2019 2nd IEEE International Conference on Soft Robotics (RoboSoft)
COEX, Seoul, Korea, April 14-18, 2019

Automated Recycling Separation Enabled by Soft Robotic Material
Classification
Lillian Chin1 , Jeffrey Lipton1,4 , Michelle C. Yuen2,3 , Rebecca Kramer-Bottiglio2 , and Daniela Rus1
Abstract— Single-stream recycling is currently an extremelylabor intensive process due to the need for manual object
sorting. Soft robotics offers a natural solution as compliant
robots require less computation to plan paths and grasp objects
in a cluttered environment. However, most soft robots are notrobust enough to handle the many sharp objects present in a
recycling facility. In this work, we present a soft sensorized
robotic gripper which is fully electrically driven and can detectthe difference between paper, metal and plastic. By combining
handed shearing auxetics with high deformation capacitive
pressure and strain sensors, we present a new puncture resistant
soft robotic gripper. Our materials classifier has 85% accuracy
with a stationary gripper and 63% accuracy in a simulated
recycling pipeline. This classifier works over a variety of objects,
including those that would fool a purely vision-based system.

I. INTRODUCTION

Fig. 1.
Demonstration of the soft robotic gripper holding a spiky
pincushion, demonstrating its resistance to puncture and lacerations. Eachfinger of the soft robotic gripper is made of a pair of handed shearing auxetic
cylinders and high-deformation capacitive pressure and strain sensors. The
gripper can grasp objects and detect their base material without fear of sharp
objects.

Soft robotics has the potential to transform recycling
through automated object sorting. Although environmental
and sustainability concerns have made it crucial to scale
up recycling operations, object sorting remains a criticalbottleneck for recycling scalability. Failure to properly sortmaterials for recycling leads to waste; in the United States,
25% of all recycled materials are so contaminated theymust be sent to landfills [1]. Single-stream recycling, whilemore convenient for the consumer, has a higher rate ofcontamination than presorted dual-stream recycling [2].
Although some recycling centers have automated sorting
systems, such as eddy currents and magnets for metals [3],and visual inspection for plastics [4, 5], most facilities still
employ large amounts of manual labor to grasp and sortobjects that escape automation. This can lead to unsafe
working conditions, especially in facilities where normal
waste is mixed with recyclables.
Relying on a purely optical object sorting process also
introduces inaccuracy since material type is not a visualproperty, but a tactile one. As a greater push is madefor increased sustainability, durable versions of previously
disposable items are increasingly more common and visuallyindistinguishable from the disposable versions. Metalliclooking plasticware or a reusable plastic coffee cup could

be difficult to distinguish purely from an image but straightforward to detect when grasped. Thus, a robot which cangrasp a wide variety of items and tactilely classify them as
metal, plastic or paper would improve the purity of recycledmaterials, remove dangerous working conditions for people,
and create a more sustainable economy.
Soft robotic grippers are a natural solution for automatedrecycling. In a recycling facility, it would be difficult to
calculate a plan for a traditional rigid gripper to grasp the thedistorted and damaged objects running past on a conveyor
belt. However, with a complaint gripper that conforms to an
object’s surface, the computational requirements to grasp an
object are significantly reduced. Rather than scan an object,calculate directions and forces and precisely execute a plan,a soft gripper allows imprecise hardware and simple software
to pick up a wide range of objects [6, 7].
Although traditional fluidic-driven soft grippers may beable to handle the variety and complexity of objects in a
recycling center, their susceptibility to puncture makes them
not robust enough for a recycling line. Given the manypotentially sharp and jagged objects in a waste stream, asingle scrape could damage the gripper and force the line
to be stopped. Even without mechanical damage, repeatedactuation can still lead to pressure-driven tearing and bursting of the internal walls [8]. Although self-healing fluidic
actuators are being investigated, these methods either requiresignificant time to heal or require specific geometries to

1 Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. email: {ltchin,jlipton, rus}@csail.mit.edu
2New

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

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.

978-1-5386-9260-8/19/$31.00 ©2019 IEEE102

allow for fiber reinforcement [8, 9]. An electrically driven,
non-inflated soft robotic gripper would avoid puncture issues
entirely and be more appropriate for operation in harshenvironments.
To address this need, we present a soft tactile-sensing
gripper that is puncture resistant, capable of operating in
dangerous environments, and can sort objects by material(Fig. 1). This gripper is built with handed shearing auextic(HSA) actuators and soft capacitive silicone pressure andstrain sensors [10, 11]. This gripper has been demonstrated
to accurately sense object size and stiffness [12], which
we then extend to create a material-based classifier to sortobjects as paper, plastic or metal. We characterize thisclassification algorithm and demonstrate its viability on amock recycling setup, correctly classifying 27 objects with
85% accuracy with a stationary gripper and 63% accuracy
on the recycling setup.
In this paper, we (1) demonstrate the functionality andpuncture resistance of an electrically driven soft robotic
gripper with integrated soft haptic feedback, (2) develop an
algorithm to classify paper, plastic and metal objects basedon sensed object stiffness and size, (3) use this algorithmwith the gripper to sort through typical recycling objects,
including pathological cases for a purely visual detector.

by looking at the deformation of known patterns undercontrolled lighting [19–21]. Although all of these sensorization techniques have relied on puncture-susceptible fluidic
actuation which is inappropriate for a recycling context, they
provide an excellent guide for the design of our gripper.
III. SYSTEM DESIGN
A. Actuators and Sensors
In order to have a soft robotic gripper that was resilient
against puncture, we used handed shearing auxetics (HSAs)
as the actuator basis (Fig. 2). Through their internal geometric structure, HSAs tightly couple twisting with extension
so that linear actuation can be achieved with a conventional
motor [10]. Since HSA actuators are not fluidically driven
and do not require a continuous surface to function, they
are significantly faster, more efficient and more resistant topuncture than traditional fluidic actuators [11]. By pairing
two HSAs with opposite handedness together and rotatingthem against each other, the pair will extend and contract asa single unit. If an internal constraint layer is placed within
the pattern, out-of-plane bending can also be achieved.
Each finger in our gripper is made of a pair of HSA
constrained cylinders, laser cut out of 60 mm long, 25.6 mm
diameter PTFE tubes with a 1.58 mm wall thickness on arotary engraver (PLS6.150D, Universal Laser Systems). Theinternal pattern was similar to the one used in [11], but withsix base units around the circumference instead, giving asmaller bend radius and a more compliant gripper.
To match the resilience of the HSA actuators, we sensorized the gripper by adding high-deformation capacitive
silicone strain and pressure sensors [22, 23]. By layering
conductive silicone with dielectric foam or non-conductive
silicones, a capacitor sensitive to mechanical deformations
is created. As these sensors get stretched or pressed, thecapacitance will change as the distance and area between the
conductive layers changes. Since the sensors are primarily

II. BACKGROUND
Single-stream recycling results in a considerable increasein adoption and set-outs for recycling over multi-stream
recycling, but results in higher contamination rates of nonrecyclable items [2, 13]. This forces recycling centers (material reclamation facilities or MRFs) to efficiently sort singlestream recycling. All MRFs require manual labor in thesorting process, whether to pre-screen the waste stream fora fully automated MRF or as the primary sorting force in a
waste stream mixed with conventional trash.
Given a single waste stream, most MRFs use high magnetic fields and eddy currents to separate out metals andnear-infrared light with an air ejection system to sort specificplastics from one another. However, these processes will
fail as soon as an object not within the expected dataset
is fed through, such as metal-embedded plastics [14]. This
forces human workers to serve as pick-and-place filters fora nominally fully-automated process . Research on recycling
automation does not address this issue, instead focusing on
separating increasingly similar materials from each other,such as paper grade and particle separation [15, 16].
While research specifically into robotic systems for recycling automation has been limited, research into soft robotic
grippers with tactile sensing has been extensive in the last
few years. Embedding strain and pressure sensors within
soft grippers has become quite popular, relying on variations
in resistance or capacitance to determine contact forces
and length changes. These approaches range from off-theshelf sensors to experimental materials such as liquid metal,
conductive elastomers or fluidic ionic conductors [6, 17, 18].
Other soft sensors convert the tactile sensing problem into
a vision problem, whether through optical waveguides orFig. 2. Overview of the soft robotic gripper. We remove the glove on the
top finger to reveal the internal layers on the table. Each finger is made ofa pair of handed shearing auxetics (HSAs) with contrasting chiralities. On
the back of the fingers is a strain sensor, while the inside of the fingers has
a pressure sensor. Silicone pads and gloves help increase contact, while apaper backing provides a surface for the pressure sensor to push against.

103

made of silicone and foam, they can match the high deformation of the HSAs without affecting the overall gripper’s
performance. The sensors are also resilient to puncture; aslong as the capacitive stack-up is maintained, a hole in asensor will simply change the measured capacitance but notprevent the sensor as a whole from working.
The pressure sensors were a three-layer capacitive stack-up
constructed with two conductive layers made from a silicone/ expanded graphite composite and an internal dielectric foamlayer made from silicone and sacrificial sugar pellets (Suglets, Colorcon). The strain sensors were a five-layer stack-up,
adding an extra two layers of ground shielding and using
a silicone elastomer for the dielectric layer (DragonSkin
10 Slow, Smooth-On). To measure the capacitance (and by
proxy, the deformation), each sensor was attached to a signal
conditioning board which charges the capacitor for a set timeand reports how long discharge to a fixed voltage takes.
B. Sensorized Gripper Integration
Mechanically integrating the actuators and sensors to forma gripper is a non-trivial task. Since the HSA-backbone of
the gripper’s fingers are two counter-rotating Teflon surfaces,
sensors cannot be placed directly on the actuators, leading
to tricky integration concerns. The strain sensor was bolted
along the outer curve of the fingers to the bottom and top
finger caps, while the pressure sensor was mounted solely
to the top finger cap along the finger’s inner curve. Bothsensors were encased inside a silicone glove to reduce noisefrom the environment. The entire gripper was mounted toa Rethink Robotics Baxter robot via 3D-printed adapters. A
silicone palm was placed in between the two fingers to help
provide a third point of contact.
Although a similar soft gripper design was used in [11, 12],significant improvements were made in order to improve thesensitivity and resolution of readings for material classification, primarily for the pressure sensor. Previous designs
relied on simply letting a neoprene foam liner or soft pressure
sensor rest directly on the HSA actuators within the glove.
This caused excessive noise due to close proximity to theservos and a lack of a stiff surface for the pressure sensors
to press against.
To solve these problems, we needed to change the way thesensorized foam interacted with the HSA. Behind the foam,
we placed a thin piece of paper with a silicone tab. These
additions created a consistent backing for the sensors with aslight bulge at the tip, ensuring sufficient contact between thesensor and item being gripped. We further strengthened thiscontact by adding a slight opening to the silicone glove at thesensor bulge. This ensured that the sensor could consistently
read the capacitance changes that came from contact with
objects.
To address noise concerns from the increase in pressure
sensor exposure, we shielded the sensor readout electronics
with extra insulation and a grounding copper tape layer. This
allowed us to use a more permissive filter on the sensor andincrease the resolution of the sensor’s operating region. We
also mounted the control electronics directly to the arm itself,

Fig. 3. Training objects used and a scatterplot of the two salient features
over ten grasping tests for each object. The x-axis is the average of the two
finger’s pressure readings, divided by the estimated object diameter. The
y-axis is the difference of the two fingers’ pressure readings, divided by theestimated object diameter. These two features provided sufficient difference
that the plane could be divided up into three regions corresponding to metal,
paper and plastic. The datapoints for the metal cup were very large in bothmean and variance, and were omitted in order to view detail.

creating a more self-contained package to reduce power railinterference.
C. Material Classifier
In order to extend our object size and stiffness measurements from [12] to apply to material properties, we would
have to perform a more rigorous classification of compliance.
Rather than simply perform a simple regression to map
measured sensor values to compliance ratings, we use sensors
in both fingers to create a more complex feature set that ismore robust to variations between objects. We use traditional
human-defined feature selection and linear discrimination tosort objects by material type.
For our classifier, we first calibrate the gripper’s sensors
by going through an open / close sequence with no object
in grasp. This gives us a baseline strain and pressure sensor
reading that we can normalize against. Then, we estimate
the size of the object. This size estimation is a simple linear
regression determined by a set of training objects from [12].
This gives us a mapping between the fingers’ strain sensor
readings to the size of the object.
Next, we close around the object and measure the two104

pressure sensors. We then take the average and difference
between the two sensor readings to give us a “mean” and“variance” features. We expect this “mean” and “variance”
to vary between soft and stiff objects as softer objects havemore give and will have more variance between the fingers
as they push against each other, but a lower force required
to hold the object. We also normalize these features against
object size. As object size increases, we expect a larger areaof deformation on the pressure sensor, resulting in a larger
capacitive difference. We want to divide out this effect toensure that different sized objects can be compared.

Algorithm 1: Sorting Algorithm
Calibrate hand by recording sensor values upon open
and close;
while True do
Items move along conveyor belt;
if IR breakbeam is broken thenConveyor stops;
Baxter moves to break beam location andgrasps object;
Read strain / pressure sensors and normalize
them based on calibrated open / close;
Use linear regression on strain sensor values toestimate size;
Calculate average and difference between
pressure sensor readings, dividing by size;
Sort via classifier to determine material type;
Place object into appropriate bin;
end
end

IV. EVALUATION
A. Classifier Characterization
Using the “mean” and “variance” feature set outlined
above, we take six objects, two of each material type,to calibrate our classifier (Fig. 3). We grasp each of thecharacterize objects ten times and plot the measured mean
and variance on to a two-dimensional feature space, allowing
us to hand-derive linear discriminating functions for material
classification. Since there is some ambiguity around theboundaries of our discriminating functions, we verify ourclassifier is accurate by grasping assorted unseen objects witha stationary gripper (Fig. 5A and B).
Among the 14 objects used for testing, we found that ourclassifier correctly identified 85% of the objects. The objects
that our classifier had difficulty with were for paper-covered
metal tins. For both of these objects, our classifier returned”paper”, suggesting that the coating may have provided
enough insulation for our gripper to not properly identify
the objects.
Although we explored other classification techniques suchas k-nearest neighbors and support vector machines, themanual feature selection gave the best classification results.
More specifically, for a stationary gripper, we achieved 85%
accuracy with manual feature selection vs. 23% accuracy
with nearest neighbors. This big difference in accuracy isprobably due to the low number of datapoints from thecharacteristic object set and sensor information.

Fig. 4. Overview of the overall recycling test setup. Items come down
the conveyor belt line until one hits the infrared break-beam, signaling therobot to grasp the object and sort it appropriately.

probably due to the close intermingling of paper and plastic
objects from our characteristic objects’ feature space. The
lower accuracy rate in the recycling task compared to thestationary task is probably due to sensors shifting as therecycled object is moved. This would cause our calibration
to be off from what we expected, leading to more inaccurate
predictions over time. The presence of the conveyor belt’s
motor and large metallic body may also cause inaccurate
readings as these may be a source of electromagnetic interference.

B. Recycling Task
To demonstrate the full capabilities of our gripper, we
use the gripper in a simulation of a MRF recycling line.
The goal of this demonstration is to take objects off of aconveyor belt and place them in the correct bin using a Baxter
robot equipped with our gripper (Fig. 4). The conveyor belt
moves objects towards Baxter’s workspace, stopping when
the object crosses an IR break beam, which also signals to
Baxter that the object is ready to grasp. Baxter will thengrasp the object and classify the object as described in theclassifier (Algo. 1).
A picture of all objects tested with can be seen in Fig. 5C.
Over three tests, we averaged 63% accuracy in identifying
the materials of our thirteen test objects, averaging one pickevery six seconds. We correctly identified all metal objects,
the paper cup and the plastic Starbucks cup across all three
trials and had the most difficulty with paper objects. This is

C. Puncture Resistance
To test puncture resistance, major and minor puncturedamage was performed on both a traditional silicone-based
fluidic actuator and our gripper (Fig. 6). Minor damage wasmodeled by scraping the side of a freshly opened metal canagainst the finger’s internal curve, while major damage was105

Fig. 6. Qualitative evaluation of gripper performance under (A) normal
conditions, (B) minor damage due to incidental lacerations, and (C) major
damage for our soft gripper due to intentional puncture with a pin. Gripper
functionality is unaffected in all cases, even after repeated damage.

Fig. 5. Objects used to evaluate the classifier. (A) and (B) were used totest a stationary gripper, while (C) were objects used in the full recycling
setup. The objects in (A) were successfully classified while the objects in(B) were unsuccessfully classified.

In order to make this system robust enough for deployment
to MRFs or other recycling facilities, further work is needed
to strengthen the classifier and the system overall. To improveclassifier accuracy, incorporating more sensors, especiallyalong the contact surface, could provide more information
to use more advanced classification algorithms. In particular,
greater contact resolution would allow the classifier to geta better understanding of the overall geometry of the object
beyond just the size. On a system level, this gripper system
will need to be incorporated with vision-based technologies
to improve accuracy and allow picking in cluttered environments. Integration with vision would also allow futuresystems to have a wider range of detectable objects, including
glass and non-recyclable materials.
Despite the preliminary nature of our work, this soft
robotic gripper offers great potential for improving recycling efficiency. Although the classifier is not perfect inperformance, any improvement to upstream sorting can havesignificant quality improvements downstream. Furthermore,
given the compact nature of our system, it may be possible
to deploy it further upstream than MRFs, allowing largecommercial and residential buildings to presort their personal
single stream waste and reduce contamination rates. Eitherway, it is clear that soft robotics can help improve recycling
and build a more sustainable economy.

modeled by taking a 1 mm diameter pin and puncturing
the gripper across the finger’s internal curve. Key functional
areas for both grippers were prioritized for major damage(i.e. the internal bladder for the pneumatic gripper, sensor
and struts for the HSA gripper), as for both grippers, there
is a large amount of bulk material that will cause little effectwhen punctured. After each puncture/scrape, grippers werethen qualitatively evaluated to see if they could still grasp as
expected and if sensor data was as expected.
For both the fluidic actuator and our gripper, minor
damage did not cause noticeable effects on the grippers.
Even upon multiple (20) lacerations, only minor weakening
occurred for both grippers and they were both able tograsp normally. For our gripper, only the silicone glove wasaffected, causing no impact to the sensor or HSA actuators.
The gripper was thus still able to identify the can as metal.
For major damage, the fluidic actuator failed after a single
puncture. For the HSA gripper, even after 20 punctures,the most damage that occurred was to the outside silicone
glove. The stiffness of the PTFE made it difficult to puncture
through the struts of the HSAs, while the sensor was just
perforated with minimal effect.
V. CONCLUSIONS AND FUTURE WORKACKNOWLEDGMENTS

We have shown a soft robotic gripper that can be used
to sort recyclables. To accomplish this, we built a gripper
that is puncture resistant with on-board tactile sensing. By
integrating the electrically driven and robust HSA actuatorswith proprioceptive strain and pressure sensors, we were able
to satisfy these design requirements and create a classifierthat could identify metal, plastic and paper objects.

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
106

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 Shuguang Li for help with
figure design.

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