Whisker-Inspired Tactile Sensing for Contact Localization on Robot Manipulators Michael A. Lin1 Emilio Reyes1 Jeannette Bohg2and Mark R. Cutkosky1

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Whisker-Inspired Tactile Sensing for Contact Localization on Robot

Manipulators

Michael A. Lin1, Emilio Reyes1, Jeannette Bohg2and Mark R. Cutkosky1

Abstract— Perceiving the environment through touch is im-

portant for robots to reach in cluttered environments, but

devising a way to sense without disturbing objects is chal-

lenging. This work presents the design and modelling of

whisker-inspired sensors that attach to the surface of a robot

manipulator to sense its surrounding through light contacts. We

obtain a sensor model using a calibration process that applies

to straight and curved whiskers. We then propose a sensing

algorithm using Bayesian ﬁltering to localize contact points. The

algorithm combines the accurate proprioceptive sensing of the

robot and sensor readings from the deﬂections of the whiskers.

Our results show that our algorithm is able to track contact

points with sub-millimeter accuracy, outperforming a baseline

method. Finally, we demonstrate our sensor and perception

method in a real-world system where a robot moves in between

free-standing objects and uses the whisker sensors to track

contacts tracing object contours.

I. INTRODUCTION

Manipulation in unstructured and cluttered environments

is characterized by limited visibility and constrained motion.

To improve robot perception, it is advantageous to sense the

environment through multiple modalities including touch [1].

Reaching into a cupboard or a refrigerator full of objects to

retrieve a particular object are examples of tasks where con-

tacts with objects may happen frequently and unexpectedly –

not only at the end-effector but along the entire arm. Sensing

the locations and forces of contacts has been recognized as

helpful for perception [2]–[5] enabling access to locations

that may be occluded from visual sensors. However, sensing

through contact when interacting with free-standing objects

that are small and light (e.g. a nearly empty bottle of

pills or spices) is challenging because the action of making

contact will likely change the object’s state. Prior work has

shown that minimizing the robot’s inertial properties at the

end-effector allows non-disturbing contacts and facilitates

environment sensing [3, 6, 7]. However, a different approach

is needed to detect and minimize disturbances arising from

contacts along the robot arm.

1M. Lin, E. Reyes and M. Cutkosky are with the Department of

Mechanical Engineering, Stanford University, Stanford, CA 94305, USA

[mlinyang,ereyes35,cutkosky]@stanford.edu

2J. Bohg is with the Department of Computer Science, Stanford Univer-

sity, Stanford, CA 94305, USA bohg@stanford.edu

Toyota Research Institute (TRI) provided funds to assist the authors with

their research but this article solely reﬂects the opinions and conclusions of

its authors and not TRI or any other Toyota entity.

must be obtained for all other uses, in any current or future media, including

reprinting/republishing this material for advertising or promotional purposes, creating

new collective works, for resale or redistribution to servers or lists, or reuse of any

copyrighted component of this work in other works.

Fig. 1: A) Demonstration of contact localization with two

curved whisker sensor arrays on a robot arm reaching among

objects of unknown shape and location. B) Plots of left

and right sensor estimated contact locations. A video of this

experiment is attached.

In nature, we encounter various solutions for non-intrusive

contact sensing. A common method is to use whiskers

or vibrissae to navigate environments or localize prey in

conditions where vision does not sufﬁce [8, 9]. In some cases

(e.g. rats) animals employ sophisticated active whisking but

in others, such as the vibrissae on the lower limbs of cats,

whiskers are mostly passive and provide information while

negotiating complex environments. The latter serves as the

primary inspiration for the approach presented here.

Although whiskers are not widely used in robotics, there

are a number of notable examples including [10]–[13]. Most

of these involve active whisking with a rotational joint and

actuator at the whisker base. Passive whiskers (or antennae)

on mobile bases are less common although there are a few

examples (e.g., [14, 15]). In most of these cases, the whiskers

arXiv:2210.12387v1 [cs.RO] 22 Oct 2022

are relatively stiff and are often straight, which means that

if they approach a very light object head-on, they are likely

to disturb it.

In our approach, we use very soft whiskers that bend easily

and may be pre-curved to minimize disturbances and avoid

the highly nonlinear phenomenon of buckling for slender

elastic columns. The whiskers are mounted along a robot

arm and we use the robot’s accurate proprioception in com-

bination with sensor measurements to localize contacts along

the whisker and gather information about the environment.

A number of notable challenges arise in passive whisker

deployment on a robot arm. The ﬁrst is that whisker motion

and deﬂection are subject to arm motion, which is typically

intended to control a distal end-effector and not to provide

exploratory sensing. Interaction with objects will often be

limited to one sustained contact as opposed to multiple

probing actions. The state estimation method should be able

to process this arbitrary motion and whisker deﬂection to

infer contact locations quickly. A second challenge is that as

a whisker sensor is moved in arbitrary directions a straight

whisker may catch its tip on object surfaces and buckle,

making the sensor signal difﬁcult to interpret. A sensor

design with a curved whisker, as shown in Fig. 4A, can

avoid this buckling effect but contact localization using these

whisker geometry may be more challenging and have been

less explored.

Contributions: We present a new sensor design and

fabrication method for creating arrays of slender, curved

super-elastic nitinol vibrissae or whiskers mounted along the

arm of a robot. We present a calibration method applicable

to curved whiskers and a Bayesian ﬁltering algorithm that

can quickly track contact locations to within sub-millimeter

accuracy. Then we implement three different Bayesian ﬁlters

(Extended Kalman Filter, Unscented Kalman Filter and Par-

ticle Filter), showing that these methods can perform better

than a baseline method [13], with the UKF being the best

performing ﬁlter in tests. Finally, we mount the sensors on

a robot arm and demonstrate the ability to combine robot

proprioception and sensor measurements to accurately track

contact locations over time, allowing the arm to maneuver

safely without disturbing even small and lightweight objects.

II. RELATED WORK ON CONTACT

INTERPRETATION

Although the sensing method presented here is new, it

builds upon prior work on perception of unstructured en-

vironments through contacts [3]–[5, 7, 16]–[18]. A common

challenge when sensing free-standing objects is that the act of

contact sensing often will change the state of the object. For

objects of known shapes, Koval et al. developed a Particle

Filtering approach to estimate object location through a

sequence of pushes to collapse the belief distribution [16].

Suresh et al. expanded on this work by posing the problem as

a Simultaneous Localization And Mapping (SLAM) problem

and were able to estimate both shape and location of objects

[18]. While these methods work when interacting with iso-

lated objects, the pushing approach is more challenging when

an object is amidst clutter which constrains both the object

and robot arm.

Sensing through non-intrusive contacts, on the other hand,

has the advantage of objects remaining static, making state

estimation easier. The most common of such perception

methods is vision, however, RGB-D cameras are not well

suited for close range sensing as is typically necessary when

reaching into conﬁned spaces with objects. Some work has

investigated using close range proximity sensing [19]–[21],

but these methods do not perform well when sensing specular

or transparent surfaces for optical transducers, or may be sus-

ceptible to variations in materials properties for magnetic and

capacitive transducers. Sensing through mechanical contact

is not affected by these problems.

As noted earlier, a modest number of investigations have

addressed whisker- or antenna-based sensing in robotics.

Early work by Kaneko et al. showed a method of active

probing where a ﬂexible antenna is rotated by actuators

at one end to make contact with objects while estimating

contact location with measured rotational compliance [11].

Subsequent efforts addressed improved contact localization

for objects of varying shapes [12, 13, 22]. Using 3-DOF

force/torque sensing at the base (two bending torques and one

axial force) it is possible to deduce contact locations from

single measurements [23]–[25]. However, these methods

either require using additional actuators for whisking or,

in the latter case, require complex models to ﬁt a unique

mapping and accuracy is limited in practice.

Fig. 2: A) Sensor design components including ﬂexible

nitinol wire and compliant base. B) Cross-section view of the

base. Whisker deﬂection results in magnet rotation around

point prand a change in magnetic ﬂux measured by the

Hall effect sensor. C) Minimizing rotation and translation

coupling of the magnet yields a nearly linear sensor reading

(blue line). D) FEA of the sensor as tip is displaced laterally

by 1 cm. Whisker deﬂections primarily result in magnet

rotation.

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Whisker-InspiredTactileSensingforContactLocalizationonRobotManipulatorsMichaelA.Lin1,EmilioReyes1,JeannetteBohg2andMarkR.Cutkosky1AbstractPerceivingtheenvironmentthroughtouchisim-portantforrobotstoreachinclutteredenvironments,butdevisingawaytosensewithoutdisturbingobjectsischal-lenging.Thisworkpres...

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Whisker-Inspired Tactile Sensing for Contact Localization on Robot Manipulators Michael A. Lin1 Emilio Reyes1 Jeannette Bohg2and Mark R. Cutkosky1

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