Exploring the world through grasping:a developmental approach*
Lorenzo Natale, Francesco Orabona, Giorgio Metta, Giulio Sandini
LIRA-Lab, DIST
University of Genoa
Viale Causa 13, 16145 Genoa, Italy{nat, bremen, pasa}@liralab.it, sandini@unige.it
Abstract – This paper is about the implementation ofgrasping skills in a humanoid robot. Following adevelopmental approach the robot is initially equipped withlittle perceptual and motor competencies whose role is tobootstrap learning through the exploration of the external
environment. This crude form of sensorimotor coordinationconsists of a set of control systems and explorative behaviors aswell as simple visual routines. The developmental path leads
the robot from the exploration of the physics and geometry of
its body to the probing of the external environment. The robot
experience builds and modifies continuously its internal
representations of the environment, being this its body or theobjects it encounters. We discuss the implications of ourapproach to the study of cognition and our effort to build a
cognitive artificial system.
Index Terms – Humanoid robotics, development, cognitive
systems.

I. INTRODUCTION
There is a growing scientific and practical interest to thestudy of cognitive systems being these biological (toanalyze them) or in trying to replicate their capabilities(engineering them). Unfortunately though, there is not evenan accepted definition of what a cognitive system is. Lyingat the two ends of the spectrum we find cognitivism [1] on
one side and enactive systems [2, 3] on the other. According
to the first, cognition is a computational process carried out
on a symbolic representation of the world. Symbols
represent the world and can be shared across different
entities: they are a complete characterization of the world inwhich the entity is located, and as such are independent of
the entity itself and its past experience. In enactive
approaches instead, cognition is the result of the interaction
and co-development of the agent’s body and theenvironment in which the agent lives. Although thedefinitive answer is still to be found, physical realizations ofartificial systems can prove to be a valid test-bed for
different theories and models.
In the debate we see our approach as more sympathetic
toward the enactive systems end. Two aspects seem thus
crucial: i) embodiment and ii) development. The two
requirements are obviously intertwined, as the interaction
between the agent and the environment is possible only bymeans of a body. As a consequence, representations of theenvironment depend on the particular embodiment and,more importantly, on the past experience of the system. It is*

worth noting that representation here is taken in its mostneutral and common-sense meaning.
To see how extreme it could be, consider for example,
the visual system of primates. This is efficient only as longas the animal moves the eyes to place the high-resolution
fovea at interesting places in the environment. Attention
regulated by a sophisticated control system allows only
specific stimuli to be selected among the wealth of possible
choices. Through action, the system’s embodiment and theenvironment codetermine the resulting representations.
Motivated by these considerations we propose adevelopmental approach to the implementation of cognitive
abilities in a humanoid robot. We identified the minimum
embodiment as possessing a head, arm, and hand. Although
limited, this robot can perform goal-directed actions on
objects, like reaching and grasping, it can visually scan theenvironment, and actively explore it by taking forcefulactions.
A grossly simplified though plausible developmental
process can be described in three distinct phases – they do
not necessarily need to be completely separated. The firststage is devoted to learning the internal models of therobot’s body (known also as the body-map) which
eventually provides basic motor and perceptual skills likegaze control, eye-head coordination and reaching. Based onthese abilities the interaction with the external world isinvestigated in the second phase where the robot discovers
properties of objects and ways to handle them (learning tointeract). The robot actively explores the environment by
taking actions (e.g. reaching) which allows starting theacquisition of information about the physical coherence andthe functioning of the environment and, at the same time,
discovering new, more efficient, ways of interaction (forexample different grasp types). Finally the third stage isabout learning to understand and interpret dynamical events
possibly including other agents: the robot has associated its
actions with the resulting perceptual consequences.
Interpretation of other people’s actions is achieved byinverting this association.
In the past we have addressed aspects related to thethird phase [4, 5]. This paper is focused only on the first twophases.
II. EXPERIMENTAL SETUP
The experimental setup consists of the robotic platformshown on Fig 1. It is an upper torso humanoid robot

Funding for the research described in this paper has been provided by the EU projects ADAPT (IST-37173) and
ROBOTCUB (FP6-004370).

composed of a 5 degree of freedom (DOF) head, 6 DOF
arm and a 6 DOF hand. The head has 5 DOF, two of which
control the neck pan and tilt, whereas the other three actuate
two cameras to pan independently and to tilt on a common
axis. The arm is a Unimate PUMA 260, an industrial
manipulator with 6 degrees of freedom; it is mounted withthe shoulder horizontal (typically vertical) to better mimic
the human kinematics. The hand consists of 5 fingers; eachfinger has three phalanges, the thumb has an additional
degree of freedom which allows it to perform a rotation
toward the palm. Overall the hand has 16 joints controlled
by only six motors. Two motors are connected to the index
fingers: they are linked to the first (proximal) and second
phalanges. The distal (small) phalange is mechanicallycoupled to the preceding one so that the two bend together(see Fig 1). Two motors control the motion of middle, ringand little finger. As in the case of the index finger, theproximal phalanges are linked to the first motor, while thesecond and third phalanges are actuated by the second
motor. The mechanical coupling between the joints isrealized by means of springs to allow a certain amount ofadaptation of the grasp type to the object shape.
From the point of view of the sensors, the head isequipped with two cameras and two microphones for visualand auditory feedback. Tactile feedback is provided by 17
force sensing resistors mounted on the hand, 5 of which areplaced on the palm and the remaining 12 evenly distributedon the thumb, index, middle and ring fingers. A JR3 force
sensor provides torque and force feedback at the wrist.
Further proprioceptive information is provided to the robot
by optic and magnetic encoders mounted on all joints of thehead, arm and hand.

Fig 1: a) the experimental setup, the Babybot. Left: details of the hand. b) and c): elastic compliance. d)-f): mechanical coupling between phalanges.

III. VISION
One of the first steps of any visual system is that oflocating suitable interest points in the scene (“salient
regions” or events) and eventually direct gaze toward theselocations. Human beings and many animals do not have auniform resolution view of the visual world but rather onlya series of snapshots acquired through a small highresolution sensor (e.g. our fovea). This leads to twoquestions: i) how to move the eyes efficiently to important
locations in the visual scene, and ii) how to decide what is
important and, as a consequence, where to look next.
There is accumulating biological evidence that attention
is directed to an object or a group of objects, and that theattention system processes properties of objects, rather thanregions of space. In fact, it has been shown that selective
attention frequently operates on an object-based
representational medium in which the boundaries ofsegmented objects, and not just their spatial position,
determine what is selected and how attention is deployed(see [6] for a review). In other words, the visual systemseems optimized for segmenting complex three-dimensional

scenes into representations of (often partly occluded)
objects for recognition and action. Indeed, perceivers must
eventually interact with objects in the world and not withdisembodied abstract locations.
The literature on visual attention in artificial vision isvast and several models have been proposed [7, 8]; most ofthem are derived from Treisman’s Feature Integration
Theory (FIT) [9]. The FIT model employs a set of low-levelfeature maps which are combined together by a spatialattention window operating in a master saliency map.
The visual attention model we propose starts fromapproximately the same type of considerations but then it
uses a concept of salience based on proto-objects defined as
blobs of uniform color in the image. The robot by acting on
objects can then figure out how to combine these protoobjects into coherent wholes, i.e. full-fledged object. Oncean object is grasped, in fact, the robot can move and rotate it
and build a statistical model of the color blobs and their
spatial relationship. This internal representation cansubsequently be used to instantiate the top-down componentof the attention system.
Throughout the paper we employed log-polar images as
defined for example in [10], which mimic the distribution of

cones, i.e. the photoreceptors of the retina involved indiurnal vision. Cones have a higher density in the central
region called the fovea, while they are sparser in the
periphery. We never used standard rectangular images.
As a first step, the input image is smoothed in time, by
taking the average between the current frame and the outputof the color quantization (see later in text) on the previous
frame (see Fig 2). Then the red, green, blue channels ofeach image are separated, and the yellow channel iscalculated as the average of the red and green one. Thesefour channels are combined to generate three color
opponent channels, similar to those of the retina. Each ofthese channels, typically indicated as (R+G-, G+R-, B+Y-),
has center-surround receptive field (RF) with spectrally
opponent color responses. That is, for example, a red input
in the center of a particular RF increases the response of thechannel R+G- while a green one in the surrounding
decreases its response. The spatial response profile of the
RF is expressed by a Difference-of-Gaussians (DoG). A RF
centered on each pixel of the input image is considered, so
the output of the RF filtering is obtained with a convolution
with DoG kernel, generating an output image of the same
size of the input. The DoG filters are not balanced andsimilarly to what happens in the human retina theunbalanced ratio implicitly code achromatic information.
Edges are then extracted on the three channels
separately by employing a generalization of the Sobel filter
due to [11]. The resulting edge maps are combined together
to generate a single map. A watershed transform [12] is thenapplied on the edge map to isolate blobs and to generate
proto-objects. As a result the image is segmented in blobs
with either uniform color or uniform gradient of color.
At this point each blob is tagged with the mean color of
the pixels within its internal area (this leads to a colorquantized image). The result is blurred with a Gaussian
filter and stored: it will be averaged with the next frame toobtain a temporal filtering and reduce the effect of noise.
As discussed above, it is known that a feature or
stimulus is salient if it differs from its immediatesurrounding area. The bottom-up salience is calculated as
the Euclidean distance in the color opponent space between
each blob and its surroundings. Moreover, the size of the
spot or focus of attention is not fixed but rather linked to thesize of the blobs. In the same way the definition of
immediate surrounding area is relative to the size of the
focus of attention. In other words, we compute the salienceof each blob in relation to a neighborhood region whose size
is proportional to that of the blob itself. In our
implementation we use a rectangular region 2.5 times thesize of the bounding box of the blob. Blobs that are too
small or too big are discarded from the saliency computationand ignored and they will not be considered as possible
candidates to be part of objects (proto-objects).
The top-down influence on attention is calculated inrelation to the visual search task. When the robot has
acquired a model of the object and begins searching for it, it
uses the knowledge of the object’s appearance to bias thesaliency map. In practice, the top-down saliency map iscomputed as the distance between the average color of eachblob and that of the target:

2

2


 
 

Stop − down =  R + G − − R + G −  +  G + R − − G + R −  +  B +Y − − B +Y − 

 
 

objectobjectobject blob  blob  blob


2

(1)
where

indicates the average of the image values over acertain area (indicated as subscripts). The total salience issimply estimated as the linear combination of the two terms
above:
S = α Stop − down + β Sbottom −up (2)where Stop-down and Sbottom-up are defined in text. The total
salience map S is eventually normalized in the range 0-255,
as a consequence the salience of each blob in the image isrelative to the most salient one.

Input image

+

R+G-

G+R-

B+YEdges

Color quantization

Salience Map

Fig 2: The visual attention system, block schema.

Local inhibition is transiently activated in the salience map.
This prevents the focus of attention from being redirected
immediately to a location that was previously attended.
Such an inhibition of return (IOR) has been also
demonstrated in human psychophysics.
Our system implements a simple object-based IOR. The
robot maintains a list of the last five positions visited, coded
in a body centered coordinate system. When the robot
redirects gaze it keeps memory of the blobs it has visited.
Inhibition occurs only if the blob presents the same color ofthat stored in the list; in case the object moves or its color
changes the location becomes available again for fixation.
IV. LEARNING ABOUT THE SELF
In general the set of the internal models that represent the
body is called a body-schema or body-map: it involves, forexample, the relative position of the limbs, the weight of the
body segments and their size. The existence of a bodyschema in the brain has gained some support thanks to thework of Graziano [13, 14] and Rizzolatti [15] who foundneurons in the primate motor cortices coding the position of
the hand in the visual field irrespective of the relative
position of the head and hand.
In biological systems the internal representation of the
body is shaped during development and maintained fit to thephysical modification occurring in life. In artificial systems(where the body does not normally change with time)
adaptation can avoid the tedious operation of manuallytuning the system’s internal models and their parameters.
The latter might be required to compensate changes in thevisual appearance of the body or drift in the sensors (e.g.
motor encoders).
We propose here an approach similar to Fitzpatrick and
Arsenio [16] and Metta and Fitzpatrick [17]. Repeated, selfgenerated actions are initiated by the robot during thelearning phase. In particular we controlled the robot to
execute a periodic movement of the wrist. The resulting
motion of the hand was detected by computing the image
difference between the current frame and an adaptive modelof the background. The period of motion of each pixel in the
resulting motion image was then computed with a zerocrossing algorithm; similar information was extracted on the
proprioceptive feedback of each motor encoder. As a result
the hand of the robot was segmented by selecting, among
the pixels that moved periodically, those whose period
matched that of the joints. Conversely non-periodic moving
pixels or pixels moving with different periods wereidentified as being originated by other sources anddiscarded. Fig 3 shows an example of the detection andsegmentation of the robot’s hand.

Fig 3: An example of the detection procedure. From left to right: theoriginal image at the beginning of the procedure, the result of the detection,
the result of a further low-pass filtering, and the final segmentation.

This information about the hand is employed to learn
the position of the hand in the visual field given the current
arm and head posture and, simultaneously, the shape andorientation of the hand (in this case represented by anellipse). Learning is carried out by using a neural network
specifically designed for online learning [18]. Eventuallythese models could predict the position of the robot’s handin the image given a future position of the head and arm.
Although of some utility, the real goal of segmenting
the hand is for guiding reaching. The solution we propose isbased on the use of a direct mapping between the eye-head
motor plant and the arm motor plant [19]. In other words,
reaching for an object starts by looking at it. Under thisassumption, the fixation point can be seen as the “endeffector” of the eye-head system. The position of the eyeswith respect to the head, determines uniquely the position of
the fixation point in space relative to the shoulder. The arm

motor command can be obtained by a transformation of theeye-head motor/positional variables. We called thisapproach motor-motor coordination, because thecoordinated action is obtained by mapping motor variables
into motor variables:
qarm = f (qhead ) (3)where qhead and qarm are head and arm posture respectively(joint space). What is interesting in this approach is notequation (3) per se, which, after all, implements the inverse
kinematics of the arm, but the mechanisms we use to learn
it. In fact this mapping can be easily learnt if the robot canlook at its hand, which is not incidentally the by product ofknowing the position of the hand in the image.
The robot explored the workspace by moving the arm
randomly, while at the same time, it tracked the hand;
whenever the head fixated the hand a new pair arm-head
posture was acquired and used as a training sample to theneural network that approximates f in equation (3). When asufficient number of samples were acquired (and the
network trained), the robot started using the mapping toreach for visually identified objects.
Once the robot has computed the final arm posture it isstill required to plan the actual movement. This was donewith a simple linear interpolation between the current andfinal arm configuration. The trajectory was divided in steps
which were effected by the low-level controller; to thispurpose we employed a low-stiffness PD controller withgravity compensation. The gravity load term for each joint
was also learnt online as described in [20].
V. BUILDING OBJECT MODELS
Either by reaching and randomly grasping or because ofa cooperating human the robot eventually will grasp an
object. Both solutions are valid bootstrapping behaviors forthe acquisition of an internal model of the object. When the
robot holds the object it can explore it by moving androtating it. In short, the idea is to represent objects ascollections of blobs as generated by the visual attention
system and their relative position (neighboring relations).
The model is created statistically by looking at the same
object several times from different points of view. The
system estimates the probability for each blob to belong tothe object by counting the number of times each blob
appears during this exploration phase.
We used the probabilistic framework proposed by
Schiele and Crowley [21]. It consists of estimating theprobability of the object O given a certain local
measurement M. This probability P(O|M) can be calculatedusing Bayes’ formula:P ( M | O ) P (O )P (O | M ) =
(4)
P(M )

OMAP = arg max { P ( O | M ) , P ( ~ O | M )}O ,~ O

where: P(O) is the a priori probability of the object O, P(M)the a priori probability of the local measurement M, andP(M|O) is the probability of the local measurement M when
the object O is being fixated. In the following experiments
we carried out only a single detection experiment per object;there are consequently only two classes, one representing
the object and another representing the background. P(O)and P(~O) are simply set to 0.5 since this choice does notaffect the maximization of equation 4.
Since a single blob is not discriminative enough, we
considered the probabilities of observing pair of blobs, i.e.
the local measure M is the event of observing both thecentral and a surrounding blob:P ( M | O ) = P Bi | Bc and ( Bi adjacent Bc ) (5)()

where Bi is the i-th blob surrounding the central blob Bc
which belongs to the object O. We exploit the fact the robot
is fixating the object and assume Bc to be constant acrossfixations. In practice this corresponds to estimating theprobability that all blobs Bi adjacent to Bc (which we take asa reference) belong to the object. This procedure, althoughrequiring the “active participation” of the robot (throughgazing) is less computationally expensive compared to theestimation of the probability for all possible pairs of blobsof the fixated object. Estimation of the full joint
probabilities would require a larger training set than the one
we used in our experiments. The probabilities P(M|~O) are
estimated during the exploration phase considering all theblobs not adjacent to the central blob. The local
measurements are treated as independent, because they referto different blobs, so the total probability P(M1,…,Mn|O) can
be factored into the product of the probabilities P(Mi|O). An
object is detected if the probability P(O|M1,…,Mn) is greaterthan a fixed threshold.

Figure 4: Visual search. The robot has acquired a model of a toy airplane
during the exploration phase (not reported); this information primes theattention system which assigns a high saliency to the blue blob at the centerof the airplane. A saccade is performed, the object is foveated. (a) and (b)show the visual scene before and after the saccade. (d) and (e) show theresult of the attention system at the same instant of time. The result of thesegmentation after the saccade is in (c).

When an object is detected after visual search, apossible figure-ground segmentation is attempted, using theinformation gathered during the exploration phase. Eachblob is segmented from the background if it is adjacent tothe central blob and if its probability to belong to the object
is greater than 0.5. This probability is calculated using theestimated probability above with the following
approximation:
P ( Bi ∈ O | Bc and ( Bi adiacent Bc ) )  P ( Bi | Bc and ( Bi adiacent Bc ) )(6)

Fig 4 shows the result of the visual search task for a given
object (a toy airplane) and of the segmentation procedure(panel C).
VI. DISCUSSION AND CONCLUSION
To recapitulate, we have shown how two phases ofautonomous development could be crafted into a humanoid
robotic system. It is important to note that the combinationof the various components was designed in by theexperimenter and not acquired by the robot. Still each of thecomponents showed some component of learning. Fig 5shows this exemplar behavior through a sequence ofpictures taken from the robot’s point of view.
The action starts when an object is placed in the robot’s
hand and the robot detects pressure in the palm (picture 1).
This elicits a clutching action of the fingers; the handfollows a preprogrammed trajectory, the fingers bendaround the object toward the palm. If the object is ofappropriate size, the intrinsic elasticity of the handfacilitates the action and the grasping of the object. The
robot moves the arm to bring the object close to the cameras
and begin the object exploration. The object is placed infour different positions (as for instance in pictures 2 and 3).
During the exploration phase the robot tracks thehand/object; when the object is stationary and fixation isachieved, a few frames are acquired and the model of theobject is constructed. At the end of the exploration theobject is released (picture 4).
At this point the robot has acquired the visual model ofthe object and starts searching for it in the visual scene. To
do this, it selects the blob whose features better match thoseof the object’s main blob and perform a saccade. After thesaccade the model of the object is matched against the blobthat is being fixated and its surrounding. If the match is
negative the search continues with another blob, otherwise
grasping starts (pictures 7-8-9). At the end of the task the
robot uses simple haptic information to detect if it is holding
the object or rather the action failed. In this process theweight of the object and its consistency is checked(proprioception from the fingers holding the object). If theaction is successful the robot waits for a new object to startagain, otherwise it performs another reaching-grasping trial.
It is fair to mention that part of the controller for thisexperiment was preprogrammed. For example, the hand wascontrolled with stereotyped motor commands. Three
primitives were used: one to close the hand after a certain
amount of pressure was detected on the palm, and two
during grasping to pre-shape the fingers and actually seize
the object. The robot relied on the elasticity of the hand toachieve the correct grasping. To facilitate grasping, thetrajectory of the arm was also programmed beforehand;
waypoints relative to the final position of the arm were
included in joint space to approach the object from above.
In spite of these limitations, we have presented resultson two important phases of the acquisition of sensorimotor
coordination in a relatively sophisticated humanoid robot.
We have shown the implementation of a visual attention
system employing top-down and bottom-up information.
More importantly, we demonstrated how the robot can
actively explore the visual appearance of the objects ithappens to grasp. This information is also fed to theattention system as a bottom-up primer to control the searchof the object. Thus the robot experience allows building a
representation of the objects it interacts with while, at thesame time, modulating the attention system. The robot’s
ability to act is used together with the body internal model
to drive the exploration of the environment. This facilitateslearning in different ways. At first, it helps the system tofocus attention in both space and time. During theacquisition of the object visual model, in fact, the robot can
track the object because it knows the position of the handfrom its proprioceptive feedback. Proprioception is also

useful to detect when the acquisition of the model can beinitiated since the object motion is under direct control by
the robot. Finally the fact that the object is being held by thehand guarantees the link between different sensory
modalities (for example the sight of the object and thekinesthetic information from the hand). The object model
makes use of visual information; in [20] we show instead
how it is possible to build a model of the object based on
haptic information only. In the future we would like toinvestigate possible ways to integrate the two approaches.

1

2

3

4

5

6

7

8

9

Fig 5: A sequence of the robot grasping an object. The action starts when an object is placed on the palm (1). The robot grasps the object and moves the eyes
to fixate the hand (2). The exploration starts in (3) when the robot brings the object close to the camera. The object is moved in four different positions while
maintaining fixation; at the same time the object model is trained (3). The robot drops the object and starts searching for it (4-6). The object is identified and asaccade performed (7-8). The robot eventually grasps the toy (9).

This work supports the view of cognition emerging
from the embodied interaction between the system and theenvironment. Cognition requires a body and the ability toautonomously build the representation of the external world
through this interaction. Even a simple set of behaviors has
been sufficient to bootstrap the exploration of theenvironment and the acquisition of a representation of it.
We have shown how this initial interaction is sufficient tostart linking action with its consequences to form prediction
about the behavior of the body and the environment.
Very often prospective control is required to plan a
successful action. During grasping, for example, the correct
timing of preshaping and closure of the fingers is required;
the lags in the sensory processing (visual and tactile) typicalof artificial and natural systems make feedback control
ineffective. To be able to anticipate the impact of the handwith the object is required to control the timing between

preshaping and actual grasping without relying on visualand tactile feedback. Prospective control, however, is not
only important for action. It gives the agent the possibility
to create expectations on which to base the interpretation of
the world and the actions performed by others. Throughinteraction with the world the agent builds a model of how
the entities involved behave and what is the resulting
sensorial consequence. This link can be used afterward to
anticipate the consequence of a similar action and,eventually, compare it with the real feedback.
In the same way, new situations can be interpreted bymatching them against the agent’s past experience. For
example the event of a ball that falls on the floor (and theresulting visual and auditory sensations) can be associatedto the action of dropping it. Anticipation and prediction
enhance the agent’s ability to understand and interact withthe environment and, for this reason, are important aspectsof cognition. The results of this paper are the first necessary
steps into the effort of developing cognitive abilities in an
artificial system.
ACKNOWLEDGEMENTS
The authors wish to thank the anonymous reviewers for the
fruitful comments and discussion on the earlier version ofthis manuscript.
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