ﬁttogether on the gripper by removing the glove from the left ﬁnger (D).

and valves to convert electric power to ﬂuidic systems adds
considerable physical bulk and power inefﬁciencies [10].
This issue of complex driving hardware is present in otherpopular actuation schemes like cable-driven and shape memory alloy systems [11]. The single-material solutions avoid
this overhead of complex assembly, but rely on bespoke
manufacturing processes with low throughput, making theirpotential adoption limited [12]. There is a clear need fora soft robotic gripper designed with scalable actuation andfabrication methods.
To address this need, we present a fully electric, sensorized
soft robotic gripper fabricated via scalable manufacturing
techniques. By combining the compliance of the handed
shearing auxetic (HSA) structures from [13] and the highlydeformable strain and pressure sensors from [14], our gripper
is able to measure its own large deformation proprioceptively.
This allows it it to sense the difference between stiff vs. soft
objects and large vs. small objects, similar to how humans
understand an object’s stiffness through the combination of
the feel of an object and the inherent knowledge of thesoftness of our own ﬁngertips and hands. We characterize
this proprioception, detecting a ﬁnger bending angle of up to45◦ and identifying grasped objects’ diameter and stiffnesses.
The main contributions of this paper are:• creation and characterization of an electrically-driven
soft HSA-based robotic gripper with integrated softstrain and pressure sensors• a simple regression-based model that can estimate theradius of grasped objects to within 33% and distinguishhard vs. soft objects with 78% accuracy2765

II. RELATED WORK
Proprioceptive soft robotic manipulators generally fallunder one of two categories: (1) compliant grippers built
from rigid materials and ﬂexible drivetrains [15–17] and(2) ﬂuid-driven grippers made directly out of soft materials,
typically, silicone elastomers [1].
The majority of efforts to incorporate sensing into
rigid-material, compliant grippers have leveraged pressuresensitive pads on the ﬁngertips for force feedback [5, 18]or for classifying the grasped object based on size andorientation [19, 20]. Joint angle sensors have also been used
to reconstruct the pose of the gripper [5, 21]. While thecapabilities of these grippers have been enhanced by theincorporation of haptic feedback, they remain limited by theexistence of rigid linkages and pre-deﬁned joints.
Soft material-based grippers, however, leverage the softness of the actuation scheme and their bulk material properties to create a fully compliant system [1]. These grippers
tend to be ﬂuid-driven with interior chambers that upon
addition / removal of ﬂuid, close up and grasp around an object. Efforts towards sensorizing soft material grippers have
utilized liquid metal-based deformation sensors to track motion of the gripper ﬁngers [9, 22, 23], conductive elastomerbased strain and pressure sensors for haptic-based mappingof object contours [24], commercially-available bend sensors
to classify objects [6], and either conductive elastomer or
liquid metal-based sensors to control the degree of graspof the gripper [14, 25]. While these soft material grippers
have demonstrated success in passively conforming to grasp
objects, a ﬂuid-driven actuation system can be unwieldy anddifﬁcult to implement. Hence, despite competent sensory
feedback, the complexity of the actuation scheme limits theutility of the sensor information and the ultimate applicabilityof soft-material grippers in real-world environments.
In this work, we integrate a rigid-material, yet compliant
gripper structure with soft-material strain and pressure sensors. By using the HSAs, our gripper is motor-driven likerigid articulated systems, allowing us to avoid the issuesof controlling inﬁnite degrees of freedom commonly foundin purely soft systems. The stretchability of the sensors
allows the large deformations ( 50% strain) of the HSAs
to be measured. The design of both the HSAs and thesensors are highly dependent on their constituent materials,
much like how conventional ﬂuidic soft robots leverage theextreme stretchability of their materials to create motion. The
coupled choices of material and design allow us to create
a compliant, sensorized gripper with a simple design andactuation scheme, and efﬁcient use of materials.

Our ﬁnal gripper design (Fig. 1) achieved these goals by
combining the compliant electric actuators shown in [13]with the stretchable strain and pressure sensors demonstrated
in [14] to create a versatile novel gripper. The gripper ismade up of two ﬁngers that pinch together, with each ﬁnger
comprising a pair of HSA cylinders of opposite chiralities
that counterrotate against each other, driven by a multi-turnservo. The strain and pressure sensors were then mounted on
to each ﬁnger via 3D-printed caps and adapters. The pressure
sensors were placed along the inner curve of the ﬁngers,
while the strain sensors were placed on the outer curve. Since
the sensors deform with the ﬁngers, the effect on overall
displacement is minimal.
Each ﬁnger was then mounted to a 3D-printed adapter
made to ﬁt a Rethink Robotics Baxter robot used for manipulation. The distance between the ﬁngers was determinedby the mounting points of the Baxter, giving a open ﬁnger
distance of 9.5 mm. To add a third point of contact, a 5 mm
wide silicone-covered palm was mounted between the two
ﬁngers. Per DR3, this gripper is only made via standard
manufacturing techniques, such as laser cutting, rod-coating,fused deposition modeling 3D printing, and silicone casting.
A. Actuator Selection
In order to have actuators that satisfy design requirements
1-3, we chose the compliant electric actuators demonstrated
in [13] that are based on handed shearing auxetics. HSAs
expand on traditional auxetics (materials with a negative
Poisson’s ratio) by having a set chirality and net shear.
Thus, HSAs directly couple twisting motion with linear
extension. When two HSA cylinders of opposite chirality are
paired together, each cylinder opposes the other’s directionof twisting, allowing the pair to extend as a unit [26].
HSAs were the ideal actuators for our system because
by using traditional motors and servos, linear actuation andbending can be achieved, directly satisfying DR2. Furthermore, since any material that can support a pin joint or living
hinge joint can exhibit HSA material properties, we hadsigniﬁcant design space and possible material selection toachieve our desired level of compliance, addressing DR1.
For the cylinders in our gripper, we laser cut 60 mm
long, 25.6 mm diameter PTFE tubes with a 1.58 mm wall
thickness on a rotary engraver (PLS6.150D, Universal Laser
Systems). We cut our tubes with a tessellated pattern similar
to [27] with six base units around the circumference. To
grasp objects, we added an internal constraint layer to thepattern to allow out-of-plane bending similar to the standard
pneunet actuator [28].
B. Sensor Selection

III. GRIPPER DESIGN
To address gaps in current research, our primary design
requirements (DRs) for this gripper were:
1) a compliant system
2) directly electrically powered3) scalable fabrication techniques4) proprioceptive / tactile sensing

Once we were set on using an HSA-based gripper design,
the best sensor for our design goals were the strain andpressure sensors originally presented in [29] and [14]. Eachsensor is based on a capacitive stack-up of a conductive
charging layer, dielectric layers and conductive shielding
ground layers [30]. When stretched, the overlapping area
between the charging and ground layers increases, while the2766dielectric layer becomes thinner, resulting in an increase incapacitance. A similar effect occurs in the pressure sensors
as they are compressed.
These sensors were ideal for our gripper as they could
match the high deformations of the HSAs and provide
reliable results. By being soft themselves, the sensors would
increase ﬁnger conformation around a grasped object without
inhibiting any ﬁnger movement. The strain sensors had alsopreviously been shown to have a linear response to theirintended mechanical parameter over thousands of cycles,
ensuring that they would still reliably work for the lifetimeof the gripper [29].
The strain sensors in this paper were made from a ﬁvelayer capacitive stack-up while the pressure sensors were athree-layer stack-up (no ground shielding). The conductive
layers were made of an expanded graphite and silicone
composite material. The dielectric layers were silicone elastomer (DragonSkin 10 Slow, Smooth-On) for the strain
sensors, and a porous (≈ 65%) foam made from silicone
and sacriﬁcial sugar pellets (Suglets, Colorcon). Each sensor
was interrogated using a signal conditioning board whichcharges the capacitor for a ﬁxed amount of time, and thenmeasures the length of time it takes to discharge to 0.2 V.
This discharge time is a proxy for the capacitance, which inturn is a measure of the deformation applied.
Since there was signiﬁcant electromagnetic interference
from the servos, extra shielding was applied to the sensors
in the form of a thin (1/32”) silicone glove and copper tape.
This glove also helped hold the sensors in place and increase
contact friction between the gripper and grasped object. An
extra inﬁnite impulse response ﬁlter was also applied to thesensor reading to reduce noise outputs at the cost of a more
delayed response time.
IV. GRIPPER CHARACTERIZATION
When the gripper grasps an object, the grasped object
places external forces on the soft gripper, causing extra
deformation. We characterized the gripper in a non-grasping
environment in order to recognize these extra deformations
and use them for object size sorting (Fig. 2).
To connect our knowledge of given servo position, strain
sensor reading and overall ﬁnger bend radius, we track the
top and bottom caps of the unsheathed ﬁnger in an Optitrack
motion capture system while stepping each of the servos
from maximum open to maximum close back to maximum
open. The maximum open state of the ﬁnger was when theinternal living hinges were fully jammed against one another.
The maximum closed state of the ﬁnger was when the ﬁnger
began undergoing helical instability and twisted in on itself.
This instability is due to manufacturing variance between
the two cylinders, as one cylinder may be slightly more
compliant than another due to variations of the internal beamwidths and living hinge thicknesses.
Our reference frame in the Optitrack system is deﬁned by
the coordinate system deﬁned by the bottom cap, with X
pointing into the page, Y pointing vertically, and Z pointing
to the right of the ﬁgure. We measure the total movement

Fig. 2. Characterization of a single ﬁnger without load, which matchthe three states of the handed shearing auxetics – closed, linear regime
and helical instability (A). We correspond the servo position to the overallbending angle (B) and the on-hand strain sensor (C). The bending angle
is measured by taking the difference between the bottom and top plate’s
reference frames, as visualized in (A).

of the system by tracking how the reference frame of themoving top cap of the ﬁnger moves in relation to thecoordinate system of the bottom cap. Initially, both reference
frames coincide, but as the ﬁnger moves, the top reference
frames rotates about the bottom reference frame. The top
reference frame always has Y perpendicular to the planecreated by the top of the top cap, which is spanned by the
X and Z axes.
We conducted three open-close-open trials, giving us two
datapoints per trial for a given servo angle. For a given servo
rotation, we report the mean and standard deviation of all
six data points. Although we only report results for the left
ﬁnger here due to space, similar results can be found forthe right ﬁnger. The only changes are that the servo turns
in the opposite direction due to differences in chirality, and2767

that the strain sensors report a slightly different value at rest.
The overall structure remains the same.
From these trials, we found a fairly linear correlation
between the servo, the strain sensor and the overall geometryof the system (Fig. 2) (R2 values vary from 0.931 to 0.974).
As the servo rotates from its closed state to 13 degrees,
the ﬁngers elongate linearly up to about 25 degrees ofbending radius. This linear elongation is expected as theﬁnger constraint forces a constant curvature response fromthe ﬁngers. At the 25 degree mark, we see the beginningsof the helical instability of the HSAs come in, resulting ina twisting of the actuators in on itself, stopping bendingat nearly 45 degrees and leading to a pitch of 20 degrees(Fig. 2A). The strain sensor is able to capture this bending
and twisting quite well, matching the linear growth in angle,
tapering off after 15 degrees of servo rotation (Fig. 2B).
Since the rest state of the ﬁngers (where top and bottom
caps align) is at roughly 3 degrees of servo rotation, we
see several anomalous values. The negative bending angle
values from 0 - 3 degrees of servo rotation is an artifact
of measuring the overall rotation angle as the difference
between the top and bottom cap. Similarly, the anomalous
sensor reading at 3 degrees bending angle is an artifact of
the time averaging across datapoints. Thus, the strain sensors
offer a good model of the state of the HSAs and can be used
to approximate the state of the gripper.
V. OBJECT SORTING
To get a better sense of the sensor values in actual
grasping use, we grasped a series of test objects with known
stiffnesses and geometries and recorded the sensor readings
during grasping (Fig. 3). We are interested in evaluating how
well the gripper can bucket different objects into categories
of “large vs. small” and “soft vs. stiff”, similar to humancategorization.
Our manipulation targets (Fig. 4) were drawn from theYCB Dataset [31] and other sources to get a wide range ofcompliance (1 - 103 MPa) and size (20 - 55 mm in radius).
For each object, we measured the cross sectional diameterand had three people rate the compliance of each object on
a 1-10 scale, 10 being the most compliant. The average ofthese measurements was taken as the overall compliance ofthe object.
We chose to evaluate compliance on a simpliﬁed humanrated scale rather than Young’s modulus or weight to moreclosely simulate human proprioception of classifying objects
by binning on a scale. Furthermore, since the pressure
sensors directly measure the grasp normal force, stiffness andweight only provide a partial capture of the actual overall
compliance of the object. Objects with the same human
compliance rating may not be the same stiffness, but fall
into the same “hard” or “soft” categorization.
To minimize variance involved in grasp planning, weplaced all of our objects manually in a set grasping conﬁguration and had Baxter run through a preset graspingroutine. Preset servo values are used for the gripper’s open
and closed states. After the gripper closed, the pressure andFig. 3. Four representative objects grasped (A) and the correspondingstrain (B) and pressure data (C) received from the sensors on the rightﬁnger. These objects were chosen as they represent the extremes of ourdatasets’ compliance and size.

strain values are recorded. Each object is then picked up andshaken to determine robustness of grasp hold. We recorded
data before shaking as the extra motion caused shifts in thegrasping normal forces, changing the pressure sensor values
dramatically. Although this pressure differential between thetwo ﬁngers could be used for future object pose estimation,
this is outside the current scope of the paper.
To get a baseline from which to evaluate the rest of theobjects in terms of our four parameters (small, large, stiffand soft), we select four representative items – a magic 8ball (compliance 1.3, diameter 107 mm), a deﬂated football(compliance 8.7, diameter 89 mm), a foam brick (compliance
9, diameter 43 mm) and a whiteboard eraser (compliance2.3, diameter 51 mm) – and measured the sensor response(Fig. 3).
From the strain sensor, we see that the smaller objects had
similar strain proﬁles as the ﬁngers are able to mostly achieve
their normal closed state. The slowly rising sensor response

2768

Fig. 4.
Objects used in grasp testing. Our gripper could pick up the
43 objects in group (A) and could not pick up the 13 objects in group(B). Plotting compliance rating vs. radius reveals that our gripper has most
difﬁculty with small stiff items (C), due to the larger force necessary to
hold a small graspable area stably

stems from the sensor’s transition to a higher strain as well as
the increased noise ﬁltration. The two larger objects caused
a lower strain in the sensors as the ﬁngers remained mostly
open from the large object. We do note, however, that thestrain sensors can not meaningfully tell the difference incross section between the two large objects (a difference of
18 mm), while there is a small difference in strain between
the smaller objects (a difference of 8 mm).
From the pressure sensor, we found that the larger objects
have a much higher response than the smaller objects. Thisis probably due to the increased contact these objects havewith the pressure sensor, causing the dielectric to compress
over a larger area and the capacitance to increase. For asimilar reason, it seems that for a given object size, more
compliant objects tend to have a higher measured pressure as
they can conform more easily to the pressure sensor, leading
to an increase in affected area. We see this dramatically bycomparing the football’s reading to the 8-ball. However, as
the item gets smaller, the contact footprint with the gripper
shrinks as well. Since smaller objects tend to be grasped witha pinch grasp rather than a power grasp, the resulting effecton capacitance also tends to be diminished. Thus, we canestimate compliance for large objects more effectively thanfor smaller ones.
Using the results from the representative objects, we
linearly interpolated the strain sensor values for the representative objects to allow our program to estimate size. We
then used this size estimate with the pressure sensor readings
to provide an estimate of compliance, performing a linear
interpolation with a factor for size. We then ran grasping
tests on the other 52 objects in our data set and recorded
estimated size and stiffness.
In our experiments, we were able to grasp 77% of the
56 objects tested (Fig. 4). We generally had difﬁculty withsmall stiff objects which required pinch grasps (ex. plastic
strawberry, small rubber duck) and heavy objects (ex. wood,
drill). While small objects in general were difﬁcult for ourgripper due to the smaller area of graspable regions, small
stiff objects were even more difﬁcult as they needed a larger
force to hold stably. Similarly, the weight of the heavier
objects would be larger than the contact friction, causing theobject to slip out of grasp. We could improve our graspingwith both sets of objects by reducing slip – whether byclosing the gripper more completely, increasing the gripper’s
contact friction, increasing the grasping force, or sensing slip
directly.
Of the objects we could grasp, since the estimates werebuilt off of an interpolation of four object sensor readings, wenoted a wide range of estimated compliance that affected ourevaluation. For very soft objects, our estimated compliance
coefﬁcient often was greater than 10, so we capped thosevalues at 10. We also found that objects with metal in them
reported an extremely large negative estimated compliancerating, ranging from -18 to -91. Since our sensors are
based around capacitive sensing, coming into contact withmetal objects would overwhelm our sensors and provide a
nonsensical response. This effect was present even in objects
with a slight amount of metal that was covered by plastic (ex.
video game controller, coffee tin). While this effect could
be useful for future object sorting, especially since living
tissue also modiﬁes the capacitance signiﬁcantly, objects withnegative compliance ratings (7 in total) were not used in ourevaluation of the estimated compliance rating. These objects
were still used in radius evaluation since the strain sensors
did not come into contact with objects.
From the 43 objects we could grasp, we were able toestimate the cross sectional radius to within 33% accuracy(Fig. 5A). Our system had a slight bias, tending to underestimate the radius of objects with radius lower than 30 mm,
and overestimating the radius of objects with radius greaterthan 40 mm. This effect may be an artifact from the linear
interpolation performed from the four representative objects
and may be mitigated with a more complex interpolation.
From the 36 non-metal objects we could grasp, we wereable to tell the difference between hard and soft objects
with 78% accuracy (p-value = 0.0003) (Fig. 5B), where soft
objects were deﬁned as those with compliance rating below
5 (the midpoint of our rating system). A binary classiﬁcation
was used to evaluate our classiﬁer as the capped estimated2769

noodle (radius 29 mm, compliance 8) and the rolling pin(radius 25 mm, compliance 3) optically appear very similar
and were close enough in radius to potentially have their size
differences be lost through noise. However, our estimatedcompliance for these objects were 9.4 and 1.4, respectively,a signiﬁcant and sufﬁcient difference to tell the objects apart.
VI. CONCLUSIONS AND FUTURE WORK

Fig. 5. Comparison of the estimated grasped object properties with groundtruth based on strain and pressure sensor readings. The estimated radius
showed a tight linear correlation with the actual radius (A), while theestimated compliance rating could do a binary classiﬁcation of hard vs. soft
objects (B). In both graphs, the dark gray line is y = x, where estimatedequals actual measurement. In (B), the size of the marker corresponds tothe size of the grasped object while the dashed lines represent the “hard /soft” boundary.

compliance meant that more soft items were classiﬁed as 10,
making the system not have as much granular resolution as
desired.
We also found a slight correlation in compliance accuracy
to the size of object. As expected, our system was able tomore accurately tell an object’s compliance when the object
was larger as the difference between a stiff and soft object
would be more pronounced. We can see this slight effectin Fig. 5B, where the smaller sized markers tend to befurther away from the ground truth line, especially for more
compliant objects.
Although the estimated compliance ratings did not matchas closely to the actual ratings as the radial measurements
did, the binary classiﬁcation was still sufﬁcient to distinguish between similarly sized objects. For example, the poolIn this paper, we demonstrated a new soft gripper with
proprioceptive sensing capabilities that is fully electricallyactuated. By designing the gripper with scalability in mind,
this gripper avoids the bulkiness of ﬂuidic driven soft
actuators and the stiffness of rigid-material grippers withcompliant drive-trains. This gripper is able to grasp a wide
range of objects with a simple open-loop control scheme andsort these objects based on object radius and compliance.
Out of 56 total objects, 77% could be grasped and have size
estimated to within 30% of actual size, while 78% of nonmetal objects could be classiﬁed as hard / soft with 78%
accuracy.
Future work on this system will focus on better understanding and improved design of the pressure sensors
to address more complex applications. Although the pressure sensors show potential and sufﬁcient sensitivity, morecharacterization is needed to fully understand what affects
their measurements. For example, the pressure sensors are
sensitive enough to detect in-hand object shifting between
the two ﬁngers, but we do not yet know how to characterize
the motion. Creating an addressable array of pressure sensors
would be a good ﬁrst step to answering these questions.
We would also like to integrate the tactile information ofthis gripper with existing vision-based manipulation systems.
Supplementing a visual inspection of grasped object withdirect tactile information could lead to more dexterous manipulation and less time spent training to recognize how tograsp new objects.
ACKNOWLEDGMENTS
This work was completed with support from Amazon, JD,
the Toyota Research Institute (TRI), and the National Science
Foundation, grant #1830901. LC was supported under the
National Science Foundation Graduate Research Fellowshipgrant #1122374, the Paul & Daisy Soros Fellowship for New
Americans, and the Fannie and John Hertz Foundation. MCY
was supported by the Laura Winkelman Davidson Fellowshipfrom Purdue University and a NASA STTR Phase II contract(80NSSC17C0030). This article solely reﬂects the opinions
and conclusions of its authors and not that of its sponsors.
The authors would like to thank Jonathan Tagoe for improving the process for laser cutting the HSAs, and Jonathan
Zong for helping with data collection.

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