OBJECT EXPLORATION WITH A HUMANOID ROBOT

Using Tactile and Kinesthetic Feedback

Nicolas Gorges, Stefan Gaa and Heinz W

orn

Institute for Process Control and Robotics, University of Karlsruhe, Engler-Bunte-Ring 8, Karlsruhe, Germany

Keywords:

Tactile Sensing, Object Exploration, Robot Control, and Humanoid Robot.

Abstract:

This work deals with the reactive and autonomous exploration of objects with a humanoid robot using only

tactile and kinesthetic sensor feedback. To coordinate the ﬂow of the exploration, a novel hierarchical explo-

ration system is introduced. The lowest level extracts contacts points and elementary features based on the

direct contact with the object. It furthermore provides elementary movement primitives. The intermediate

level consists of different controlling behaviors to generate exploration movements according to the sensor

feedback. This level enables the robot to explore an object pointwisely or continously. The highest level eval-

uates the process of the exploration and determinates the reactive behavior of the underlying components. The

evaluation scenario comprises the exploration of edges, which are arbitrarily located in space. The evaluation

platform consists of a robot arm, a force-torque sensor, and a tactile sensor matrix. The proposed approach is

evaluated and the different reactive behaviors as well as the used sensor modalities are compared.

1 INTRODUCTION

Service robots build a new block of research area.

Their range of application highly differ from conven-

tional industrial robot, as they are rather deployed in

a domestic environment and are usually not intended

for repetitive tasks with need for high precision or

high forces. Typical tasks for a humanoid robot are

all kinds of fetch and carry or manipulation tasks with

a broad variety of objects. Not all of these object

can be assumed to be known appriori. The robot

might encounter unknown or partially unknown ob-

jects. Therefore, the robot has to use its sensors, like

visual or haptic sensors, to explore an unknown ob-

ject. The visual exploration is adequate to determine

the location and the rough shape of an object but it

is limited due to the ambiguity of visual data and the

need for textured objects as well as good light condi-

tions.

The haptic exploration describes the active palpa-

tion of objects. The relevant parts of the haptic explo-

ration are the tactile perception (surface sensibility)

and the kinesthetic perception (depth sensibility). A

haptic exploration procedure of an object requires the

direct interaction of the robot with the object. On the

one hand, this delivers accurate 3D information about

the object but, on the other hand, requires a reactive

control strategy to bring the sensors into the right po-

sition.

Previous work in (Klatzky et al., 1987) (Leder-

man and Klatzky, 1987) identiﬁed the following ex-

ploration procedures from observing human explo-

ration behavior: lateral motion, pressure, enclosure,

and contour following.

These procedures allows to determine the texture,

hardness, shape and size of an object by haptic ex-

ploration. The exploration procedure of this work pri-

marily focuses on the procedure of contour following.

Here, the tactile perception is represented by a tac-

tile sensor matrix whereas the kinesthetic perception

is given by a force-torque sensor. Robotic exploration

procedures so far include approaches with grippers

(Schmidt et al., 2006) and with tactile sensor matri-

ces (Chen et al., 1995), (Heidemann and Schoepfer,

2004).

A major problem for such exploration tasks is the

detection of a contact with the object to be explored.

The limitation is usually given by the sensibility and

the spatial resolution of the sensor. Therefore, many

approaches using tactile sensors assume that the sen-

sor is already located directly at the region of interest.

In order to avoid this restriction, this work intro-

duces a novel approach by coupling the information

of a force-torque sensor in the wrist and a tactile sen-

274

Gorges N., Gaa S. and Wörn H. (2008).

OBJECT EXPLORATION WITH A HUMANOID ROBOT - Using Tactile and Kinesthetic Feedback.

In Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - RA, pages 274-281

DOI: 10.5220/0001502702740281

 SciTePress

Figure 1: A system overview.

sor matrix representing the pose of an opened hand.

This enables the robot on the one hand to detect di-

rect contacts using the tactile sensor matrix, which

are very accurate but less sensitive, and on the other

hand to detect indirect contacts using the force-torque

sensor and a model of the hand, which make it less

accurate but very sensitive.

The remainder of this paper is structured as fol-

lows: After this introduction, the system overview of

this approach is discussed in section 2 followed by the

perception level in section 3. Our results are presented

in section 4 before conclusions are given in section 5.

2 SYSTEM OVERVIEW

In order to consecutively determine the shape of an

object, a concept consisiting of three components is

introduced, as shown in ﬁgure 1. Here, the highest

level is represented by the exploration planer. A re-

active control layer complies with the skill library,

from which consecutively a skill is chosen and ex-

ecuted. The haptic layer builds the lowest block of

the framework and is realized by three components:

tactile perception, arm planning and kinesthetic per-

ception. The planning layer and the reactive control

layer are explained in the following. The haptic layer

is presented separately in section 3.

2.1 Exploration Planner

The exploration planner makes decisions based on the

geometric shape of an object which can be determined

by a successive palpation sequence. Based on the es-

timate of the object’s shape, it determines the align-

ment of the robot towards the object. The planner ex-

ecutes a sequence of skills whereas the skills provides

the planner with contact features during their execu-

tion. The planner evaluates the alignment towards the

object and chooses a different skill if neccessary.

2.2 Exploration Skills

In this work, the exploration skills represent strategies

to explore structures of an object. They are elemen-

tary operations for the exploration procedure and are

executed by the superior global exploration compo-

nent. A sequence of simple exploration skills leads to

a complex exploration behavior.

Their key tasks are the coordination of the arm

control and the creation of a deﬁned exploration be-

haviors according to the preprocessed data. A skill

has to fulﬁll two objectives. First of all, it has to bring

the robot arm into the right position which is realized

by a sequence of states. Secondly, it has to provide

the planning level with an amount of contact points.

To fulﬁll these tasks, a skill can access the underly-

ing haptic components. The planning level supervises

the execution of a skill and interprets the features pro-

vided by a skill in a global context.

A single skill is represented by a state machine

which determines the ﬂow of action. Such a skill is

able e.g. to follow a structure or to rotate around a

structure according to its task deﬁnition. The most

states imply a coordination of sensor data and move-

ments but also include instructions from the upper

level. In general, the skills can be separated into two

groups:

• Skills with discrete movements

• Skills with continuous movements

These two types of skills are explained in more detail

in the following.

2.2.1 Skills with Discrete Movements

Skills with discrete movements are characterized by a

palpation sequence as the skill departs from the object

after establishing a contact before moving the arm to

next position of interest. This is achieved by move-

ment and control primitives which are the building

blocks of the total movement. Movement primitives

enable the robot to move with respect to a local or a

global coordinate system. Control primitives also em-

bed sensor data during a movement, e.g. to buildup a

certain amount of pressure. Here, the communication

between a skill and the planer is destinctive. The ben-

eﬁt is that structures can be explored systematically

whereas the drawback is that they can only perform

an alignment according to point contacts. They need

OBJECT EXPLORATION WITH A HUMANOID ROBOT - Using Tactile and Kinesthetic Feedback

275

robot

arm

Force

torque

sensor

φτ

act

−

φτ

des

−

tactile

sensor

act

des

err

position

control

φτ

Err

<φτ

Tol

tactile

perception

kinesthatic

perception

tactile

control

des

kinesthatic

control

des

1/2

Figure 2: A control loop for skills with continous movements

at least two contact points to perform an alignment

and also depend on the measuring accuracy of these

contact points.

The process of tracking an edge, for example, can

be summarized as a sequence of the control and move-

ment primitives ¨move up¨, ¨move to the side¨, ¨move

down”, and ¨detect contact¨. A sequence of this skill

results into a simple but effective exploration proce-

dure.

2.2.2 Skills with Continuous Movements

The exploration behavior of skills with continuous

movements is given by a complex control loop, e.g.

zero-force control or a tactile control. At ﬁrst, these

kind of skills establish a contact with the object sur-

face. Then, using a superior control loop, they try to

keep a steady contact towards the surface. Figure 2

shows the basic structure of such a superior control

loop. It shows a cascaded system with the force con-

trol preceding the tactile control. It is characterized

by a coarse control based on the measured forces and

torques of the force-torque-sensor and by a ﬁne con-

trol based on the measured pressure proﬁle.

The idea is to use the force-torque sensor to keep

the applied pressure φτ

des

stable. On the one hand,

the applied pressure should not exceed a given limit

as it could damage the robot or the object. On the

other hand, the pressure should not be too little as the

tactile sensor matrix would not be able to measure an

adequate pressure proﬁle anymore and the sensor pad

could even loose contact to the object surface. The

tactile control loop is activated as soon as the applied

pressure is within an acceptable interval, expressed by

φτ

err

< φτ

tol

. The tactile control loop is deﬁned by the

deviation θ

err

of the center of the tactile image from

center of the sensor pad. The position corrections τ

des

are given to the position control of the robot. This

Figure 3: Setup of the evaluation platform and the involved

coordinate systems.

enables the robot arm to align to the object and follow

its surface.

3 PERCEPTION

The perception level is part of the lowest level. It pre-

processes the data provided by the tactile sensor and

the force-torque sensor. The perception in this work

is therefore split into the tactile and kinesthetic per-

ception. In the course of the paper, the tactile sen-

sor matrix refers to the tactile perception whereas the

force-torque sensor represents the kinesthetic percep-

tion. Figure 3 show the setup of the evaluation plat-

form. It shows the tactile sensor and the force-torque

sensor mounted to the robot arm. Furthermore, it il-

lustrates the different coordinate systems which are

involved in the perception layer.

The tactile sensor identiﬁes very accurately the

position of contacts caused by a direct touch. But

ICINCO 2008 - International Conference on Informatics in Control, Automation and Robotics

276

a small change in the orientation of the tactile sen-

sor matrix causes a signiﬁcant chance in the resulting

tactile image. Imagine an imprint of an edge on the

tactile sensor, a small twist along the perpendicular

image axis of the edge, makes the edge disappear un-

til only a single point contact is determined. After a

certain angle between the tactile sensor and the sur-

face, no contacts can be detected as the sensor only

turns normal forces into a pressure proﬁle. This angle

is called the critical angle.

The force-moment sensor, on the other hand, is

not able to determine contacts in the inner regions of

the sensor pad’s surface. It can only detect the direc-

tion from the center point of the sensor to the border

of the surface.

There is a simple decision rule for the determina-

tion of a contact, combining the tactile and the force-

moment-sensor:

1. If the tactile sensor detects a contact, the contact

must be inside the surface of the sensor pad. This

is called a TS-contact and is the desired type of

contact.

2. If the tactile sensor does not detect a contact but

the kinesthetic perception does, the contact must

be at the border of the sensor pad. We call this a

FTS-contact.

It is obvious that the tactile sensor can only take

a pressure proﬁle if the robot apply enough force to-

wards the direction of the object surface. Therefore,

the more sensitive force-torque sensor is used to reg-

ulate the pressure applied to the object.

3.1 Tactile Perception

3.1.1 Working Principle

As already investigated and published by Weiss et

al. (Weiss and Woern, 2005), the working principle

of the tactile sensors depends on an interface effect

between the metal electrodes and the structured con-

ductive polymer covering the sensing electrodes. The

resistance between the common electrode and a sen-

sor cell electrode is a function of the applied load and

time. This technique leads to very accurate pictures of

the applied pressure proﬁle and minimizes crosstalk

between the sensor cells as well.

As each sensor cell represents a measured voltage,

the voltage image has to be transferred to a pressure

image. This characteristic curve of the tactile sensor

can be obtained by calibration.

3.1.2 Tactile Feature Extraction

Identifying the characteristic features of an image us-

ing moments is a well known paradigm in image pro-

cessing. The data of the tactile sensor matrix corre-

sponds to a two-dimensional planar image. We an-

alyze this image using moments up to the 2

order

(Hu, 1962). The two-dimensional (p +q)

order mo-

ment m

p,q

of an image is deﬁned as the following dou-

ble sum over all image pixels (x,y) and their values

f (x,y):

p,q

∑

f (x,y) p,q ≥ 0 . (1)

The moment m

0,0

constitutes the resulting force ex-

erted on the sensor. The center of gravity x

)

of this force can be computed to

1,0

0,0

(2)

0,1

0,0

. (3)

Using the center of gravity, we can verify that the ob-

ject surface is aligned to the center of the sensor pad.

It also allows to calculate the higher order moments

with respect to the center of gravity, the so-called cen-

tral moments µ

p,q

∑

(x − x

)

(y − y

)

f (x,y) p,q ≥ 0 . (4)

The 2

order central moments

2,0

∑

(x − x

)

f (x,y) (5)

0,2

∑

(y − y

)

f (x,y) (6)

1,1

∑

(x − x

)(y − y

) f (x,y) (7)

approximate the image by an ellipse and represent its

principal axes. The eccentricity of a contact is de-

scribed by the relation of the eigenvalues λ

and λ

. If

both eigenvalues have a similar value, then the contact

area has a round shape and the eccentricity is close to

zero. For these contacts it is not possible to calculate

the orientation.

Touching an edge results in an oblong ellipse with

an eccentricity ε close to 1 when using

ε =

(µ

2,0

− µ

0,2

)

+ 4µ

1,1

(µ

2,0

+ µ

0,2

)

ε ∈ [0, 1] . (8)

A corner point results to an eccentricity close to zero.

To control the orientation of the sensor pad with re-

spect to the object surface, we are interested in the

OBJECT EXPLORATION WITH A HUMANOID ROBOT - Using Tactile and Kinesthetic Feedback

277

Figure 4: The angle θ between the principal axes of the

tactile image and the sensor coordinate system.

Figure 5: The 9 regions of

the sensor pad.

Figure 6: Evaluation of a

contact point.

angle θ between the principal axes and the sensor co-

ordinate system (cf. Fig. 4) which can be readily com-

puted by

θ =

arctan

2µ

1,1

2,0

− µ

0,2

. (9)

When tracking an edge, the desired angle θ is zero

and can thus be directly used as the system deviation

input to the controller to control one orientation DOF.

The angle theta will be also referred to as the quality

measure q

in the following.

To evaluate a contact point regarding to the orien-

tation along the y-axis, we compute a quality measure

from the distance |y

−y

| of the contact point (x

)

to the center of the sensor pad called (x

). As the

correlation between this distance and the angle is not

linear, we weight the distance by a circular function.

This circular function f with the radius r is given by

(x) =

− x

(10)

This results into the function

− |y

− y

(11)

Figure 6 illustrates the computation of this quality

measure which is used for the alignment of the sen-

sor pad towards the object surface.

Furthermore, we devide the pad into 9 regions: 4

corner regions, 4 border regions and one interior re-

gion, as shown in ﬁgure 5. Each region is checked,

if it accommodates a contact or not. This computa-

tion results into a 9-dimensional binary feature vector

which can be used for a simple classiﬁcation like the

determination of corner contacts or contact side, de-

pending on the present context.

3.2 Kinesthetic Perception

The force-torque sensor that we use is an FTC 50-40

from SCHUNK. It has 6DOFs, with a range of 150N

for the forces, 4Nm for the torques Mx and My, and

8Nm for Mz. The accuracy is 5%. The data is sam-

pled every 1ms and transmitted via CAN bus with a

baudrate of 500kbit/s.

Since the data is quite noisy, the preproccesing of

the sensor data includes a median ﬁlter with window

size 7 to remove outliers. Since the tactile sensor pad

is mounted on the top of the sensor, we must deduct

its weight from the sensor values.

The compensation of the torques and forces is

only possible in the global robot coordinate system

and not in the local system. Local compensated forces

are computed via back-transformation of global com-

pensated forces into local coordinates. The required

transformation steps are f

→ f

g,c

→ f

l,c

, where

l and g mark local coordinated and global respec-

tively. The marker c describes compensated values.

It is possible to determine contacts without

the tactile sensor by relating the measured torques

) with a model of the used sensorpad. The

sensorpad can be described as a simple rectangle with

the length (l

). The angle α from the center of

the pad to the contact point can be calculated by

atan(m

). The angle furthermore describes, if a

contact is safe or not. A corner contact is declared

as unsafe, as is it not accurate enough to identify the

correct contact side.

4 RESULTS

At ﬁrst, the measuring accuracy of the contact points

is investigated using the tactile and the force-torque

sensor. Then, the implementation of a skill with dis-

crete movements for edge tracking is shown. This

skill is used to evaluate the exploration with each sen-

sor in a stand-alone application and with a combina-

tion of both sensors. Finally, a skill with continuous

movements is shown and evaluated.

ICINCO 2008 - International Conference on Informatics in Control, Automation and Robotics

278

Figure 7: Implementation of a discrete skill.

4.1 Measuring Accuracy

To obtain the measuring accuracy of the tactile and the

force-torque sensor, reference and measured points

are generated consecutively along a line with a dis-

placement of 1.0 cm in relation to the center of the

tactile sensor pad. The measured contact points are

determined by the interpolation of the contact area in

the resulting tactile image. This simple experiment

has shown that the used tactile sensor matrix with a

cell distance of 0.6 cm has a mean square error of

0.2 cm. Furthermore, the accuracy is not correlated

to the location of the contact point. A similar ex-

periment has been done for testing the force-torque

sensor. Here, the reference points were taken only at

the boundary of the sensor pad. The measured mean

square error is 0.6 cm.

The tactile sensor is superior to the force-torque

sensor regarding the measuring accuracy. The tacile

has one signiﬁcant disadvantage - it exist a critical

angle which restricts the operational area. Applying

the maximum force of 8N towards a planar surface,

the critical angle is 18 degrees. If the sensor pad is

aligned with a larger angle towards the object, a con-

tact cannot be detected anymore.

4.2 Discrete Skill for Edge Tracking

4.2.1 Implementation

The objective is a skill which pointwisely tracks an

edge. Figure 7 shows a coarse view of the implemen-

tation of such a skill consisting of 4 main states and

several sub-states. The skill tracks the contour of an

edge and changes the direction of exploration as soon

as the ﬁrst corner has been detected. The ﬁrst state

involves the determination of the ﬁrst contact and the

contact side. Therefore, the robot arm moves into a

speciﬁed direction until it detects a contact and de-

parts again. According to this ﬁrst contact, the orien-

tation of the sensor pad is aligned so that the long side

Figure 8: Visualisation of the exploration procedure.

Figure 9: Evaluation using only force-torque sensor.

of the pad is used for tracking the edge. After this ini-

tial alignment, the ﬁrst contact point is collected and

the arm departs again. The ﬁrst two steps are only

executed once.

The third step is executed consecutively until a

corner point and hence the end of the edge is detected.

This step collects the next contact point, departs from

the object, evaluates the current spatial alignment, and

executes a correction of the alignment. If a corner is

detected by the tactile perception, the fourth phase is

triggered which involves the decision to change the

direction of the exploration, if the ﬁrst corner has been

detected, or to stop the exploration if the ﬁnal cor-

ner has been found. Figure 8 visualizes the outcome

of such an exploration procedure: a set of points and

lines.

4.2.2 Force-torque Sensor vs. Tactile Sensor

To compare both sensors, the implemented skill has

been executed once only with the force-torque sen-

sor and another time only with the tactile sensor ma-

trix. In order to make the skill be executable for both

sensors in a stand-alone application, several require-

ments must be considered. At ﬁrst, the sensor pad

was aligned with an angle of 10 degrees towards the

object. Secondly, the exploration procedure only in-

OBJECT EXPLORATION WITH A HUMANOID ROBOT - Using Tactile and Kinesthetic Feedback

279

Figure 10: Evaluation using only tactile sensor.

Figure 11: Evaluation using force-torque sensor and tactile

sensor.

volves tracking the edge in one direction up to the ﬁrst

corner, as the force-torque is not able to detect the

corner of an edge. After the second contact point, the

alignment towards the edge is evaluated.

For tracking an edge two alignments are needed.

At ﬁrst, the imprint of the edge must be parallel to

a speciﬁed boundary of the sensor pad. Secondly,

the center of gravity of the contact point must be in

the center of the sensor pad. For these two align-

ments, two quality measures were introduced in sec-

tion 3. For the sake of convenience, these two qual-

ity measures will be scaled to a score from zero to

ten whereas ten indicates a good score. The score

XY refers to the difference between the center of the

contact point and the center of the sensor pad, as de-

scribed in equation 11. The second score Z refers to

the deviation of the angle of the edge, as stated in

equation 9. The preﬁx 2-Point indicates that only the

last two contact points are used for an estimate of the

edge whereas the preﬁx Local points out that all ex-

tracted point so far are taken into account.

Both experiments haven been repeated several

times. Representative results of both experiments are

shown in ﬁgures 9 and 10. The diagrams plot four

curves which result from two quality measures for the

current alignment, labelled 2-Point-XY and 2-Point-Z

and two quality measures for the alignment over sev-

eral exploration steps, labelled Local-XY and Local-Z.

As to be expected, the tactile sensor scores well

with an average score of about 9 points for both qual-

ity measures. The results of the force-torque sensor is

worse with an average score of 5 points. Furthermore,

the tactile edge tracking converges faster towards the

optimum of the quality measures. Both sensors en-

able the robot to explore an edge considering some

restrictions but both sensors also complement each

other. The FTS-determination of contacts points is

less accurate but independent from the edge angle. It

allows the alignment of the sensor pad so that the crit-

ical angle of 18 degrees is under-run and the tactile

sensor can take over the exploration procedure in or-

der to undertake a more precise computation of the

edge.

4.2.3 Combination of Both Sensor

Finally, the edge tracking with a combination of both

sensor is evaluated. The initial angle is 25 degrees

which exceeds the critical angle. Figure 11 shows the

result of the exploration procedure. The additional

comment shows one of the three possible states of the

contact point: FTS-contact, TS-contact, and corner

point. As predicted, the orientation of the edge can be

calculated only by the use of the force-torque sensor

so that the tactile sensor can take over the exploration

after two FTS-contacts. Signiﬁcant is the increase of

the quality measures after the tactile sensor has taken

over. After this, the tactile sensor does not loose con-

trol over the exploration procedure at any time.

4.3 Continous Skill for Edge Tracking

The next experiment involves a skill with continu-

ous movement according to the proposed control loop

presented in section 2. The task is to follow the edge

without loosing contact to the object surface. For

this kind of skill only the accomplishment of certain

phases is checked. This experiment has been per-

formed ﬁve times.

The ﬁrst phase involves the alignment of the pad

towards the edge using zero-force control followed by

the tracking of the edge until the ﬁrst corner point.

The third phase involves the return to the starting po-

sition and tracking towards the opposite direction. In

all experiments, all phases were completely accom-

plished. Signiﬁcant is that this exploration procedure

ICINCO 2008 - International Conference on Informatics in Control, Automation and Robotics

280

took one third of the time compared to the edge track-

ing skill based on discrete movements (250s vs. 80s).

4.4 Comparision of Both Skills

The skill with discrete movements as well as the skill

with continous movements are capable to completely

explore an edge. Both skills have beneﬁts and dis-

advantages. The beneﬁt of the skill with continu-

ous movements is its speed and its simplicity as it

needs less states compared to the other skill. In par-

ticular, the fast alignment towards the edge using an

adapted zero-force control has to be pointed out. As

the skill with discrete movements has at ﬁrst to col-

lect single points to perform the alignment gradu-

ally, the skill needs more time and more control pro-

cesses. Otherwise it is easier for a superior level to

supervise these discrete movements. As outliers can

be detected by collecting a great amounts of contact

points, the exploration behavior becomes very stable.

For the exploration of unknown structures, collecting

single points is still favored, as it provides the supe-

rior level with more possibilitys for interaction. Skills

only based on a control algorithms need the whole

ﬂow of information for the spatial alignment and are

not made for interaction. A combination of both ap-

proaches seems to be promising.

5 CONCLUSIONS AND FUTURE

WORKS

This work introduced a novel framework for the ex-

ploration of objects using haptic feedback. This

framework consists of three layer: an exploration

planner, a skill library with reactive exploration be-

haviors and a haptic perception layer. Two different

skill schemes have been introduced: skills with dis-

crete and skills with continuous movements. The per-

formance of our approach has been evaluated in the

evaluation scenario of tracking an edge which is ar-

bitrarily located in space. The tactile sensor and the

force-torque complement one another. The determi-

nation of contact points using a force-torque sensor

is less accurate but independent from the edge angle.

It allows the alignment of the sensor pad so that the

critical angle for the tactile sensor is under-run and

the tactile sensor can take over the exploration proce-

dure in order to undertake a more precise exploration

procedure.

Furthermore it became apparent that the explo-

ration with discrete and the exploration with contin-

uous movements have both beneﬁts and drawbacks.

The continuous exploration based on a control behav-

ior is faster but provides less possibilities for inter-

action. Exploration procedures with discrete move-

ments are slower but are more robust and better to su-

pervize. A combination of both approaches seems to

be promising. Future work will include the extension

of the skill library and the transfer of an exploration

behavior on a humanoid robot hand.

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