SCIENCE ROBOTICS | REVIEWSENSORS

Electronic skins and machine learning for intelligent
soft robots
Benjamin Shih1, Dylan Shah2, Jinxing Li3, Thomas G. Thuruthel4, Yong-Lae Park5, Fumiya Iida4,
Zhenan Bao3, Rebecca Kramer-Bottiglio2, Michael T. Tolley1*

Copyright © 2020
The Authors, somerights reserved;
exclusive licensee
American Association
for the Advancementof Science. No claim
to original U.S.
Government WorksINTRODUCTION

Skin plays an essential role for biological systems as a barrier between
an organism’s external environment and its internal components.
Embedded within its layers is a dense network of mechanical, chemical,
vibrational, temperature, and pain receptors, which work in coordination to enable somatosensation in skin. These capabilities would
also be incredibly useful for robots. Electronic skin (e-skin) research
was originally motivated, in part, by a desire to understand biologicalsensing, but the lessons learned can help improve the design ofrobotic systems. To sense, plan, and act, robots require a variety of
sensors embedded throughout their bodies so that they can obtain
information about their environment.
The field of soft robotics (1) studies the use of flexible andcompliant materials as components for building robots, insteadof traditionally rigid components such as metals. Soft robots often
draw inspiration from nature, which has evolved organisms that canoperate in unstructured environments. In contrast, current robotic
systems are usually confined to structured laboratories or warehouseenvironments. In addition, natural environments typically containseveral objects of varying material properties that further complicate
tasks such as object interaction and locomotion.
The overlap between e-skins, soft robotics, and machine learning
is continually growing, and recent advances are summarized inFig. 1. Soft actuation has improved tremendously in capabilities(Fig. 1 bottom), and soft sensors and e-skins exhibit a wide rangeof complexities (Fig. 1 left).
Several recent advances have combined principles from each field,
often physically manifesting in the form of sensorized fingers andgrippers (Fig. 1 top). Future breakthroughs in the field may comefrom further integration of sensors and actuators as roboticists movetoward designing systems that rival the abilities of biologicalorganisms.
1

Department of Mechanical and Aerospace Engineering, University of California,
San Diego, CA, USA. 2Department of Mechanical Engineering and Materials Science,
Yale University, CT, USA. 3Departments of Chemical Engineering and Material
Science and Engineering, Stanford University, CA, USA. 4Department of Engineering,
University of Cambridge, UK. 5Department of Mechanical and Aerospace Engineering,Seoul National University, South Korea.
*Corresponding author. Email: tolley@ucsd.edu
Shih et al., Sci. Robot. 5, eaaz9239 (2020)

22 April 2020

Several reviews have covered related topics on e-skins and perception in soft robots, including design and fabrication of e-skins(2, 3), wearable sensors (4), e-skins for interactive robots (5, 6), andfuture directions in sensing and perception for soft robots (7, 8).
This Review examines recent developments in skin-based sensing
for soft robots, covering hardware and fabrication techniques andmachine learning techniques that translate robot perception into
action planning. To limit the scope of this Review, we consider asoft robot skin to be skin sensors directly mounted on the surface[e.g., (9)] or embedded in a thin layer beneath the surface of the bodyof a soft robot [e.g., (10)]. To highlight the opportunities at theintersection of e-skin and soft robotics research, we cover a variety ofinterdisciplinary topics including fabrication, learning, and control.
INTERDISCIPLINARY TOOLSDesign and fabrication of integrated e-skins
Compared with rigid robots, the high mechanical compliance of soft
robots enables safer and more efficient human-robot interaction(HRI) because they can seamlessly interact with the human body(11). Further advancement of soft robots requires high-performanceelectronics and sensors that can stretch continuously with theirbodies. Recent research in artificial skin has mainly focused on making
individual sensor devices with better performance, such as sensitivity,
stretchability, and reliability over many use cycles (Fig. 2). To realize
fully biomimetic skin for soft robotics, artificial skin should containsensor arrays that are stretchable, cover large areas with a highspatiotemporal resolution, and have multiple functions that mimic
diverse receptors of the human skin (Fig. 2A). These features should
enable robots to use data-driven methods to extract rich information
about their environment.
Increasing sensor density and quantity normally requires a larger
number of interconnecting wires. To reduce this burden, sensor
arrays are normally designed in matrix form. For example, a recently
developed tactile glove comprising 548 force sensors was constructedusing readily available materials and simple fabrication tools (Fig. 2B)(12). This sensor array recorded a large-scale dataset of tactile maps(about 135,000 frames), which was used to identify objects using
convolutional neural networks. This work highlights the ability of
1 of 11

Downloaded from http://robotics.sciencemag.org/ at Yale University on April 23, 2020

Soft robots have garnered interest for real-world applications because of their intrinsic safety embedded at
the material level. These robots use deformable materials capable of shape and behavioral changes and allow
conformable physical contact for manipulation. Yet, with the introduction of soft and stretchable materials to
robotic systems comes a myriad of challenges for sensor integration, including multimodal sensing capable ofstretching, embedment of high-resolution but large-area sensor arrays, and sensor fusion with an increasing
volume of data. This Review explores the emerging confluence of e-skins and machine learning, with a focus
on how roboticists can combine recent developments from the two fields to build autonomous, deployable soft
robots, integrated with capabilities for informative touch and proprioception to stand up to the challenges ofreal-world environments.

SCIENCE ROBOTICS | REVIEW
Used in machine learning

Natural organisms

Integration of e-skins,
machine learningand soft robotics

Standalone sensors

Actuation and sensing
Integration of e-skins with soft robotics

Sophistication of sensing

5 cm

0.5 cm1 cm

Actuation-focused soft robots

Sophistication of actuation
Fig. 1. Trends in the intersections between e-skins, soft robotics, and machine learning. (Left) A range of e-skins and soft sensors that increase in complexity, frombottom to top, by metrics including density, resolution, and fabrication (19, 21, 26–30). (Middle) Soft robots and e-skins that merge actuation and sensing (10, 37–45, 47),
from left to right and top to bottom. (Bottom) Soft robots that focused primarily on actuation (31–35), from left to right. Red boxes indicate work that has leveraged
machine learning in the processing of their sensor information.

large-scale datasets collected by a high-density sensor array to enable
not only a sense of touch but also the intelligent extraction of inform­
ation from touch. Increasing the sensor density simply by scaling downa passive matrix architecture will reduce the amplitude of analog signals while increasing cross-talk between interconnects. If multiple
sensors are sampled simultaneously, each line will produce electromagnetic noise, which will corrupt the signals being carried on neighboring conductive traces. Furthermore, the large number of addressing
lines will be difficult to manage as the number of sensors increases
substantially. These problems can be addressed with an active matrix
that pairs each sensor with a transistor to provide local signal amplification and allows sensors to take turns transmitting information (13–16).
Active matrices with multiplexed signal transduction typically consume less power than passive matrices because they require fewer
sampling lines and do not need external circuitry (17). However,stretchable e-skins could allow better coverage of curved robot
surfaces while allowing sensing of complex texture information
Shih et al., Sci. Robot. 5, eaaz9239 (2020)

22 April 2020

through detection of deformation and vibration, mimicking biologicalskin. Recent advancements in organic electronics by Wang et al. (18)
led to the creation of an intrinsically stretchable transistor array with
347 transistors per square centimeter. Their proof-of-concept demonstration illustrated that such a conformable active matrix could accurately map the force applied on each sensor. These capabilities
indicate that stretchable active matrices containing soft sensors andtransistors are a promising step toward soft robotic skin with highresolution and high data fidelity.
Making multilayered sensor arrays in a three-dimensional (3D)
lattice can further increase the sensor areal density and allow greater
integration of different sensor modalities. Just as receptors in biological skin are embedded at various depths, engineers can embed
sensors that are sensitive to different stimuli in different spatial
locations. For example, pressure, shear, and strain sensors can be
distributed in different layers of the e-skin to achieve optimized
sensitivity. Huang et al. (19) demonstrated that stretchable electronics
2 of 11

Downloaded from http://robotics.sciencemag.org/ at Yale University on April 23, 2020

CREDIT: ADAPTED BY A. KITTERMAN/SCIENCE ROBOTICS FROM MATERIAL PUBLISHED BY IEEE (10, 26, 27, 32, 37, 40–42, 45, 109), SPRINGER/NATURE (19, 20, 35), WILEY (28), THE NATIONAL ACADEMY OF SCIENCE (31), AND
AAAS/SCIENCE (21, 29, 33, 34, 39, 43, 44, 47), LORI K. SANDERS/HARVARD UNIVERSITY (35), A. ECKERT/TUM (109)1 cm

SCIENCE ROBOTICS | REVIEWA Active matrix

C Multimodal sensing array

D Three-dimensional integration

E SkinHeat, pain,
and cold
receptor

Merkel disk
(touch)Touch
receptors
Pressure
receptor

Meissner corpuscles Free nerve ending(vibration)(temperature)

Fig. 2. Sensor arrays enable e-skins to extract information about their environment. (A) Human skin with various receptors used to sense stimuli. (B) A scalable
tactile glove containing a passive matrix of 548 force sensors for the collection of large datasets (12). (C) 2D sensor array used to generate a profile of pressure intensityfrom experimental mapping of the pixel signals using an active matrix (126). The icons at the bottom represent biological analogies: Merkel disks, Meissner corpuscle,
and free nerves. (D) A 3D array of electronic sensors assembled from 2D electronics (127). (E) Multimodal sensor array that can capture both pressure and temperature
information (24).

integrated in 3D can be built with a layer-by-layer method usingtransfer printing of predesigned stretchable circuits on elastomers
with vertical interconnects. This stretchable human-machine interface had a four-layer design that offered multimodal sensing andhad integrated circuits for wireless data transfer. Using strain engineering methods, 2D structures can also be assembled into 3D electronic systems with sensing capabilities. Semiconductor materials canplay critical roles in this context, through demonstrations of complex, mechanically assembled 3D systems for light-imaging capabilities that can encompass measurements of the direction,
intensity, and angular divergence properties of incident light.
3D printing has also been used to directly print sensors in soft
robots to improve both exteroceptive and interoceptive capabilities(20). This work highlights how a 3D integration framework enables ahigher integration density on stretchable substrates than single-layer
approaches and allows new functionalities that would be difficult toimplement with conventional layer-by-layer designs.
Processing complex tactile information from a sensor array
requires efficient signaling and sampling methods. In human skin,stimulation of the receptors is converted into a series of voltage
pulses sent to the nerves. This inspired researchers to develop artificial receptors and afferent nerves to convert tactile information todigital pulses at the site of sensation (21, 22). The signal could
potentially be perceived by a user’s nerves and brain, thus directly
linking the human brain with soft robotic prosthetics. For example,
Kim et al. (23) recently developed a flexible artificial afferent nervethat can effectively collect pressure information from arrays of pressure
sensors and convert them to action potentials to activate muscles.
Biological skin contains receptor networks that can detect various
stimuli, such as vibration, humidity, and temperature. Several studies
on e-skin sensor arrays focused on the classification of a single typeof information, such as force, shape, or direction of motion. The next
Shih et al., Sci. Robot. 5, eaaz9239 (2020)

22 April 2020

generation of e-skins should integrate multimodal sensor arrays tocapture richer sensory information than their predecessors. Recently,
Lee et al. (24) reported a neuromimetic architecture that enabledsimultaneous transmission of both tactile and thermotactile inform­
ation (Fig. 2E). The pressure- and temperature-sensitive transducers
can both be communicated through the pulse signatures by a single
electrical conductor. As a biomimetic signaling method, this approachis promising for reducing wiring and computational requirements
when a robot is covered with thousands of sensors. Multimodal sensing could also be achieved through integration of multiple stretchable
optical fibers, which has been shown to be effective at localizing andestimating force in soft actuators (25).
Overall, many innovations are required for realizing high-density
and multifunctional sensor arrays for soft robots. A close collaboration
between roboticists and materials scientists is needed to develop
high-performance stretchable conductors for electrodes and interconnections and stretchable semiconductors for building active
matrices and signal amplifiers. Different sensing modalities andintegration architectures should also be explored. Lastly, hardware
and algorithms for data processing should be considered during thedesign of sensory systems, and their performance should be evaluatedon a holistic range of practical robotic tasks.
Skin-based sensing for soft robots
As sensors are increasingly integrated into soft robots, we can imagine
a conceptual plane that categorizes research based on the sophisticationof actuation and sensing independently (Fig. 1). Stand-alone sensors
lie on the y axis; some consist of simpler strain sensors [the bottom
three images in Fig. 1 left (26–28)], whereas others have more sophisticated sensing schemes, including distributed or multimodal sensing[the top four images in Fig. 1 left (19, 21, 29, 30)]. The x axis, representing actuation-focused soft robots, shows examples of increasingly
3 of 11

Downloaded from http://robotics.sciencemag.org/ at Yale University on April 23, 2020

CREDIT: ADAPTED BY A. KITTERMAN/SCIENCE ROBOTICS FROM MATERIAL PUBLISHED BY SPRINGER/NATURE (126, 127) AND SCIENCE ROBOTICS (24) AND UNPUBLISHED MATERIAL FROM THE AUTHORS OF (12)B Passive matrix

SCIENCE ROBOTICS | REVIEWCREDIT: A. KITTERMAN/SCIENCE ROBOTICS

InputsProcessing(57) used liquid metal sensors to measure human gaits using asensor fabrication process first presented by Park et al. (27). The
resistance of these sensors increases as the embedded microchannels
inside the elastomer matrix are stretched because of the increased
length and decreased area of their bulk liquid-metal channels. Others
incorporated their sensors with robots: Boutry et al. (58) paired ashear force sensor with a robot arm to allow robotic hand control.
Booth et al. (47) demonstrated reconfigurable, actuatable e-skins that
could control the motion of deformable inanimate objects fromtheir surface. Zhao et al. (43) embedded optical sensors within soft
pneumatic fingers, which they then integrated with a Baxter robot.
As skin-based sensing capabilities continue to improve, the goal is
to develop capabilities that match or outperform biological systems(top right corner of Fig. 1).
Machine learning for soft e-skins
As e-skins increase in resolution, their signals could be processed todetect higher-order deformation modes and higher-level notions about
the environment, such as material type. However, obtaining thisinformation requires algorithms that can extract useful informationfrom large quantities of data. To handle the vast amount of data thate-skins can provide, machine learning is emerging as a versatile tool
for making sense of large quantities of data (Fig. 3). For example,
Piacenza et al. (59) obtained high-resolution data from a robotic
fingertip and used ridge regression to process this data to estimate
the locations of indentations. Similarly, Larson et al. (60) usedconvolutional neural networks to learn deformations on a sensor
array that can interpret human touch in soft interfaces. At the levelof abstraction of the entire robotic system, Van Meerbeek et al. (53)tested various learning algorithms to estimate the twist and bend
angles in sensorized foam, finding that k-nearest neighbors (kNN)outperformed other common algorithms, including support vector
machines (SVM) and multilayer perceptrons. In addition, researchers
have also focused on recurrent neural networks, which have beenshown to be advantageous for learning patterns in time series data
(36, 39, 61, 62).
Because of the complexity of the mapping between raw sensory
information and relevant functional abstractions, information theory
and machine learning will play a large role in bringing tactile sensing

Outputs

StrainLevel of abstraction

ML equivalent

ShearHigh level

Reinforcement
learning

Exploration strategy
Action planning

Direct policy learning
PolicyState Reward

Long shortterm memory
+

+
+

+

Environment

Temperature

Mid-level

Supervised
learning

Regression

Pressure

Low-level

Unsupervised
learning

Clustering

ChemicalExamplesConvolutional
neural networks

Autoencoders

Dimensionality reduction

Fig. 3. Machine learning techniques for processing raw sensory information, different levels of abstraction to aid in robot perception, and action planning. The
level of abstraction depends on the task, and the most effective type of learning architecture depends on the quality and structure of the sensor signals. The level of abstraction
depends on the task, and the most effective type of learning architecture depends on the quality and structure of the sensor signals. Higher-level processes can include
parallel execution of lower-level processes. End-to-end architectures [e.g., (61)] without mid-level and low-level pipelines would likely be faster and more effective but arecomputationally expensive to develop.
Shih et al., Sci. Robot. 5, eaaz9239 (2020)

22 April 2020

4 of 11

Downloaded from http://robotics.sciencemag.org/ at Yale University on April 23, 2020

complex soft systems that can walk (31, 32), grow (33), swim (34), andoperate autonomously on chemical fuel (35) (Fig. 1 bottom). Last, many
recent works have begun exploring the intersection of the actuationand sensing (Fig. 1 middle). Several of them embedded strain sensors forstate estimation or tactile sensing in a finger-like structure (10, 36–44),
whereas the others mounted their skins externally (45–47). As bothareas progress, we envision further integration of increasingly sophisticated actuation and sensing, extending into the top-right quadrantof the conceptual plane.
Access to higher-resolution data about touch will increase theability of soft robots to perceive the complex deformations that theyexperience during tasks, including locomotion and manipulation.
Today’s discrete sensors, which are built with high sensitivity andselectivity, can be tailored to sense deformation modes in a localized
region or known environment with high confidence (46, 48). However,this sensing paradigm is insufficient in dynamic or unknown environments where robots will experience substantial deformation because
robots do not yet have the level of sophistication of human skin
receptors or the human brain to collect a broad range of information.
In addition, many robots are unable to process the volume of inform­
ation to accurately determine the environment or the object being
sensed. The transition from discrete to continuous sensing and theshift from structured to unconstrained environments both requiree-skins that can rapidly collect and process large amounts of inform­
ation. The added complexity from both transitions compounds theprocessing required to interpret the signals.
Several designs of skin-like sensors have been used in soft robotics.
Many of these sensors contain conductive and stretchable materials
to produce resistive or capacitive strain sensors (10, 26, 49, 50).
Other groups have used optical devices such as cameras and opticalfibers to sense deformations within an actuator (51–53). Several ofthese existing sensors are well suited for measuring characteristics
such as strain, pressure, and bending but do not enable the highsensor densities or resolutions that have been demonstrated in e-skins.
Soft robots would benefit from integration with e-skins, such as theskin-like sensor arrays that have been deployed in medical applications or directly on skin (22, 29, 54–56).
Currently, soft skin-like sensors have been deployed in several
ways. Some groups used their sensors as wearables; Mengüc et al.

SCIENCE ROBOTICS | REVIEWAPPLICATIONS OF E-SKINS

Shape sensing
Whereas environmental sensing helps a robot understand its surroundings, having a self-model of the robot’s body is important forplanning trajectories and actions within that environment. For
robots primarily composed of rigid components, the geometry ofeach segment remains the same throughout the robot’s lifetime, andrelative rotations or translations of links provide enough additional
information to fully specify the overall changes in shape. However,for soft robots, individual segments can continuously change theirshapes, via both intentional and unintentional deformation modes,
which complicates modeling and sensing schemes. Complementing
recent work on soft sensing (7), the direct sensing of surface deformations would enhance the functionality of soft robots.
One approach to sensing the shape of soft robots involves pairing a model with a relatively low number of sensors, typically on thesame order of magnitude as the number of controllable degrees of
freedom in the system. A great deal of progress has been made inmodeling manipulators that can be parameterized by a curve in
3D space (71). These models have even been coupled with sensing
mechanisms to enable closed-loop control of continuum manipulators(72). Some approaches embedded sensors into other soft robotic
components, such as bending actuators, to achieve closed-loop
control in a low-dimensional task space (73).
Shih et al., Sci. Robot. 5, eaaz9239 (2020)

22 April 2020

The primary drawback of this type of approach is that when otherunplanned deformation modes are introduced, such as buckling ora change of material properties through damage or natural material
aging, the models accumulate error. In addition, it is unclear how to
generalize these advances to reconfigurable soft robots (74) or
robots that have more complex morphologies. For example, recent
simulations suggest that there is a wide range of soft robot morphologies that could produce useful locomotion, including quadrupedal
shapes and various oddly shaped exteriors (75). All these classes ofrobots would benefit from sensing mechanisms with fewer assumptions about the robot’s mechanical properties.
The ideal shape-sensing system could stretch with the robot’s surface without affecting its kinematics or dynamics, sense shape without external components, and be thin. E-skins designed for wearable
applications should accommodate the strains of about 55 to 75%
experienced by biological skin (22), and a similar range should besuitable for most soft robotic applications, although different robots
experience different surface strains. Although a perfect solution forshape sensing of soft robots does not currently exist, recent advancesin the field of flexible shape-sensing e-skins (Fig. 4) have the potential to greatly improve the capabilities of soft robots.
In contrast to that of soft skins, most work on shape-sensing
e-skins treats the skin as an inextensible sheet of rigid elements joinedby known axes of rotation (Fig. 4, A and B). The primary challenge
is thus estimating the relative orientation between sections with known
geometries to determine the spatial locations of discrete points within
the sheet. In one early study, Hoshi and Shinoda (76) arranged 24
printed circuit board (PCB) “nodes” into a mesh and estimatedinternode rotations using accelerometers and magnetometers (Fig. 4A).
Building upon this work, Mittendorfer and Cheng (48) developed
rigid sensorized hexagonal PCBs that could be integrated into semiflexible sheets and wrapped around robots (Fig. 4B). The nodes
contained accelerometers similar to the work by Hoshi and Shinoda(76) and had similar assumptions (PCBs are free to rotate but cannot
be stretched), but rotations between neighboring PCBs were calculated by obtaining at least two orientations of the skin-per-skinshape and solving a constrained Procrustes problem for aligningmatrices of data points in real time. Hermanis et al. (77) then useda grid-like arrangement of accelerometers and gravitometers on a
flexible fabric sheet. The sheets were demonstrated in a dynamic state
estimation task where a user wore a shirt equipped with the shape-­
sensing sheets while bending and crouching.
In contrast to the discrete sampling methods mentioned above,other approaches leveraged techniques from machine learning andstatistics to process various sensing signals and extract a continuous
estimate of the shape of the skins (Fig. 4, C and D). This kind ofdata-driven technique will be increasingly useful as the sensory
spatial density increases, as discussed throughout this Review. For
instance, Rendl et al. (78) used regularized least squares to processdata from 16 piezoelectric bend sensors on a plastic sheet [polyethylene
terephthalate (PET)] to approximate the shape of the sheet as a combination of several shape primitives. This created a flexible system that
could sense the bent state of the sheet with a roughly centimeter-levelaccuracy over an approximately A4-sized sheet. Another study
used relatively inextensible optical fiber Bragg gratings arranged ina circle on the top and bottom of a silicone e-skin (Fig. 4C) (79). The
relation between the strains on the fiber and the shape of the sheet
was extracted from training data using a feed-forward artificial
neural network containing one hidden layer for computation between
5 of 11

Downloaded from http://robotics.sciencemag.org/ at Yale University on April 23, 2020

to human-like performance levels. In particular, the subfield of rein­
forcement learning (RL) will be important for developing closed-loop
control for tactile feedback. Suitable algorithms and architectures foranalogous tasks in soft robotics can be developed by learning from biological processes. For example, in computer vision and machine learning, thehierarchical nature of visual processing (corresponding to compositional
functions) (63) has recently enabled deep neural networks to achieve
human-like performance across a variety of visual processing tasks (64).
Processing signals from arrays of tactile sensors may benefit fromsimilar techniques, as sets of sensor readings have information encodedin spatial relationships that can be naturally represented using matrices.
Tactile exploration can benefit from recent developments inlearning-based simultaneous localization and mapping algorithms.
Notably, Mirowski et al. (65) used an asynchronous advantage
actor-critic algorithm for navigating in a complex environment andadditionally solved auxiliary prediction tasks that made the RL problem faster and more data efficient. Chen et al. (66) showed a direct
policy learning algorithm with spatial memory and bootstrapped withhuman-mediated imitation learning without explicit task rewards.
In the absence of continuous reward functions, actor-critic algorithms
are preferred because they require fewer samples.
Similarly, tactile manipulation tasks can use insights from learning-­
based manipulation controllers. A general trend observed in suchworks is the success of model-based RL (67) or learning by demonstration (68), approaches that leverage techniques from control theoryor human knowledge, respectively. There has been a successful solution for the direct learning of control policies for dexterous manipulation, but it relied on the availability of an accurate simulation
environment (69). Until robot simulators can model soft-bodydynamics that reliably transfer to real robot hardware, such approaches are difficult to apply to soft robots and deformable objects.
For specific simple tasks, it might be easier to find a direct policy
than to fit a general-purpose model of the system dynamics (70).

SCIENCE ROBOTICS | REVIEWA Flexible at joints

B

C Continuously flexible
0.17
0.16

0.15

0.15

0.14

0.14

0.13
–0.02
–0.04

0.13
0.12

0.14

0.12

X (m)

CREDIT: ADAPTED BY A. KITTERMAN/SCIENCE ROBOTICS FROM MATERIAL PUBLISHED BY IEEE (48, 76, 79), ICS/TUM (48), AND SCIENCE ROBOTICS (53)D

Ground truth: [48 , –70 ]Prediction: [48.6 , –69.3 ]Fig. 4. E-skins that can sense their shape in 3D. Recent advances in shape-sensing
e-skins use several sensing modalities. (A and B) Accelerometers and/or magnetometers on rigid PCBs can rotate relative to each other and reconstruct their shapeat discrete points. (A) “3D capture sheet” (76). (B) Hexagonal PCBs with integrated
accelerometers (48). (C and D) Continuously flexible devices can sense deformation
throughout their surface and estimate their resulting shape. Data-driven methods
were then used in these examples to estimate the continuous shape of the e-skin.
(C) Fiber Bragg gratings in silicone (79). (D) Plastic optical fibers in silicone foam (53).

the input and output layers. In a similar spirit, an array of opticalfibers were twisted through an elastomeric foam, and their outputs
were sent to several machine learning algorithms (including kNN, SVM,
neural networks, and decision trees) to predict the mode of deformation
and angle of deformation of their structure (Fig. 4D) (53). These
approaches all dealt well with a limited set of deformations and, inprinciple, should work for a wider range of deformations when pairedwith a more expressive (deeper) network. However, none of these existing works can mechanically accommodate large in-plane strains,primarily because of the inextensibility of the optical fibers used.
Toward feedback control of soft robots
The intrinsic material compliance of soft robots can protect both
the robot and the environment from damage when interacting with
unstructured environments. This property makes soft robots appealing in contexts such as HRI and robotic manipulation, where
safety around fragile objects can be important (11, 80). E-skins havegreat potential to enable soft robots to interact intelligently with
their environment.
In addition, tactile information obtained through skin is vital fora variety of general robotic control tasks. The type of sensor modality
to be used, the processing algorithm, and the response from the bodyall depend on the task at hand (81). These tasks can be divided into
Shih et al., Sci. Robot. 5, eaaz9239 (2020)

22 April 2020

6 of 11

Downloaded from http://robotics.sciencemag.org/ at Yale University on April 23, 2020

Z (m)

0.16

three broad categories depending on the flow of information orenergy and the primary system of concern (Fig. 5).
Manipulation
Robotic manipulation involves altering the state of an external
object to a desired set point using internal actuators. The role oftactile sensors is mainly to obtain state information of the external
object. As energy flows to the environment, stability of the object isof high concern. Grasp force optimization and stabilization is oneof the most basic manipulation tasks involving tactile sensors (82).
Early works were built on the estimation of normal and tangential
forces on the hand to detect slip and react accordingly (83). Recent
works used learning-based methods for slip onset prediction withadjustment and grasp failure detection with adjustment because of
the ability of these methods to handle complex multimodal sensory
information (84) and their generalizability (85).
Other manipulation studies used low-dimensional sensor space
representations to improve performance in certain situations.
Van Hoof et al. (86) used autoencoders to generate a low-dimensional
representation of their complex and continuous tactile data. Control policies learned using this latent space representation required
fewer rollouts and were more robust to noise. Another study was on
calibration and self-modeling of a fully sensorized body for wholebody manipulation (87). Recent work has shown that this process
can be fully automated using control signal information and othersensor modalities, including inertial measurement units (88).
Perhaps the most complex manipulation task is in-hand manipulation, which imposes strict requirements on the body, brain, andsensors (89). Current progress in in-hand manipulation usingtactile sensors is primarily limited to rolling circular objects (90). On
the other hand, notable developments toward in-hand manipulation
have been achieved with external visual tracking systems (69). However, control policies trained using vision alone are scene dependentand require large quantities of training data, motivating further
research into using tactile sensing during in-hand manipulation.
Exploration
Tactile exploration is the process of voluntary motion of the body
based on the somatosensory feedback for identifying environmental
properties (91). The environmental property of interest could be low-­level features, such as surface texture (92, 93) or temperature (94), or
midlevel tasks, such as object classification (95, 96). However, to be fullyautonomous, the higher-level process of selecting the best actions for
obtaining better sensory information, also known as active exploration,must be considered. This is not trivial because the concept of an objective function and a reward function becomes difficult to define.
It is currently conjectured that human exploration is driven by a
combination of extrinsic and intrinsic reward variables (97). Extrinsic
rewards are task specific, such as classification of objects, whereas
intrinsic rewards are task independent and hence more general, such as
curiosity-driven exploration. Experiments suggest that humans primarily
use six types of exploratory movements when exploring objects todetermine their properties (98). Hence, there have been studies onacquiring these specialized closed-loop policies based on intrinsic
rewards such as curiosity (99) or extrinsic rewards such as texture
discrimination ability (100). To achieve efficient exploration with soft
robots, a combination of tactile and proprioceptive feedback will
likely be useful for effectively implementing such reward functions.
A first step toward an autonomous tactile exploration control
architecture, referred to as tactile servoing by the authors, wasproposed by Li et al. (101). By framing the control objective asSCIENCE ROBOTICS | REVIEWAManipulation
Information processing
Body

Sensors
Environment

B

Exploration
Information processing
Body

Sensors
Environment

Flow of energy

C

Reaction
Information processing
Body

Sensors
Environment

Flow of information

adaptation (106, 107). Otherwise, closed-loop reaction using tactile
sensing is similar to the closed-loop manipulation problem and isoften implemented in parallel with manipulation tasks as in the caseof slip detection (108).
The main challenges in whole-body sensing are the organizationand calibration of many spatially distributed multimodal tactile sensing
elements (109). Spatial calibration can be manually performed or
automated using robot kinematics and action inference techniques(110). Data-driven methods are also promising for end-to-end models
without an explicit kinematic/dynamic calibration (111). The mostrecent and comprehensive whole-body tactile sensing research wasable to self-organize and self-calibrate 1260 multimodal sensing units
and implement a hierarchical task manager composed of the fusionof a balance controller, a self-collision avoidance system, and a skincompliance controller (112).

CREDIT: ADAPTED BY A. KITTERMAN/SCIENCE ROBOTICS FROM MATERIALS PUBLISHED BY JOHAN63/ISTOCK (ELEPHANT), MIKE VEITCH/ ALAMYSTOCK PHOTO (OCTOPUS), BENJAMIN
TORODE/GETTY IMAGES (CAT), OSADA/SÉGUIN/DRAASM/STANFORD UNIVERSITY (DIVER), SAGE [ROBOT HAND (69)], IEEE [DIVER (128)], AND SCIENCE ROBOTICS (DRONE) (129)Fig. 5. Closed-loop tasks where tactile sensing is essential. These tasks primarily
differ depending on the system that determines the objective (denoted by theshaded boxes). The middle row consists of biological demonstrations of the tasks.
The bottom row contains examples (69, 128, 129) of these capabilities in current
rigid robots, which we expect to further improve in parallel with the integration of
e-skins, soft robotics, and machine learning. Note that the presented division is notstrict, and real-world tasks often involve a combination of all three elementarytasks. Yellow arrows indicate energy flow; blue arrows indicate information flow.
(A) Manipulation involves altering the state of an external object to a desired set
point using internal actuators. (B) Exploration involves motion of the body toaccount for uncertainties in the environment based on somatosensory feedback.
(C) Reaction involves estimating and responding to environmental cues such thatthe body remains in a desired state.

the problem of following a trajectory in the sensor feature space,various autonomous sensory exploration strategies emerged. The
emergent exploration strategies included maintaining contact withan object, edge tracking, and shape exploration of an unknown
object. Exploration has also been framed as a force and pose control
problem on an unknown object using tactile sensors for feedback(102). Additional tactile information obtained during the process
was then used to estimate the compliance of the object. Recent works
integrated active exploration with object discrimination (103). However, the midlevel processes were independent from the high-level
exploration strategy, and the proposed algorithm was therefore
relatively inefficient and slow. The next challenge in this area isto develop exploration strategies that run simultaneously and are
regulated by the tactile feature extraction process. Such an algorithm
would allow robots equipped with e-skins to efficiently process their
sensory information to make informed decisions on how to move
within the world to gather information and achieve at least locally
optimal exploration strategies.
Reaction
Whole-body tactile skins are required for reacting to active environmental forces applied by external agents (104). Here, the control
objective is to estimate and react to external forces such that the
body remains stable while executing a behavior. Often, the safetyof the external agent, typically a human, becomes a higher priority
than robot stability (105). Because reaction typically involves HRI,
additional challenges arise from safety, context prediction, andShih et al., Sci. Robot. 5, eaaz9239 (2020)

22 April 2020

The fields of e-skins and soft robotics have both experienced rapid
progress in recent years. However, incorporating advances from bothfields to produce intelligent, autonomous soft robots is a challenging
task that will require progress in several key areas (Fig. 6). Here, weoutline major open questions in this area and identify areas ofresearch that could provide solutions.
Design and fabrication
The primary future challenges of developing sensor arrays for soft
robots will be to design stretchable sensory arrays with wide bandwidth and high dynamic range, resolution, and sensitivity. In addition,
multimodal sensing would increase the robots’ knowledge of theirenvironment, leading to richer HRI (Fig. 6, A and B). Sensing ofpressure, shear, and vibration and even detecting the presence ofchemical and biological markers in the environment would be usefulfor a wide range of applications, including manipulation, disaster
response, and manufacturing. Recent efforts on integrating bacteria
cells into soft robots have made it possible to directly detect anddisplay chemical information on soft robots (44). Other major design challenges include choosing how many sensors to integrate
into a skin and deciding how to place them intelligently. Resources
are limited and require careful allocation.
Machine learning and information processing
Advancing the intelligence of soft robots will also require computational models that can extract useful information from sensor arrays.
However, the details of how to develop and implement such algorithms are unclear. For example, deciding which algorithms can mostefficiently accomplish tasks in classification, regression, and fault
detection; whether neural networks should be used; which architectures are easiest to train; and whether there are trade-offs between
efficiency and reliability are all open questions that need to beaddressed. Answering these questions will necessitate collaboration
among computer and data scientists, materials engineers, andneuroscientists. The result will be robots that are more aware ofthemselves, their environment, and their interactions with humans,
yielding richer and more productive experiences for human end users.
Affective touch is a crucial form of nonverbal communicationthat humans use daily and is one application that would benefit fromthe combination of e-skins, soft robotics, and machine learning. In
contrast, most robots currently are unable to understand gestures
7 of 11

Downloaded from http://robotics.sciencemag.org/ at Yale University on April 23, 2020

OPPORTUNITIES AND OUTLOOKSCIENCE ROBOTICS | REVIEW
A Multimodal sensing

B Wearable robots

C Closed-loop morphing

D Dexterous manipulation

CREDIT: A. KITTERMAN/SCIENCE ROBOTICS

such as a pat on the back because either they do not have the sensors
necessary to measure the interaction or they are not able to makesense of the affective contact.

highest spatial resolution (around 2 mm), as evident from the dense
mechanoreceptor distribution at the human fingertip (119, 120).
On the other end of the spectrum, tactile reaction likely requires thelowest spatial resolution, as suggested by the poor spatial resolution
Shape sensing
across other parts of the body. Tactile manipulation lies in between,
Despite the recent progress in shape-sensing e-skins, it is unclear how with an expected spatial resolution of 5 mm (121).
to extend these advances to the wide range of soft robots presented
The type and the distribution of mechanoreceptors across the bodyin literature. Soft robots experience large strains and complex de- also suggest the type of sensor technologies that would be useful forformations; key challenges include increasing the stretchability of a particular task. Humans use distinct sensors for static and dynamic
shape-sensing skins and improving the resolution of sensors to cues. Low-bandwidth mechanoreceptors (10 to 50 Hz) can be founddetect small curvatures.
mainly in the fingertip and would be essential for tactile exploration
Once the field has reliable solutions for soft robot proprioception, (122). Higher-bandwidth mechanoreceptors (50 to 400 Hz), which
it is conceivable that shape feedback would enable controlled shape respond to the vibrations induced during object slippage, arechange in robots. Current soft robots are not able to morph into distributed primarily at the palm of the hand (123). The response
specific configurations, yet even simple shape change has led to and the sensing areas of the mechanoreceptors are strongly depeninnovative solutions for a wide range of tasks, such as obstacle avoid- dent on the skin morphology. Hence, it is vital to consider the designance (9), rolling locomotion (113), underwater locomotion (114), of the body and the motion capabilities for mimicking the dynamic
and camouflage (115). Larger shape changes could result in robots receptors in our body.
that switch between morphologies and corresponding locomotion
Other insights can be gained by extending such an analysis togaits on demand (Fig. 6C).
invertebrate biological organisms, such as octopuses. An octopus
has several receptors, primarily chemoreceptors, located on each
Feedback control
sucker. In addition, the octopus has strain receptors associated with
Using sensorized skins to close the loop has the potential to improve its muscles and a relatively large brain for processing its receptor
the ability of soft robots to react to their environment, to locomote, information. Despite these capabilities, it has a poor proprioceptive
to explore, and to manipulate objects using their deformable bodies sense and cannot estimate the overall shape and location of external(Fig. 6D). The use of soft tactile sensors for closed-loop control is objects that it is handling. There is local proprioceptive feedback instill in its nascency. The few relevant studies in this area used low-­ each arm for low-level control, but the only feedback to the central
dimensional soft strain sensors for closed-loop kinematic or force nervous system comes through vision (124). Wells (125) conjectured
control (73, 116, 117). This is surprising given the wealth of litera- that in flexible animals, motor control is hierarchical and proture on soft sensing technologies and considering the intended prioceptive information must be used locally. Contrary to popularapplication of these sensors (7). One reason for this discrepancy belief, the performance of an octopus in manipulation tasks is poor.
could be that soft sensors were originally developed for wearable Therefore, it might be necessary to incorporate rigid components indevices and therefore used only for state estimation. Another reason fully soft robots, if they are to be used for tactile-based closed-loop
could be the demanding performance expectations placed on soft sensors. control tasks.
Although it would be useful to develop drift-free, linear sensors withhigh gauge factors, biology suggests that workarounds are possible. Outlook
For example, the human tactile sensing system is hysteretic, nonlin- Researchers have developed many interesting forms of actuation
ear, time varying, and slow. Nature adapted to these drawbacks by that more closely mimic the functionality and capabilities found indeveloping hyper-redundant sensing networks and intelligent data nature. The next step for the field is to develop biologically inspired
processing techniques (118).
tactile sensing for soft-bodied robots that can safely interact with,
Along the same lines, various sensor design strategies can be and explore, their environments. Current work tends to concentrate
found by observing nature. Tactile exploration likely requires the on the design and fabrication of soft robots and explores how
Shih et al., Sci. Robot. 5, eaaz9239 (2020)

22 April 2020

8 of 11

Downloaded from http://robotics.sciencemag.org/ at Yale University on April 23, 2020

Fig. 6. Potential capabilities and technologies that could be achieved with e-skins and soft robotics. (A) Multimodal sensing would be useful during manipulation
for detecting gripper states, object properties, and events such as contact and slip. (B) E-skins with an integrated human-robot interface could enable seamless assistive
wearable robots and intuitive teleoperation of anthropomorphic robots. (C) When paired with the appropriate actuators, shape sensing would enable closed-loop changesof shape. (D) Closed-loop control algorithms would enable soft robots equipped with e-skins to succeed when performing complex tasks, including in-arm manipulation.

SCIENCE ROBOTICS | REVIEW
machine learning can enhance soft robot perception. In the shortterm, the field can focus on deployable, high-resolution sensor skins,
algorithms for processing the dense sensor information, and reliable feedback control for soft robots. The longer-term goal is robots
that can touch and feel with the sensitivity and perception of natural
systems.
We believe that future societies will include robots tightly integrated with humanity. This includes in-home, assistive robots thatcan sense and understand gestures such as a pat on the back, collaborative robots that work alongside humans, and exploratory robots
that can navigate the unpredictable real world.
REFERENCES AND NOTES

Shih et al., Sci. Robot. 5, eaaz9239 (2020)

22 April 2020

38. R. Deimel, O. Brock, A novel type of compliant and underactuated robotic handfor dexterous grasping. Int. J. Robot. Res. 35, 161–185 (2016).

39. T. G. Thuruthel, B. Shih, C. Laschi, M. T. Tolley, Soft robot perception using embedded soft
sensors and recurrent neural networks. Sci. Robot. 4, eaav1488 (2019).
40. L. Scimeca, P. Maiolino, D. Cardin-Catalan, A. P. del Pobil, A. Morales, and F. Iida,
Non-destructive robotic assessment of mango ripeness via multi-point soft haptics, in2019 International Conference on Robotics and Automation (ICRA) (IEEE, 2019), pp. 1821–1826.

41. R. A. Bilodeau, E. L. White, R. K. Kramer, Monolithic fabrication of sensors and actuators ina soft robotic gripper, in 2015 IEEE/RSJ International Conference on Intelligent Robots andSystems (IROS) (IEEE), pp. 2324–2329.

42. N. Farrow, N. Correll, A soft pneumatic actuator that can sense grasp and touch, in 2015 IEEE/
RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE), pp. 2317–2323.

43. H. Zhao, K. OBrien, S. Li, R. F. Shepherd, Optoelectronically innervated soft prosthetic
hand via stretchable optical waveguides. Sci. Robot. 1, eaai7529 (2016).

44. K. B. Justus, T. Hellebrekers, D. D. Lewis, A. Wood, C. Ingham, C. Majidi, P. R. LeDuc, C. Tan,
A biosensing soft robot: Autonomous parsing of chemical signals through integratedorganic and inorganic interfaces. Sci. Robot. 4, eaax0765 (2019).
45. A. Alspach, J. Kim, K. Yamane, Design of a soft upper body robot for physical humanrobot interaction, in 2015 IEEE-RAS 15th International Conference on Humanoid Robots(Humanoids) (IEEE, 2015), pp. 290–296.
46. P. Mittendorfer, E. Dean, G. Cheng, 3D spatial self-organization of a modular artificial skin,in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 14 to
18 September 2014 (IEEE, 2014), pp. 3969–3974.
47. J. W. Booth, D. Shah, J. C. Case, E. L. White, M. C. Yuen, O. Cyr-Choiniere, R. Kramer-Bottiglio,
Omniskins: Robotic skins that turn inanimate objects into multi- functional robots.
Sci. Robot. 3, eaat1853 (2018).

9 of 11

Downloaded from http://robotics.sciencemag.org/ at Yale University on April 23, 2020

1. D. Rus, M. T. Tolley, Design, fabrication and control of soft robots. Nature 521, 467–475(2015).
2. S. Wagner, S. P. Lacour, J. Jones, I. H. Pai-hui, J. C. Sturm, T. Li, Z. Suo, Electronic skin:Architecture and components. Phys. E. 25, 326–334 (2004).
3. J. Case, M. Yuen, M. Mohammed, R. Kramer, Sensor skins: An overview, in Stretchable
Bioelectronics for Medical Devices and Systems (Springer, 2016), pp. 173–191.
4. M. Amjadi, K.-U. Kyung, I. Park, M. Sitti, Stretchable, skin-mountable, and wearable strain
sensors and their potential applications: A review. Adv. Funct. Mater. 26, 1678–1698 (2016).
5. N. Yogeswaran, W. Dang, W. T. Navaraj, D. Shakthivel, S. Khan, E. O. Polat, S. Gupta,
H. Heidari, M. Kaboli, L. Lorenzelli, G. Cheng, R. Dahiya, New materials and advances
in making electronic skin for interactive robots. Adv. Robot. 29, no. 21, 1359–1373 (2015).
6. R. Dahiya, N. Yogeswaran, F. Liu, L. Manjakkal, E. Burdet, V. Hayward, H. Jörntell,Large-area soft e-skin: The challenges beyond sensor designs. Proc. IEEE 107, 2016–2033(2019).
7. H. Wang, M. Totaro, L. Beccai, Toward perceptive soft robots: Progress and challenges.
Adv. Sci. 5, 1800541 (2018).
8. K. Chin, T. Hellebrekers, C. Majidi, Machine learning for soft robotic sensing and control.
Adv. Intell. Syst. 2020, 1900171 (2020).
9. D. S. Shah, M. C. Yuen, L. G. Tilton, E. J. Yang, R. Kramer-Bottiglio, Morphing robots using
robotic skins that sculpt clay. IEEE Robot. Autom. Lett. 4, 2204–2211 (2019).
10. B. Shih, D. Drotman, C. Christianson, Z. Huo, R. White, H. I. Christensen, M. T. Tolley,
Custom soft robotic gripper sensor skins for haptic object visualization, in 2017 IEEE/RSJ IROS(IEEE, 2017), pp. 494–501.
11. T. Arnold, M. Scheutz, The tactile ethics of soft robotics: Designing wisely for human–robot
interaction. Soft Robot. 4, 81–87 (2017).
12. S. Sundaram, P. Kellnhofer, Y. Li, J.-Y. Zhu, A. Torralba, W. Matusik, Learning the signaturesof the human grasp using a scalable tactile glove. Nature 569, 698–702 (2019).
13. J. Viventi, D.-H. Kim, L. Vigeland, E. S. Frechette, J. A. Blanco, Y.-S. Kim, A. E. Avrin,
V. R. Tiruvadi, S.-W. Hwang, A. C. Vanleer, D. F. Wulsin, K. Davis, C. E. Gelber, L. Palmer,
J. Van der Spiegel, J. Wu, J. Xiao, Y. Huang, D. Contreras, J. A. Rogers, B. Litt, Flexible,
foldable, actively multiplexed, high-density electrode array for mapping brain activity
in vivo. Nat. Neurosci. 14, 1599–1605 (2011).
14. D.-H. Kim, J.-H. Ahn, W. M. Choi, H.-S. Kim, T.-H. Kim, J. Song, Y. Y. Huang, Z. Liu, C. Lu,
J. A. Rogers, Stretchable and foldable silicon integrated circuits. Science 320,
507–511 (2008).
15. T. Someya, Y. Kato, T. Sekitani, S. Iba, Y. Noguchi, Y. Murase, H. Kawaguchi, T. Sakurai,
Conformable, flexible, large-area networks of pressure and thermal sensors with organic
transistor active matrixes. Proc. Natl. Acad. Sci. U.S.A. 102, 12321–12325 (2005).
16. T. Sekitani, Y. Noguchi, K. Hata, T. Fukushima, T. Aida, T. Someya, A rubberlike stretchable
active matrix using elastic conductors. Science 321, 1468–1472 (2008).
17. X. Ren, K. Pei, B. Peng, Z. Zhang, Z. Wang, X. Wang, P. K. Chan, A low-operating-power
and flexible active-matrix organic-transistor temperature-sensor array. Adv. Mater. 28,
4832–4838 (2016).
18. S. Wang, J. Xu, W. Wang, G.-J. N. Wang, R. Rastak, F. Molina-Lopez, J. W. Chung, S. Niu,
V. R. Feig, J. Lopez, T. Lei, S.-K. Kwon, Y. Kim, A. M. Foudeh, A. Ehrlich, A. Gasperini, Y. Yun,
B. Murmann, J. B.-H. Tok, Z. Bao, Skin electronics from scalable fabrication of an intrinsicallystretchable transistor array. Nature 555, 83–88 (2018).
19. Z. Huang, Y. Hao, Y. Li, H. Hu, C. Wang, A. Nomoto, T. Pan, Y. Gu, Y. Chen, T. Zhang, W. Li,
Y. Lei, N. H. Kim, C. Wang, L. Zhang, J. W. Ward, A. Maralani, X. Li, M. F. Durstock, A. Pisano,
Y. Lin, S. Xu, Three-dimensional integrated stretchable electronics. Nat. Electronics,
473–480 (2018).
20. B. Shih, C. Christianson, K. Gillespie, S. Lee, J. Mayeda, Z. Huo, M. T. Tolley, Design
considerations for 3D printed, soft, multimaterial resistive sensors for soft robotics.
Front. Robot. AI 6, 30 (2019).

21. B. C.-K. Tee, A. Chortos, A. Berndt, A. K. Nguyen, A. Tom, A. McGuire, Z. C. Lin, K. Tien,
W.-G. Bae, H. Wang, P. Mei, H.-H. Chou, B. Cui, K. Deisseroth, T. N. Ng, Z. Bao,
A skin-inspired organic digital mechanoreceptor. Science 350, 313–316 (2015).
22. A. Chortos, J. Liu, Z. Bao, Pursuing prosthetic electronic skin. Nat. Mater. 15, 937–950 (2016).
23. Y. Kim, A. Chortos, W. Xu, Y. Liu, J. Y. Oh, D. Son, J. Kang, A. M. Foudeh, C. Zhu, Y. Lee,
S. Niu, J. Liu, R. Pfattner, Z. Bao, T.-W. Lee, A bioinspired flexible organic artificial afferent
nerve. Science 360, 998–1003 (2018).
24. W. W. Lee, Y. J. Tan, H. Yao, S. Li, H. H. See, M. Hon, K. A. Ng, B. Xiong, J. S. Ho, C. Tee,A neuro-inspired artificial peripheral nervous system for scalable electronic skins.
Sci. Robot. 4, eaax2198 (2019).
25. P. A. Xu, A. Mishra, H. Bai, C. Aubin, L. Zullo, R. F. Shepherd, Optical lace for synthetic
afferent neural networks. Sci. Robot. 4, eaaw6304 (2019).
26. H. A. Sonar, M. C. Yuen, R. Kramer-Bottiglio, J. Paik, An any-resolution pressure
localization scheme using a soft capacitive sensor skin, in 2018 IEEE International
Conference on Soft Robotics (RoboSoft) (IEEE, 2018), pp. 170–175.
27. Y.-L. Park, B.-R. Chen, R. J. Wood, Design and fabrication of soft artificial skin using
embedded microchannels and liquid conductors. IEEE Sensors J. 12, 2711–2718 (2012).
28. T. Hellebrekers, O. Kroemer, C. Majidi, Soft magnetic skin for continuous deformation
sensing. Adv. Intell. Syst. 1, 1900025 (2019).
29. D. H. Kim, N. Lu, R. Ma, Y. S. Kim, R. H. Kim, S. Wang, J. Wu, S. M. Won, H. Tao, A. Islam,
K. J. Yu, T. I. Kim, R. Chowdhury, M. Ying, L. Xu, M. Li, H. J. Chung, H. Keum, M. McCormick,
P. Liu, Y. W. Zhang, F. G. Omenetto, Y. Huang, T. Coleman, J. A. Rogers, Epidermal
electronics. Science 333, 838–843 (2011).
30. M. Kaltenbrunner, T. Sekitani, J. Reeder, T. Yokota, K. Kuribara, T. Tokuhara, M. Drack,
R. Schwödiauer, I. Graz, S. Bauer-Gogonea, S. Bauer, T. Someya, An ultra-lightweight
design for imperceptible plastic electronics. Nature 499, 458–463 (2013).
31. R. F. Shepherd, F. Ilievski, W. Choi, S. A. Morin, A. A. Stokes, A. D. Mazzeo, X. Chen,
M. Wang, G. M. Whitesides, Multigait soft robot. Proc. Natl. Acad. Sci. U.S.A. 108,20400–20403 (2011).
32. D. Drotman, S. Jadhav, M. Karimi, P. Dezonia, M. T. Tolley, 3D printed soft actuators for a
legged robot capable of navigating unstructured terrain, in 2017 IEEE International
Conference on Robotics and Automation (ICRA) (IEEE, 2017), pp. 5532–5538.
33. E. W. Hawkes, L. H. Blumenschein, J. D. Greer, A. M. Okamura, A soft robot that navigates
its environment through growth. Sci. Robot. 2, eaan3028 (2017).
34. R. K. Katzschmann, J. DelPreto, R. MacCurdy, D. Rus, Exploration of underwater life
with an acoustically controlled soft robotic fish. Sci. Robot. 3, eaar3449 (2018).
35. M. Wehner, R. L. Truby, D. J. Fitzgerald, B. Mosadegh, G. M. Whitesides, J. A. Lewis,
R. J. Wood, An integrated design and fabrication strategy for entirely soft, autonomous
robots. Nature 536, 451–455 (2016).
36. G. Soter, A. Conn, H. Hauser, J. Rossiter, Bodily aware soft robots: Integration of
proprioceptive and exteroceptive sensors, in 2018 IEEE International Conference on
Robotics and Automation (ICRA) (IEEE, 2018), pp. 2448–2453.
37. L. Chin, J. Lipton, M. C. Yuen, R. Kramer-Bottiglio, D. Rus, Automated recycling separation
enabled by soft robotic material classification, in 2019 2nd IEEE International Conference
on Soft Robotics (RoboSoft) (IEEE, 2019), pp. 102–107.

SCIENCE ROBOTICS | REVIEWShih et al., Sci. Robot. 5, eaaz9239 (2020)

22 April 2020

74. S. Y. Kim, R. Baines, J. Booth, N. Vasios, K. Bertoldi, R. Kramer-Bottiglio, Re-configurable
soft body trajectories using unidirectionally stretchable composite laminae.
Nat. Commun. 10, 3464 (2019).
75. N. Cheney, R. MacCurdy, J. Clune, H. Lipson, Unshackling evolution: evolving soft robots
with multiple materials and a powerful generative encoding, in Proceedings of the 15th
Annual Conference on Genetic and Evolutionary Computation (ACM, 2013), pp. 167–174.
76. T. Hoshi, H. Shinoda, 3D shape measuring sheet utilizing gravitational and geomagnetic
fields, in 2008 SICE Annual Conference, 20 to 22 August 2008, pp. 915–920.
77. A. Hermanis, R. Cacurs, M. Greitans, Acceleration and magnetic sensor network for shapesensing. IEEE Sensors J. 16, 1271–1280 (2016).
78. C. Rendl, D. Kim, S. Fanello, P. Parzer, C. Rhemann, J. Taylor, M. Zirkl, G. Scheipl,
T. Rothlnder, M. Haller, S. Izadi, Flexsense: A transparent self-sensing deformable surface,in Proceedings of the 27th Annual ACM Symposium on User Interface Software andTechnology (UIST’14), Honolulu, Hawaii, USA (ACM, 2014), pp. 129–138.
79. T. L. T. Lun, K. Wang, J. D. L. Ho, K.-H. Lee, K. Y. Sze, K.-K. Kwok, Real-time surface shapesensing for soft and flexible structures using fiber Bragg gratings. IEEE Robot. Autom. Lett.
4, 1454–1461 (2019).
80. L. Pinto, D. Gandhi, Y. Han, Y.-L. Park, A. Gupta, The curious robot: Learning visual
representations via physical interactions, in European Conference on Computer Vision(Springer, 2016) pp. 3–18.
81. M. R. Cutkosky, R. D. Howe, W. R. Provancher, Force and tactile sensors, in Springer
Handbook of Robotics, B. Siciliano, O. Khatib, Eds. (Springer, 2008), pp. 455–476.
82. M. Li, Y. Bekiroglu, D. Kragic, A. Billard, Learning of grasp adaptation through experienceand tactile sensing, in 2014 IEEE/RSJ International Conference on Intelligent Robots andSystems (IEEE, 2014), pp. 3339–3346.
83. N. Wettels, V. J. Santos, R. S. Johansson, G. E. Loeb, Biomimetic tactile sensor array.
Adv. Robot. 22, 829–849 (2008).
84. F. Veiga, J. Peters, T. Hermans, Grip stabilization of novel objects using slip prediction.
IEEE Trans. Haptics 11, 531–542 (2018).
85. Y. Chebotar, M. Kalakrishnan, A. Yahya, A. Li, S. Schaal, S. Levine, Path integral guided
policy search, in 2017 IEEE International Conference on Robotics and Automation (ICRA)(IEEE, 2017), pp. 3381–3388.
86. H. Van Hoof, N. Chen, M. Karl, P. van der Smagt, J. Peters, Stable reinforcement learningwith autoencoders for tactile and visual data, in 2016 IEEE/RSJ International Conference onIntelligent Robots and Systems (IROS) (IEEE, 2016), pp. 3928–3934.
87. Y. Ohmura, Y. Kuniyoshi, Humanoid robot which can lift a 30kg box by whole bodycontact and tactile feedback, in 2007 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IEEE, 2007) pp. 1136–1141.
88. P. Mittendorfer, E. Yoshida, G. Cheng, Realizing whole-body tactile interactions
with a self-organizing, multi-modal artificial skin on a humanoid robot. Adv. Robot. 29,
51–67 (2015).
89. K. Hertkorn, M. A. Roa, C. Borst, Planning in-hand object manipulation with multifingered
hands considering task constraints, in 2013 IEEE International Conference on Robotics andAutomation (IEEE, 2013), pp. 617–624.
90. H. Van Hoof, T. Hermans, G. Neumann, J. Peters, Learning robot in-hand manipulation
with tactile features, in 2015 IEEE-RAS 15th International Conference on Humanoid Robots(Humanoids) (IEEE, 2015), pp. 121–127.
91. G. E. Loeb, G. A. Tsianos, J. A. Fishel, N. Wettels, S. Schaal, Understanding haptics by
evolving mechatronic systems, in Progress in brain research (Elsevier, 2011), vol. 192,
pp. 129–144.
92. N. Jamali, C. Sammut, Majority voting: Material classification by tactile sensing using
surface texture. IEEE Trans. Robot. 27, 508–521 (2011).
93. J. Sinapov, V. Sukhoy, R. Sahai, A. Stoytchev, Vibrotactile recognition and categorizationof surfaces by a humanoid robot. IEEE Trans. Robot. 27, 488–497 (2011).
94. H. Yousef, M. Boukallel, K. Althoefer, Tactile sensing for dexterous in-handmanipulation in robotics––A review. Sensors Actuators A Phys. 167, 171–187 (2011).

95. N. Jamali, C. Sammut, Material classification by tactile sensing using surface textures, in2010 IEEE International Conference on Robotics and Automation (IEEE, 2010), pp. 2336–2341.

96. B. S. Homberg, R. K. Katzschmann, M. R. Dogar, D. Rus, Haptic identification of objects
using a modular soft robotic gripper, in Intelligent Robots and Systems (IROS), 2015 IEEE/
RSJ International Conference on (IEEE, 2015), pp. 1698–1705.

97. J. Gottlieb, P.-Y. Oudeyer, M. Lopes, A. Baranes, Information-seeking, curiosity, andattention: Computational and neural mechanisms. Trends Cogn. Sci. 17, 585–593 (2013).
98. S. J. Lederman, R. L. Klatzky, Hand movements: A window into haptic object recognition.
Cogn. Psychol. 19, 342–368 (1987).
99. L. Pape, C. M. Oddo, M. Controzzi, C. Cipriani, A. Förster, M. C. Carrozza, J. Schmidhuber,
Learning tactile skills through curious exploration. Front. Neurorobot. 6, 6 (2012).
100. J. A. Fishel, G. E. Loeb, Bayesian exploration for intelligent identification of textures.
Front. Neurorobot. 6, 4 (2012).
101. Q. Li, C. Schürmann, R. Haschke, H. Ritter, A control framework for tactile servoing,in Robotics: Science and Systems (2013).

10 of 11

Downloaded from http://robotics.sciencemag.org/ at Yale University on April 23, 2020

48. P. Mittendorfer, G. Cheng, 3D surface reconstruction for robotic body parts withartificial skins, in 2012 IEEE/RSJ International Conference on Intelligent Robots andSystems, 7 to 12 October 2012 (IEEE, 2012), pp. 4505–4510.
49. R. K. Kramer, C. Majidi, R. Sahai, R. J. Wood, Soft curvature sensors for joint angle
proprioception, in Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International
Conference on (IEEE, 2011)pp. 1919–1926.
50. J. T. Muth, D. M. Vogt, R. L. Truby, Y. Mengüc, D. B. Kolesky, R. J. Wood, J. A. Lewis,
Embedded 3D printing of strain sensors within highly stretchable elastomers. Adv. Mater.
26, 6307–6312 (2014).
51. A. Alspach, J. Kim, K. Yamane, Design and fabrication of a soft robotic hand and arm
system, in 2018 IEEE International Conference on Soft Robotics (RoboSoft), 24 to 28 April 2018
(IEEE, 2018), pp. 369–375.
52. J. Jung, M. Park, D. W. Kim, Y.-L. Park, Optically sensorized elastomer air chamber
for proprioceptive sensing of soft pneumatic actuators. IEEE Robot. Autom. Lett. 5,2333–2340 (2020).
53. I. Van Meerbeek, C. De Sa, R. Shepherd, Soft optoelectronic sensory foams
with proprioception. Sci. Robot. 3, eaau2489 (2018).
54. G. Schwartz, B. C.-K. Tee, J. Mei, A. L. Appleton, D. H. Kim, H. Wang, Z. Bao, Flexible
polymer transistors with high pressure sensitivity for application in electronic skinand health monitoring. Nat. Commun. 4, 1859 (2013).
55. J. Byun, Y. Lee, J. Yoon, B. Lee, E. Oh, S. Chung, T. Lee, K.-J. Cho, J. Kim, Y. Hong, Electronicskins for soft, compact, reversible assembly of wirelessly activated fully soft robots.
Sci. Robot. 3, eaas9020 (2018).
56. H. Jeong, L. Wang, T. Ha, R. Mitbander, X. Yang, Z. Dai, S. Qiao, L. Shen, N. Sun, N. Lu,
Modular and reconfigurable wireless e-tattoos for personalized sensing. Adv. Mate. Technol.
4, 1900117 (2019).
57. Y. Mengüc, Y.-L. Park, H. Pei, D. Vogt, P. M. Aubin, E. Winchell, L. Fluke, L. Stirling,
R. J. Wood, C. J. Walsh, Wearable soft sensing suit for human gait measurement.
Int. J. Robot. Res. 33, 1748–1764 (2014).
58. C. M. Boutry, M. Negre, M. Jorda, O. Vardoulis, A. Chortos, O. Khatib, Z. Bao,A hierarchically patterned, bioinspired e-skin able to detect the direction of appliedpressure for robotics. Sci. Robot. 3, eaau6914 (2018).
59. P. Piacenza, S. Sherman, M. Ciocarlie, Data-driven super-resolution on a tactile dome.
IEEE Robot. Autom. Lett. 3, 1434–1441 (2018).
60. C. Larson, J. Spjut, R. Knepper, R. Shepherd, A deformable interface for human touchrecognition using stretchable carbon nanotube dielectric elastomer sensors and deepneural networks. Soft Robot. 6, 611–620 (2019).
61. D. Kim, J. Kwon, B. Jeon, Y.-L. Park, Adaptive calibration of soft sensors using optimaltransportation transfer learning for mass production and long-term usage. Adv. Intell. Syst.
2020, 1900178 (2020).
62. D. Kim, J. Kwon, S. Han, Y.-L. Park, S. Jo, Deep full-body motion network (dfm-net)
for a soft wearable motion sensing suit. IEEE/ASME Trans. Mechatron. 24,
18453663 (2018).
63. M. Riesenhuber, T. Poggio, Hierarchical models of object recognition in cortex.
Nat. Neurosci. 2, 1019–1025 (1999).
64. Y. Bengio, A. Courville, P. Vincent, Representation learning: A review and new
perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2013).
65. P. Mirowski, R. Pascanu, F. Viola, H. Soyer, A. J. Ballard, A. Banino, M. Denil, R. Goroshin,
L. Sifre, K. Kavukcuoglu, D. Kumaran, R. Hadsell, Learning to navigate in complex
environments. arXiv:1611.03673 (2016).
66. T. Chen, S. Gupta, A. Gupta, Learning exploration policies for navigation.
arXiv:1903.01959 (2019).
67. J. Fu, S. Levine, P. Abbeel, One-shot learning of manipulation skills with online dynamics
adaptation and neural network priors, in 2016 IEEE/RSJ International Conference onIntelligent Robots and Systems (IROS) (IEEE, 2016), pp. 4019–4026.
68. A. Rajeswaran, V. Kumar, A. Gupta, G. Vezzani, J. Schulman, E. Todorov, S. Levine, Learningcomplex dexterous manipulation with deep reinforcement learning and demonstrations.
arXiv:1709.10087 (2017).
69. OpenAI, M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. M. Grew, J. Pachocki,
A. Petron, M. Plappert, G. Powell, A. Ray, J. Schneider, S. Sidor, J. Tobin, P. Welinder,
L. Weng, W. Zaremba, Learning dexterous in-hand manipulation. arXiv:1808.00177 (2018).
70. D. P. Bertsekas, Dynamic Programming and Optimal Control (Athena Scientific, 1995), vol. 1.
71. J. Burgner-Kahrs, D. C. Rucker, H. Choset, Continuum robots for medical applications:
A survey. IEEE Trans. Robot. 31, 1261–1280 (2015).
72. T. George Thuruthel, Y. Ansari, E. Falotico, C. Laschi, Control strategies for soft robotic
manipulators: A survey. Soft Robot. 5, 149–163 (2018).
73. J. Morrow, H.-S. Shin, C. Phillips-Grafflin, S. Jang, J. Torrey, R. Larkins, S. Dang, Y.-L. Park,D. Berenson, Improving soft pneumatic actuator fingers through integration of soft
sensors, position and force control, and rigid fingernails. in 2016 IEEE International
Conference on Robotics and Automation (ICRA), 16 to 21 May 2016 (IEEE, 2016),
pp. 5024–5031.

SCIENCE ROBOTICS | REVIEWShih et al., Sci. Robot. 5, eaaz9239 (2020)

22 April 2020

121. F. Mancini, A. Bauleo, J. Cole, F. Lui, C. A. Porro, P. Haggard, G. D. Iannetti, Whole-bodymapping of spatial acuity for pain and touch. Ann. Neurol. 75, 917–924 (2014).
122. A. B. Vallbo, R. S. Johansson, Properties of cutaneous mechanoreceptors in the human
hand related to touch sensation. Hum. Neurobiol. 3, 3–14 (1984).
123. R. S. Johansson, J. R. Flanagan, Coding and use of tactile signals from the fingertips
in object manipulation tasks. Nat. Rev. Neurosci. 10, 345–359 (2009).
124. M. J. Wells, Octopus: Physiology and Behaviour of an Advanced Invertebrate (Springer
Science & Business Media, 2013).
125. M. Wells, Tactile discrimination of surface curvature and shape by the octopus. J. Exp. Biol.
41, 433–445 (1964).
126. K. Takei, T. Takahashi, J. C. Ho, H. Ko, A. G. Gillies, P. W. Leu, R. S. Fearing, A. Javey,
Nanowire active-matrix circuitry for low-voltage macroscale artificial skin. Nat. Mater. 9,
821–826 (2010).
127. W. Lee, Y. Liu, Y. Lee, B. K. Sharma, S. M. Shinde, S. D. Kim, K. Nan, Z. Yan, M. Han, Y. Huang,
Y. Zhang, J.-H. Ahn, J. A. Rogers, Two-dimensional materials in functional threedimensional architectures with applications in photodetection and imaging.
Nat. Commun. 9, 1417 (2018).
128. O. Khatib, X. Yeh, G. Brantner, B. Soe, B. Kim, S. Ganguly, H. S. Stuart, S. Wang,
M. Cutkosky, A. Edsinger, P. Mullins, M. Barham, C. R. Voolstra, K. N. Salama, M. L’Hour,
V. Creuze, Ocean one: A robotic avatar for oceanic discovery. IEEE Roboti. Autom. Mag. 23,
20–29 (2016).
129. D. Falanga, K. Kleber, D. Scaramuzza, Dynamic obstacle avoidance for quadrotors with
event cameras. Sci. Robot. 5, eaaz9712 (2020).
Acknowledgement: We also thank members of the University of California San Diego
Bioinspired Robotics and Design Lab and the Yale Faboratory for helpful comments. Funding:
B.S. and M.T.T. acknowledge support from the Office of Naval Research (grant nos.
N00014-17-1-2062 and N00014-18-1-2277). D.S. was supported by a NASA Space Technology
Research Fellowship (grant 80NSSC17K0164). T.G.T. was supported by the SHERO project, a
Future and Emerging Technologies (FET) program of the European Commission (grant
agreement ID 828818). Z.B. acknowledges support of related research by the Air Force Officeof Scientific Research Materials Chemistry Program (grant no. FA9550-18-1-0143). R.K.-B.
acknowledges support of related research by the NSF Emerging Frontiers and Multidisciplinary
Activities Program (award no. 1830870). Author contributions: B.S., D.S., J.L., and T.G.T
conceptualized the review and wrote the manuscript. Y.-L.P., F.I., Z.B., R.K.-B, and M.T.T guided
the topics of discussion and structure of the review. All authors provided feedback andrevisions. Competing interests: The authors declare that they have no competing financial
interests. Data and materials availability: Correspondence and requests for materials should
be addressed to M.T.T. (email: tolley@ucsd.edu).
Submitted 22 October 2019
Accepted 24 March 2020
Published 22 April 2020
10.1126/scirobotics.aaz9239
Citation: B. Shih, D. Shah, J. Li, T. G. Thuruthel, Y.-L. Park, F. Iida, Z. Bao, R. Kramer-Bottiglio,
M. T. Tolley, Electronic skins and machine learning for intelligent soft robots. Sci. Robot. 5,
eaaz9239 (2020).

11 of 11

Downloaded from http://robotics.sciencemag.org/ at Yale University on April 23, 2020

102. Z. Su, J. A. Fishel, T. Yamamoto, G. E. Loeb, Use of tactile feedback to control exploratory
movements to characterize object compliance. Front. Neurorobot. 6, 7 (2012).
103. M. Kaboli, K. Yao, D. Feng, G. Cheng, Tactile-based active object discrimination and target
object search in an unknown workspace. Auton. Robot. 43, 123–152 (2019).
104. H. Iwata, S. Sugano, Whole-body covering tactile interface for human robot
coordination, in Proceedings 2002 IEEE International Conference on Robotics andAutomation (Cat. No. 02CH37292) (IEEE, 2002), vol. 4, pp. 3818–3824.
105. M. Frigola, A. Casals, J. Amat, Human-robot interaction based on a sensitive bumper skin,in 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems (IEEE, 2006),
pp. 283–287.
106. S. Haddadin, E. Croft, Physical human–robot interaction, in Springer Handbook of robotics(Springer, 2016), pp. 1835–1874.
107. D. Silvera-Tawil, D. Rye, M. Velonaki, Artificial skin and tactile sensing for sociallyinteractive robots: A review. Robot. Auton. Syst. 63, 230–243 (2015).
108. G. Canepa, R. Petrigliano, M. Campanella, D. De Rossi, Detection of incipient object
slippage by skin-like sensing and neural network processing. IEEE Trans. Syst. Man
Cybern. B Cybern. 28, 348–356 (1998).
109. G. Cheng, E. Dean-Leon, F. Bergner, J. R. G. Olvera, Q. Leboutet, P. Mittendorfer,
A comprehensive realization of robot skin: Sensors, sensing, control, and applications.
Proc. IEEE 107, 2034–2051 (2019).
110. A. Del Prete, S. Denei, L. Natale, F. Mastrogiovanni, F. Nori, G. Cannata, G. Metta, Skin
spatial calibration using force/torque measurements, in 2011 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IEEE, 2011), pp. 3694–3700.
111. R. Calandra, S. Ivaldi, M. P. Deisenroth, E. Rueckert, J. Peters, Learning inverse dynamics
models with contacts, in 2015 IEEE International Conference on Robotics and Automation(ICRA) (IEEE, 2015), pp. 3186–3191.
112. E. Dean-Leon, J. R. Guadarrama-Olvera, F. Bergner, G. Cheng, Whole-body active
compliance control for humanoid robots with robot skin, in 2019 International Conferenceon Robotics and Automation (ICRA) (IEEE, 2019), pp. 5404–5410.
113. E. Steltz, A. Mozeika, N. Rodenberg, E. Brown, H. M. Jaeger, JSEL: Jamming skin enabled
locomotion, in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems(2009), pp. 5672–5677.
114. M. Ishida, D. Drotman, B. Shih, M. Hermes, M. Luhar, M. T. Tolley, Morphing structure
for changing hydrodynamic characteristics of a soft underwater walking robot.
IEEE Robot. Autom. Lett. 4, 4163–4169 (2019).
115. J. H. Pikul, S. Li, H. Bai, R. T. Hanlon, I. Cohen, R. F. Shepherd, Stretchable surfaces
with programmable 3D texture morphing for synthetic camouflaging skins. Science 358,
210–214 (2017).
116. H. A. Sonar, A. P. Gerratt, S. P. Lacour, J. Paik, Closed-loop haptic feedback control using
a self-sensing soft pneumatic actuator skin. Soft Robot. 7, 22–29 (2019).
117. J. Wirekoh, L. Valle, N. Pol, Y.-L. Park, Sensorized, flat, pneumatic artificial muscle
embedded with biomimetic microfluidic sensors for proprioceptive feedback. Soft Robot.
6, 768–777 (2019).
118. R. S. Dahiya, G. Metta, M. Valle, G. Sandini, Tactile sensing—From humans to humanoids.
IEEE Trans. Robot. 26, 1–20 (2009).
119. J. C. Stevens, Aging and spatial acuity of touch. J. Gerontol. 47, P35–P40 (1992).
120. R. S. Johansson, A. B. Vallbo, Tactile sensibility in the human hand: Relative and absolutedensities of four types of mechanoreceptive units in glabrous skin. J. Physiol. 286,
283–300 (1979).

Electronic skins and machine learning for intelligent soft robots
Benjamin Shih, Dylan Shah, Jinxing Li, Thomas G. Thuruthel, Yong-Lae Park, Fumiya Iida, Zhenan Bao, Rebecca
Kramer-Bottiglio and Michael T. Tolley

Sci. Robotics 5, eaaz9239.
DOI: 10.1126/scirobotics.aaz9239

http://robotics.sciencemag.org/content/5/41/eaaz9239

RELATED
CONTENT

http://robotics.sciencemag.org/content/robotics/4/32/eaax2352.full
http://robotics.sciencemag.org/content/robotics/4/26/eaav1488.full
http://robotics.sciencemag.org/content/robotics/3/24/eaau6914.full
http://robotics.sciencemag.org/content/robotics/3/19/eaat3818.full

REFERENCES

This article cites 84 articles, 9 of which you can access for free
http://robotics.sciencemag.org/content/5/41/eaaz9239#BIBL

PERMISSIONS

http://www.sciencemag.org/help/reprints-and-permissions

Use of this article is subject to the Terms of Service
Science Robotics (ISSN 2470-9476) is published by the American Association for the Advancement of Science, 1200
New York Avenue NW, Washington, DC 20005. The title Science Robotics is a registered trademark of AAAS.
Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancementof Science. No claim to original U.S. Government Works

Downloaded from http://robotics.sciencemag.org/ at Yale University on April 23, 2020

ARTICLE TOOLS