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Biology to Technology in Active Touch
Sensing – Introduction to the Special Section
Yon Visell, Nathan Lepora, Mitra J. Z. Hartmann, and Vincent Hayward
Index Terms—Active touch, active perception, haptics, robotics, animals

Ç
1

INTRODUCTIONR

ECENT years have seen increased study of the role ofactive touch sensing in animal behavior. Touch is an
important means through which animals acquire information
about their surroundings. The behavioral tasks enabled by
active touch, and the goals that these tasks subserve, areextraordinarily diverse. They may range from locomotion(walking, climbing, and crawling), to foraging, feeding,grooming, and tool use. In most cases, these tasks would beimpossible to complete without touch sensation. This notion
should be familiar to anyone who has dismissed the possibility of eating after a visit to the dentist that included local
anesthesia. The close interplay between movement and somatosensation is a distinctive hallmark of the haptic modality.
Our understanding of haptic perception has built on
research that has unfolded over the course of a century.
This pace stands in stark contrast to the rapid advances inrobotic systems, engineering design, and control that haveoccurred over a much shorter time span, measured in years
or, at most, decades. Even as the operational capabilities ofsuch systems has expanded, it has become increasingly
clear that touch sensing is indispensable for robotic systems
that are required to function effectively in real-world environments. Many tactile sensing technologies have beendeveloped during the last few decades [14], but the important role played by motor behavior in touch sensing is only
beginning to receive wide attention in the robotics community. This has served as further motivation for the present
special section on active touch sensing in humans, robots,and other animals.
Active touch sensing is recovering information about
the world by “touching” rather than “being touched” [6].
There are differences of opinion, however, about the extent
to which touching motions must be “purposeful” in order to

Y. Visell is with the Department of Electrical and Computer Engineering,Media Arts and Technology Graduate Program, University of California,Santa Barbara, CA 93106. E-mail: yonvisell@ece.ucsb.edu.
N. Lepora is with the Department of Engineering Mathematics and Bristol
Robotics Laboratory, University of Bristol, Bristol, BS8 1TH, United
Kingdom. E-mail: n.lepora@bristol.ac.uk.
M.J.Z. Hartmann is with the Departments of Mechanical Engineering andBiomedical Engineering, Northwestern University, Evanston, IL 60208.
E-mail: hartmann@northwestern.edu.
V. Hayward is with the Institute of Intelligent Systems and Robotics,
Sorbonne Universits, UPMC Univ Paris 06, UMR 7222, ISIR, F-75005,Paris, France. E-mail: vincent.hayward@isir.upmc.fr.

For information on obtaining reprints of this article, please send e-mail to:
reprints@ieee.org, and reference the Digital Object Identifier below.
Digital Object Identifier no. 10.1109/TOH.2016.2571458

qualify as active touch. While “passive touch” refers unambiguously to the activation of mechanoreceptors by an external force, the term “active touch” embeds both physiologicaland psychological components [4], [5], [6], [8], [9], [10]. The
physiological component is the activation of mechanoreceptors via self–generated movement, while the psychologicalcomponent is the intent of the movement: either to displace alimb to a different position in space (e.g., reaching or locomotion), or to acquire sensory information (e.g., when determining fabric texture or when manipulating a tool) [10]. For
example, walking would not typically be used as an example
of “active touch,” but tapping one’s foot against mud tosense compliance would be – even though these two behaviors might actively stimulate the foot in similar manners.
In engineering language, active touch refers to the idea ofinterpreting touch–elicited signals that are captured by sensors whose motion is deliberately controlled to facilitate
information gain or to otherwise achieve behavioral goals
that depend on it [3].
Thus, the term “active” is applied to suggest an essential
role of motor behavior in eliciting or shaping sensory
signals, at least when the movement is intended to capture
information via touch. This can be contrasted with familiar
situations such as stationary listening to an auditory scene,
which might involve cognitive activity on the part of the listener, but which are otherwise largely independent of themovements of the listener. The movements involved in
active sensing may be deliberate, or they may be reflexive,driven by hardwired behavioral patterns or activity controlled at earlier stages in neural processing. In an engineered system, the former might refer to a reactive
algorithm for exploration that adapts motor behavior towhat is felt in a continuous fashion, while the latter would
suggest a pre–programmed strategy in which a touched surface is systematically probed in a way that elicits sensor signals that are to be interpreted at a later stage.
As such, in animals or robots, the behavior involved may
depend to a greater or lesser degree on the sensory signalsthat are felt. This has led to some ambiguity, and someauthors [3], [5] have preferred the term “active perception”
as opposed to “active sensation.” The former emphasizes
that the process of controlling the sensor is indeed understood to capture sensory information that can refine integrated percepts or achieve related goals of the organism.
In animals, the process of active touch sensing can engagemultiple body parts and employ highly evolved specializations of the sensory and motor systems. In humans, the1939-1412 ß 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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distinctive capabilities of the hands and fingers are especially
important for touch sensing, and we use them to explore theworld in ways that are expressly adapted to extracting
salient object features: following a contour to determine
shape, sliding a finger against an object to gauge surface
texture, and so forth [8]. Likewise, the sensory whiskers(vibrissae) that are present in nearly all mammals aresomatosensory structures that actively probe their environment, either through head motions or muscles specificallyused to move the vibrissae, moving in contact with objects or
fluids in order to extract information through touch [1], [4],[7], [10]. These ideas have been influential in our understanding of haptic perception, and have also influenced thinking
about active touch sensing in robots [13], [16].
Robotic systems that employ active touch sensing havebeen designed to utilize an array of different technologies[14], and have, in some cases, been modeled on specializations seen in biology, especially on the capabilities of humanfingers (e.g., [2]), but also, more recently, on tactile sensing inother mammals (e.g., [16]). A large diversity of embodimentsof robotic systems has been proposed in recent years. Larger
still, however, is the range of potential approaches to tactile
sensing that could be drawn from the many hundreds ofthousands of biological species that utilize it. Consequently,
one might conclude that the number of potential paradigms
for robotic touch sensing that remain to be explored far
exceeds those that have been studied until now.

2CONTENTS OF THE SPECIAL SECTION

The contents of the special section include six papers on
topics addressing diverse challenges of active touch sensing
in robots, humans, or other animals. Of these, two papers
address tactile sensing in non-human animals, two papers
concern tactile behavior for artificial human hands those ofrobots or of upper limb prostheses, one paper provides acomparative examination of three paradigms for active
touch with biomimetic whiskers and fingertips, and one
paper focuses on active touch sensing of surface texture in
humans. Together, these papers highlight the breadth ofresearch in the field, and the extent of cross-disciplinary
inquiry that is frequently involved.
The first papers concern themselves with the great extent
to which mechanical interactions affect touch interactions inanimals. The mammalian vibrissal system has been an
important and highly influential model system for researchon the neuromechanics of active touch. In “Simulations of aVibrissa Slipping along a Straight Edge and an Analysis of
Frictional Effects during Whisking,” Lucie A. Huet andMitra J. Z. Hartmann investigate the effect of friction on
whisker motion and what sensory information is available
to the animal, when a rat palpates, or whisks, an object in its
surroundings with its vibrissae. In “Biomimetic Active
Touch with Fingertips and Whiskers” Nathan F. Lepora
demonstrates that the same approach for biomimetic active
touch applies to three distinct sensing technologies: tactile
vibrissae and two types of tactile fingertip.
Although the vibrissal system has been intensively studied for over a century [11], [12], the tactile capabilities of thepectoral fish fin has been the subject of more recent attention.
Using a robotic model of the pectoral fin, Jeffrey C. Kahn Jr.
and James L. Tangorra in “The Effects of Fluidic Loading on

VOL. 9,NO. 2,

APRIL-JUNE 2016

Underwater Contact Sensing with Robotic Fins and Beams”
demonstrate the important role played by fluidic loading inaffecting the time course of strain in a rayed-fin appendage.
It has increasingly become apparent that in order to realize artificial hands capable of interacting with previously
unknown objects, it is valuable for them to possess not
only adequate motor abilities, but also sensory input. In“Neuromimetic Event-Based Detection for Closed-Loop
Tactile Feedback Control of Upper Limb Prostheses,” Luke
Osborn, Rahul R. Kaliki, Alcimar B. Soares, and Nitish V.
Thakor adopt a biomimetic approach to providing tactile
feedback to a prosthetic hand. They employ a spiking representation of tactile information as input to a local control
algorithm capable of performing compliant grasp or slip
prevention during object grasping. In “Single-Grasp Object
Classification and Feature Extraction with Simple Robot
Hands and Tactile Sensors”, Adam J. Spiers, MinasV. Liarokapis, Berk Calli, and Aaron M. Dollar address theproblem of tactile sensing for a robotic hand using dataacquired from tactile sensors during simple, un-plannedgrasps. Their system is able to process this data usingmachine learning algorithms or object property estimation
algorithms in order to accurately classify the grasped object.
In human hands, as in robotic hands, what is felt when anobject is touched can vary tremendously from trial to trial,and even given knowledge of the object parameters and pose,
it is often difficult to relate the mechanical signals elicited by
active touch sensing to the properties of the touched surface.
In the last article “On Frictional Forces between the Finger
and a Textured Surface during Active Touch,” Marco Janko,
Richard Primerano, and Yon Visell measured and characterized the frictional forces felt by a finger as it slides across a textured surface. They observed large trial to trial variations thateluded any simple explanation in terms of interaction parameters, and that made the task of designing a model that could
predict these force signals prohibitively difficult.
This collection of papers on humans, animals, and robots
reflects several key directions in current research on active
touch sensing. Together, they highlight the diverse possibilities for the embodiments of active touch sensing systems, theenvironments in which they operate, the goals they subserve,
and the multidisciplinary challenges posed by this field ofresearch. We expect knowledge in this area to continue togrow, in part due to the fertile interactions between activities
in biology, neuroscience, robotics, and haptics.
Yon Visell
Nathan Lepora
Mitra J. Z. Hartmann
Vincent Hayward
Guest Editors

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IEEE TRANSACTIONS ON HAPTICS,

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Yon Visell received the MA and BA degrees inphysics from the University of Texas at Austin
and Wesleyan University, and the PhD degree inelectrical and computer engineering from McGill
University in 2011. Since 2015, he has been anassistant professor of media arts and technology
and electrical and computer engineering at theUniversity of California, Santa Barbara, where hedirects the RE Touch Lab. He was an assistantprofessor from 2012 to 2015 in the ECE Department at Drexel University, and a post-doctoral
fellow from 2011 to 2012 at the Institute of Intelligent Systems and Pierre et Marie Curie. He has been employed in
Robotics, Universite
industrial R&D for sonar, speech recognition, and music DSP at several
high-technology companies. His current interests include whole-handhaptic function and engineering, and soft robotics.

157

Nathan Lepora received the BA degree in mathematics and the PhD degree in theoretical physics from the University of Cambridge, UnitedKingdom. He is a lecturer and program director(MSc in robotics) with the Department of Engineering Mathematics, University of Bristol (UOB),
United Kingdom, and is affiliated with the Bristol
Robotics Laboratory, UOB and UWE, United
Kingdom. His research interests span robot andanimal perception, artificial and natural decision
making, and biomimetics.
Mitra J. Z. Hartmann received the BS degree inapplied and engineering physics from Cornell
University in Ithaca, NY, and the PhD degree in
integrative neuroscience from the California Institute of Technology in Pasadena, CA. She was a
postdoctoral scholar at the Jet Propulsion Laboratory, Pasadena, CA, where she was in the BioInspired Technology and Systems Group. She iscurrently an associate professor in the Departments of Biomedical Engineering and MechanicalEngineering, Northwestern University, and amember of the Northwestern University’s, Institute for Neuroscience(NUIN). Her research interests involve understanding the mechanics
and neuroscience of active sensing behaviors with a particular focus on
the rat vibrissal system.
Vincent Hayward received the Dr-Ing degree in1981 from the University de Paris XI. He was apostdoctoral fellow then a visiting assistant professor in 1982 at Purdue University, and joined de Recherches in
CNRS, France, as Charge
1983. In 1989, he joined the Department of Electrical and Computer Engineering at McGill University as an assistant, associate, and then a fullprofessor in 2006. He was the director of theMcGill Center for Intelligent Machines from 2001
to 2004. From 2008 to 2011, he held the “Chaire Pierre et Marie Curie whereinternationale d’haptique” at the Universite
he is now a professor. He is interested in haptic device design and applications, perception, and robotics. He cofounded spin-off companies andreceived several best paper and research awards. He was on the editorial boards of the IEEE Transactions on Robotics, the ACM Transactions
on Applied Perception, and the IEEE Transactions on Haptics. He is afellow of the IEEE.
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