The document describes the wireless integration of tactile sensing on the hands of a humanoid robot named NAO. FlexiForce sensors were attached to the fingers without modifying the robot's existing hardware. The sensors allow the robot to differentiate objects based on properties like weight, stiffness, and texture. A printed circuit board was designed to amplify sensor signals. The robot was programmed to pick up objects, measure tactile properties, and identify objects by comparing measurements to a database. This enhances the robot's perception and ability to learn about its environment through touch.

1. 
Abstract — The new-generation humanoid robots, such as
the NAO robot made by Aldebaran Robotics, are characterized
by autonomous learning and close interaction with the
environment including humans. While equipped with advanced
vision and audio features including object and face recognition,
speech recognition, sound source recognition, speech synthesis,
etc., the NAO robot’s tactile sensing is limited to several
buttons that can be used to trigger associated actions. This
paper presents the wireless integration of tactile sensing on the
hand of a NAO robot. Without any replacement or
modification of its existing hardware, the add-on allows the
NAO robot to differentiate objects with similar size, color and
shape but different weight, stiffness, or texture.
I. INTRODUCTION
The “sense of touch” is particularly important among
various modalities that are needed to perceive and react to the
dynamics of the real world. It allows the assessment of object
properties such as size, shape, weight, stiffness, and texture.
The new-generation humanoid robots are characterized by
autonomous learning and close interaction with their
environments including humans. Tactile sensing, combined
with vision and/or audio in many cases, enhances
multisensory perception of humanoid robots. Considerable
research has been conducted on robot tactile sensing [1]-[7],
especially on humanoid robots [5]-[7]. The advancements in
intelligent humanoid robots provide new opportunities and
challenges for tactile sensing. For example, the smaller finger
area comparing to robot manipulators, the coordination
between vision, touch, and other senses, requirements on the
touch sensory system to avoid interference to mobility, etc.
The humanoid robot NAO (shown in Fig. 1), made by
Aldebaran Robotics, has been used by more than 350
universities and research labs around the world for a wide
variety of research topics in robotics as well as computer
science, human-machine interaction, and social sciences.
While equipped with advanced vision and audio features
including object and face recognition, speech recognition,
sound source recognition, speech synthesis, etc., the NAO
L. G. Ni is an associate professor at the Electrical and Computer
Engineering Department, College of Engineering, California Baptist
University, Riverside, CA 92504 USA (corresponding author, phone: 951-
343-4470; fax: 951-343-4782; e-mail: gni@calbaptist.edu ).
D. P. Kari recently received his BS ECE degree from California Baptist
University. He is currently a Master EE degree candidate at the Bourns
College of Engineering, University of California - Riverside, CA 92521
USA. (e-mail: David.Kari@calbaptist.edu ).
A. Muganza, B. Dushime, A. Zebaze are recent BS ECE, BS ECE and
BS ME graduates from California Baptist University. (e-mail:
Alex.Muganza@calbaptist.edu, Bertrand.Dushime@calbaptist.edu,
Andre.Zebaze@calbaptist.edu ).
robot’s tactile sensing is limited to several buttons that can be
used to trigger associated actions. The main objective of our
project is to enhance the NAO robot’s perception and
intelligence by giving it the capability of identifying different
objects by their weight, stiffness and texture.
Figure 1. The humanoid robot NAO.
Several features of the NAO robot make it appealing to
our research. With its intuitive graphical interface, the
programming software developed by Aldebaran Robotics for
the NAO robot called Choregraphe allows easy programming
of behaviors required for active exploration of objects using
touch sense. For instance, the robot needs to grasp the object
with one hand and stroke the object surface with the other
hand in order to identify the surface texture. The built-in
speech recognition and speech synthesis capabilities of the
NAO robot makes the development more interactive, as the
robot asks questions about the object it is touching or tells the
name of the object based on tactile properties. The built-in
visual object recognition capability provides opportunity for
fusion of vision and touch in future research.
This paper is organized as follows: The system
configuration including hardware setup and software
components are presented in Section II. The calibration and
testing of tactile sensors are discussed in Section III. The
experimental results of active exploration and identification
of objects are presented in Section IV. Conclusions and
directions for future work are summarized in Section V.
II. SYSTEM CONFIGURATION
From the system integration point of view, the
framework of our wireless integration of tactile sensing on
the NAO robot can be applied to other commercially
Wireless Integration of Tactile Sensing on the Hand of a Humanoid
Robot NAO
Liya Grace Ni, Senior Member, IEEE, David P. Kari, Member, IEEE, Alex Muganza, Member, IEEE
Bertrand Dushime, Member, IEEE, and Andre N. Zebaze
2012 IEEE RO-MAN: The 21st IEEE International Symposium on
Robot and Human Interactive Communication.
September 9-13, 2012. Paris, France.
978-1-4673-4606-1/12/$31.00 ©2012 IEEE 982

2. available humanoid robots or adding other external sensors
to the NAO robot.
A. Hardware Setup
In order to avoid violation of warranty, we have the
constraint of not replacing or modifying any existing
hardware on the NAO robot. The hardware components we
used in the system, besides the NAO robot itself, are listed
in Table I, along with simple descriptions of their
functionalities, their locations, and mounting methods. Fig. 2
shows how the hardware components are physically
mounted on the NAO robot.
TABLE I. HARDWARE COMPONENTS
Component Name Functionality Location
Mounting
Method
FlexiForce sensors Tactile sensing Fingers
Double sticky
tape
Pinted Circuit
Board (PCB)
Auxiliary circuit
for sensors
Upper
arm
Velcro strap
& tape
RF Link transmitter
Transmits sensor
data
Back Enclosure box
Arduino Mega 2560
w. battery
Interface
between PCB
and RF
transmitter
Back Enclosure box
RF Link receiver
Receives sensor
data
Not on
the robot
N/A
Arduino Uno
Interface
between RF
receiver and
computer
Not on
the robot
N/A
Computer
Processes sensor
data; delivers
behavioral
commands to the
NAO robot
Not on
the robot
N/A
Figure 2. Hardward mounted on the NAO robot.
The configuration of the five FlexiForce Sensors on the
NAO robot’s three-fingered right hand is shown in Fig. 3.
On both the left and right fingers, one sensor is mounted at
the tip and one is at the center. The fifth one is at the tip of
the thumb. The same sensor labeling as shown in Fig. 3 will
be used later in Section IV.
Figure 3. Configuration of the five sensors on NAO’s hand.
The data flow in the system is illustrated in Fig. 4. The
tactile sensor measurements are sent to the computer
wirelessly through the RF module and microcontrollers. The
computer analyzes and logs the sensor measurements, and
sends speech and behavior commands to the CPU on the
NAO robot itself wirelessly. The connection between the
NAO robot and the tactile sensors is only a physical
attachment without data flow in between.
Figure 4. Data flow chart.
B. Software Components
The software we developed for this project contains
several components, as listed in Table II.
Choregraphe allows easy capture of the joint angles for
the starting and ending positions of each motion we
implemented on the NAO robot later for the integration of
touch sensing. Fig. 5 shows how the arm angles were
captured in Choregraphe.
A cross-platform Arduino IDE is used to program the
microcontrollers that interface with the RF transmitter and
receiver, which communicate with 434 MHz radio frequency
signals. With the restriction of a maximum 4800 bits per
second (BPS) data rate of the RF module, currently the data
packets containing measurements of all five sensors are
transmitted at the frequency of 25 Hz.
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3. TABLE II. SOFTWARE COMPONENTS
Component
Description
Development
Language
Development
Stage
Location of
Execution
Motion
recording
Choregraphe Preparation Computer
Wireless
communication
C Integration Microcontrollers
Main
application
C# Integration Computer
Speech and
behavioral
modules
Python Integration The NAO robot
Display of
measurements
C# Testing Computer
Figure 5. Arm angles obtained in Choregraphe.
The main application involves a learning process for the
NAO robot based on tactile information including weight,
stiffness and roughness. Just like how a toddler learns about
objects in his/her surroundings, the NAO robot will go
through the following steps during its learning process:
Step 1. Pick up an object and learn how heavy/light, how
hard/soft, and how rough/smooth it is, with measurements
from tactile sensors and associated actions.
Step 2. Characteristics extracted from measurements are
compared with the corresponding features of objects in the
database. Decisions are made as follows.
- If the actual features of weight, stiffness and roughness
are close to the features of the current object in the database,
in other words, the absolute values of the differences are
below predefined thresholds, say the name of the current
object.
- If the actual features do not match the features of the
current object, and the current object is not the last one in the
database, move on to the next object.
- If the actual features do not match the features of the
current object, and the current object is the last one in the
database, go to Step 3.
Step 3. Ask the name of the object and add it to the
database.
The software flow of the main application is shown in
Fig. 6. Although the main application was developed in C#,
in order to use the NAO SDK to send speech and behavioral
commands to the NAO robot, a Python script was written
for each action and was invoked in the C# program.
Figure 6. The Unified Modeling Language (UML) diagram of software
flow in the C# application.
A graphical user interface programmed in C# is
embedded in the main application to display the weight,
stiffness and roughness data during testing and
demonstrations.
III. SENSOR CALIBRATION AND TESTING
A. Design of Printed Circuit Board (PCB)
The FlexiForce sensor is an ultra-thin and flexible printed
circuit that uses a resistive-based technology. The application
of a force to the active sensing area of the sensor results in a
change in the resistance of the sensing element in inverse
proportion to the force applied. A modified version of the
recommended amplifier circuit in the user manual [8] is
shown in Fig. 7.
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4. Figure 7. Amplifier circuit for FlexiForce sensors [8].
The feedback resistance RF as well as the drive voltage
VT can be used to adjust the sensitivity of the sensor. A
feedback resistance value of 100 kΩ and a drive voltage of
-1.5 V were selected in our design. A two-step process was
implemented to supply the -1.5 V to the sensors. First, a
voltage regulator consisting of two IN914 diodes connected
in series and a 240 Ω resistor provides +1.5 V with a 5 V
supply from the microcontroller. Next, an ADM660 Switched
Capacitor Voltage Converter was used to convert it to -1.5 V.
Considering the constraint of the size of PCB in order to
mount it on the robot’s upper arm, we chose a quad Op-Amp
chip MCP6004 and a dual Op-Amp chip MCP6002 to
provide all the Op-Amps needed in the circuit. The layout of
the custom PCB is shown in Fig. 8.
Figure 8. PCB layout.
B. Sensor Calibration
Two different methods were used to calibrate the sensor.
First, a flat load was applied to the sensor with its weight
changed by adding additional mass on top of it. Second, a
plastic ball, or spherical load, was used as test object. The
weight was also changed by adding additional mass on top of
the ball. The sensor was calibrated over a range of 0 to
approximately 2 N. The results are shown in Fig. 9. There is
a linear relationship for a flat load between the voltage output
from the sensor and the force applied to the sensor. The
linear equation fits the data with an R2
value (a statistical
metric that indicates how close the curve fits the data points)
of 0.9875. The values obtained for the spherical loads are
higher than the values obtained for the flat loads, likely
because of the smaller contact area for the spherical loads.
The results indicate that at higher values of weight, the
spherical load more closely approximates a flat load because
of more even distribution of the load due to compression of
the spherical object.
Figure 9. Experimental results of FlexiForce sensor measurements (square:
flat loads, circle: spherical loads).
Next, the FlexiForce sensors were attached on the fingers
of the NAO robot and calibration was performed with the
following configuration: the robot’s right arm and hand
were kept still and a plastic ball was placed in its hand with
a fixed position. Measurements were taken from two sensors
mounted at the centers of both left and right fingers. Then
the sensors were replaced by two other sensors, and so on.
The weight of the plastic ball was adjusted by adding water
through a hole on its top. The voltage for a particular mass
was obtained by averaging the voltages measured by the
sensors. Fig. 10 shows the experimental results of the ranges
and average voltages of the five sensors versus different
weight inputs.
Figure 10. Calibration results with sensors on the NAO robot
(bar: range of voltages, square: average of voltages).
IV. EXPERIMENTAL RESULTS
In this preliminary study, three objects, namely a golf
ball, a ping pong ball and a cotton ball, which are of similar
size, shape and color but different weight, stiffness and
roughness, were chosen for our experiments.
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5. A. Comparison of Weight
The NAO robot was programmed to reach out with its
right forearm and open its right hand. At the same time, it
asked “Give me the ball.” The golf ball was then placed in its
right hand. The measurements from the two sensors mounted
at the center of the fingers are shown in Fig. 11. The sensors
at the finger tips and on the thumb were not pressed due to
the size and shape of objects in our experiments, therefore
their readings were discarded. A period of five seconds was
allotted to complete the test, and the voltage samples from
each of the above two sensors throughout the testing period
were averaged and logged in the database. A progress bar on
the display shows how much of the five-second period has
elapsed. As can be observed from Fig. 11, the center of
gravity is closer to one of the two fingers during that
particular test. Therefore, the average of sensor #3 and sensor
#4 voltages is recoded as the indicator of object weight.
Figure 11. Display of weight test result for a golf ball.
This process was repeated for a ping pong ball and a
cotton ball. The average voltages over a period of five
seconds for sensor #3 and #4 are shown in Table III for all
three objects. It can be easily observed from the sensor data
that the weight of the golf ball is much higher comparing to
either a ping pong ball or a cotton ball.
TABLE III. WEIGHT MEASUREMENTS
B. Comparison of Stiffness
Stiffness is an important property of an object that can be
obtained using the sense of touch. Stiffness is defined as the
extent to which an object resists deformation in response to
an applied force. Ideally, stiffness of the object being
identified by the NAO robot should be calculated as:
Fx,
where F is the force applied on the object and x is the amount
of deformation of the object surface at the contact point.
Unfortunately the positions of the fingertips of the NAO
robot cannot be obtained programmatically, which means the
measurement of object deformation using the penetration of
the fingertip in the object is very difficult to implement if not
impossible. Therefore, the sensor measurements were utilized
to characterize stiffness.
The NAO robot’s right hand was open for the weight test.
When the stiffness test started immediately after the weight
data were logged, the robot was commanded to secure the
ball using its left hand from above and then close its right
hand slowly. The assistance by the left hand was necessary,
especially for the ping pong ball which could have easily
slipped out of the NAO robot’s hand. Display of sensor
voltage readings and corresponding forces for a ping pong
ball is shown in Fig. 12.
Figure 12. Display of stiffness test result for a ping pong ball.
As can be observed from Fig. 12, the sensor on the thumb
(labelled as sensor #5) and the one at the center of the left
finger (labelled as sensor #4) showed high voltages but the
others showed zero voltages. This can be explained by how
the ping pong ball was grasped by the NAO robot’s right
hand. The ping pong ball was mainly between the thumb
and the left finger while the right finger was almost simply
resting on the surface of the ball. Due to the relative size of
the ball versus the hand, the finger tips were slightly above
the ball.
Object
Sensor #3
Average Voltage (V)
Sensor #4
Average Voltage (V)
Golf Ball 0.08 0.24
Ping Pong Ball 0.00 0.00
Cotton Ball 0.08 0.07
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6. Sensor voltages from stiffness test are shown in Table IV
for all three objects. Although both the ping pong ball and the
cotton ball are very light as mentioned in Section IV-A, the
sensor voltages in Table IV showed their difference in
stiffness clearly, which was used for object identification
later. The sensor voltages for the golf ball were even higher
than those for the ping pong ball, which met our expectation.
TABLE IV. STIFFNESS MEASUREMENTS
In our experiment with only three objects: a golf ball, a
ping pong ball, and a cotton ball, the average of all sensor
measurements is sufficient to serve as an indicator of
stiffness. However, for objects with less difference in
stiffness, the sensor measurements should be analyzed more
selectively.
C. Comparison of Roughness
Due to the fact that currently only the right hand of the
NAO robot is equipped with the FlexiForce sensors, we
programmed the robot to put the ball in his left hand
immediately after stiffness data were logged in the database.
When the left hand grasped the ball firmly, the right hand
started stroking the surface of the ball with the tip of one
finger.
The interpretation of tactile sensor measurements for the
roughness of object surface is more challenging than weight
and stiffness. The research in surface texture discrimination
by robots has been advanced by both the development of
tactile sensing arrays and algorithms for temporal or
spatiotemporal analysis of the sensor data [9][10]. The Fast
Fourier Transform (FFT) was performed on the voltage data
collected from the sensor mounted on the tip of the finger
that stroked the object surface. By comparing the spectrum
of the golf ball data and that of the ping pong ball data
shown in Fig. 13, we noticed significant difference at the
high end of the frequency range. The sampling rate is
limited to 25 Hz by the data rate of the RF module. The
magnitude at the frequencies close to 12.5 Hz (half of the
sampling frequency) for the golf ball, which has a rough
surface, is obviously higher than that of the ping pong ball,
which has a smooth surface. Therefore, the magnitude of the
FFT at the highest frequency 12.5 Hz was logged in the
database for each object as the indicator of roughness.
(a)
(b)
Figure 13. Roughness test results: (a) golf ball; (b) ping pong ball.
D. Object Identification
The NAO robot asked for the name of the object after the
weight, stiffness and roughness data were all logged in the
data base. The learning process was repeated for all three
objects. The main application continued with object
identification following the learning process. The NAO
robot was programmed to ask the user to give it a ball. After
a ball was randomly selected and placed in its right hand, it
was able to identify whether it was a golf ball, a ping pong
ball, or a cotton ball.
V. CONCLUSIONS AND FUTURE RESEARCH
A tactile sensing system with five FlexiForce sensors and
wireless communication was successfully integrated to the
NAO humanoid robot. Active exploration behaviors were
programmed on the NAO robot and software interpretation
of the sensor voltages were implemented for weight,
stiffness, and roughness respectively. The NAO robot was
able to learn these properties of a golf ball, a ping pong ball
and a cotton ball, and identify them based on their
differences.
Future research includes investigation of more advanced
tactile sensing technology such as MEMS tactile sensor
arrays; improvement of hardware integration, for example,
using wearable LilyPad microcontrollers; integration of
tactile sensing with the NAO robot’s existing visual object
recognition capability; and last but not least, evaluation of
the accuracy of tactile-sensing based object identification
with testing on a large variety of objects with different
weight, stiffness and roughness.
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Object
Sensor Voltages (V)
#1 #2 #3 #4 #5
Golf Ball 0.03 0.61 0.01 2.05 4.99
Ping Pong Ball 0.0 0.0 0.0 2.12 3.89
Cotton Ball 0.02 0.02 0.02 0.02 0.02
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