Integrated Sensing in Robotic Skin Modules
William R. Johnson III, Joran Booth, and Rebecca Kramer-Bottiglio

2021 IEEE Sensors | 978-1-7281-9501-8/21/$31.00 ©2021 IEEE | DOI: 10.1109/SENSORS47087.2021.9639529

Mechanical Engineering & Materials ScienceYale University
New Haven, Connecticut, USAwill.johnson@yale.edu, joran.booth@yale.edu, rebecca.kramer@yale.edu

Abstract—Robotic skins combine actuation and sensing in a2D, modular soft robot. Robotic skins can be attached to orwrapped around arbitrary objects to add robotic functionality,and they have been demonstrated in a variety of applications,including continuum robots and active wearables. However, thesensors and actuators in current robotic skins are not wellintegrated. The attachable and detachable components on robotic
skins limit their efficiency and robustness compared to the
biological systems with embedded multi-functionality they aimto rival. This work integrates sensors into active skins to create
robotic skins with embedded actuation and sensing. Experiments
demonstrate the skin’s ability to reconstruct its perimeter within
0.59% and its vertex locations within 12.6% of its length scale.
This work is the first step toward robotic skins with fully
integrated sensing and actuation.
Index Terms—shape reconstruction, robotic skins

I. I NTRODUCTION
Robotic skins are 2D soft robots that contain both actuationand sensing [1]. Robotic skins are modular and reconfigurable,
and they can transform passive structures into soft robots when
they are attached at the surface [2] or wrapped around a softbody [3]. Robotic skins have been demonstrated achieving
shape change by sculpting clay [4] and learning their own
models for closed-loop control [5]. There is a large body ofliterature on electronic, sensory skins inspired by biologicalskin [6]. Sensory skins can be adhered to the surface of soft
bodies [7] [8] or placed at the surface of a soft robot during thefabrication process [9]. Sensory skins have been demonstrated
with haptic sensing [8], texture sensing [10], and strain andpressure sensing [11] [12] suitable for a variety of soft robotic
and wearable applications. In contrast, robotic skins, which
integrate actuation and sensing, are relatively new with many
opportunities for advancing the technology.
Current robotic skins are made from modular componentsthat are not well integrated, resulting in failure at the attachment points between sensors, actuators, and substrates.
Moreover, tighter integration of sensors and actuators leads
to increased efficiency and previously unachievable functionality [13]. We have previously shown membrane actuatorsthat have integrated actuation in active skins in the form ofpneumatic bladders [14], which enabled larger deformations
and increased robustness on a skin-based tensegrity robot. Proprioception is essential for effective control of soft robots [15].
Integrated sensing for proprioception can lead to improved
This material is based upon work supported by the National Science
Foundation under grant no. IIS-1955225.

Fig. 1. Fabrication process for tightly integrated robotic skins. First, amultiphase composite (MPC) layer is coated on the active skin using a papermask. After curing, the MPC layer is encapsulated, and the encapsulant
doubles as glue to adhere the strain-limiting layer. These steps are repeated
for the reverse side of the skin so that the two MPC layers together form
capacitive sensors.

robustness and more reliable control. This work integrates
strain sensing into active skins toward tightly integrated roboticskins. We introduce a method for embedding sensors into wellintegrated active skins and describe the one-time calibration
process for the sensors. Experiments demonstrate the skinmodules’ reliable proprioception for soft robotic applications.
II. M ODULE FABRICATION
We chose to integrate capacitive strain sensors into theactive skins due to their high accuracy, high linearity, cyclicstability, and negligible hysteresis [16]. In general, the capacitance of a parallel plate capacitor depends only on its

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geometry, and a capacitive sensor behaves like a parallel plate
capacitor if the electrode resistance is low [17]. In this work,
we use a multiphase composite (MPC) that we developed in
our previous work that has a notably low elecrical resistivity (4 × 10−5 Ω m) [18]. MPC is made from a silicone elastomer (Eco-Flex 50; Smooth-On), a room-temperature liquid
metal (eutectic Gallium-Indium), and exfoliated intercalated
graphite. In addition to its low resistivity, MPC is capable ofhigh strains (200%) and can bond well to silicone substrates,making it a suitable choice for a soft sensor [18].
Following the manufacturing process shown in Fig. 1, theMPC sensors are embedded into an active skin made froma silicone elastomer (DragonSkin 10; Smooth-On) that has
three inflatable pneumatic bladders. First, 100 mm x 3 mm
strips of MPC are coated on top of the silicone skin using a
100 µm paper mask. Wire leads are inserted into the pre-cured
composite on a 5 mm x 5 mm pad at the end of the strip, andthe MPC is allowed to cure for two hours at room temperature.
Next, a thin, encapsulating layer of Eco-Flex 50 is applied andused to adhere a strain-limiting layer. The strain-limiting layer
consists of fabric reinforcements at the corners of the skin anda silicone composite with unidirectional inextensible fibers thatlimit strain anisotropically. For the manufacturing procedureof the strain-limiting layer (as well as the active skin withoutsensors), the reader is referred to [14]. The unidirectional
fibers ensure that the capacitive sensors only experience straintransverse to the fibers’ alignment and therefore only measure
the strain in that direction. The skin is placed under a weight
and the encapsulant/adhesive is allowed to cure for three hours
at room temperature. Then, the skin is flipped over and theMPC and strain-limiting layers are coated on the reverse sidein the same manner.

[a][b]Fig. 2. Sensor calibration results. [a] Calibration data from three sensors onone skin. The markers represent the calibration trials, and the lines represent
the linear fits. [b] Histogram of residuals from all sensor calibration trials inunits of strain. σ = 0.0138.

III. E XPERIMENTAL M ETHODS
A. Sensor Calibration
Sensors were calibrated once before use. Each sensor wasstretched to seven different known lengths between 120 mm(rest length) and 192 mm (60% strain), and the resulting
capacitance was measured using a commercial capacitive
sensing breakout board (MPR121; Adafruit). This calibration
procedure was repeated three times for a total of 21 data points
per sensor. The data were used to construct a linear model
mapping capacitance to length for each sensor (Fig. 2a). Allsensors exhibited high linearity with R2 > 0.99. The standard
deviation of the error between the linear model’s prediction
and the sensor’s state in units of strain is 1.38% (Fig. 2b).
B. 2D Shape Reconstruction
To validate the shape reconstruction capabilities of theskin, experiments were done with a pegboard with 12 mm
spacing between the holes (Fig. 3a) following the experimental
procedure of [7]. A script output random coordinates on thepegboard within the sensors’ calibrated range (120 mm to192 mm), and the capacitance of each sensor was measured
and converted into a length using the linear model corresponding to that sensor. The three lengths were used to reconstruct

the coordinates of the vertices of the triangle (Fig. 3b) using
the same method as our previous work [7]:x0 = L0
L22 − L21 + x21
2x1qy2 = L22 − x22

x1 =(1)(2)(3)

These variables are defined in Fig. 3a. One example fromthe reconstruction experiments is shown in Fig. 3b. Each of
the seven skins was used for 20 reconstruction trials.
IV. R ESULTS
The shape reconstruction results are summarized in Fig. 4.
We define the Euclidean error as the sum of the planar
distances between the reconstructed vertices and the true
vertex locations. While there are different ways to quantify
the reconstruction error, we chose to show the Euclidean error,
which is preferred in applications where the location of one
vertex is known. If shape accuracy is more important, theAuthorized licensed use limited to: Yale University. Downloaded on February 02,2022 at 17:05:16 UTC from IEEE Xplore. Restrictions apply.

[a]
[a]

[b]
Fig. 3. (a) Experiments were conducted with the skins on an acrylic pegboard
with 12 mm spacing between the holes. (b) An example of reconstruction.
The black lines connect the points where the skin’s vertices were placed on
the pegboard, and the red lines represent the sensor reconstruction.

Procrustes error should be calculated as in [2]. The graph inFig. 4a shows the average Euclidean error for each skin across
20 trials as an absolute distance on the right and normalized by
the rest length (120 mm) of one side of the triangular skin on
the left. The normalized Euclidean error from each of the 140
trials is plotted in Fig. 4b. The average normalized Euclidean
error was 12.6% (15.1 mm on an absolute scale) with astandard deviation of 5.80% (6.96 mm). We also quantify
the error in reconstructing the perimeter of the triangle. In
the perimetric case (Fig. 4c), the average error over all skins
and trials is 0.59% (2.76 mm) with a standard deviation of
1.77% (8.09 mm). The Euclidean error is greater than theperimetric error due to error propagation when calculating thevertex positions from the side lengths. Tightly integrating thesensors eliminates the error from sensor attachment, so theseskins exhibit high accuracy in proprioception.
We conducted the experiments to validate the skins’ sensing
separately from their actuation to avoid coupling effects. A
limitation of this work is the skins’ inability to reliably
reconstruct shape during actuation because the strain sensors
respond to the actuators’ inflation as well as the stretching of
the skin. Future work will involve modeling to optimize thesensor placement relative to the pneumatic bladders in order[b][c]Fig. 4. Error analysis. [a] The Euclidean error is plotted for each skin. The
left axis is normalized by the rest length (120 mm) of one side of the skin. [b]The normalized Euclidean error is plotted in a histogram for all trials acrossall skins. µ = 12.6%; σ = 5.80%. [c] The perimetric error is plotted in ahistogram for all trials across all skins. µ = 0.59%; σ = 1.77%.

to mitigate the coupling effect, a strategy employed in [19].
V. C ONCLUSION
This work presents tightly integrated strain sensing inrobotic skins. Experiments demonstrate high reliability inshape reconstruction due to the tight integration. This work
is an important step toward fully integrated robotic skins withrobust feedback control.

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