From sensorimotor development to object perception
Lorenzo Natale, Francesco Orabona, Fabio Berton, Giorgio Metta, Giulio Sandini
LIRA-Lab, DIST
University of Genoa
Viale Causa 13, 16145, Genoa, Italy
Email: {nat, bremen, fberton, pasa, sandini}@liralab.it

Abstract— This paper describes a developmental sequence thatallows a humanoid robot to learn about the shape of its bodyand successively about certain parts of the environment. We
equipped the humanoid robot with an initial set of motor andperceptual competencies ranging from simple stereotyped actions
to more sophisticated visual routines providing a bottom-up
attention system. This initial form of sensorimotor coordination
is sufficient to initiate the interaction with the environment andallows the robot to improve its motor and perceptual skills by
first constructing a “body-schema” and later by learning about
objects. The body-schema allows controlling movements to fixate,
reach and touch objects in the environment. The interaction isfurther used to form a visual model of the objects grasped by
the robot which eventually modulate the attention system in a
top-down way. In another experiment we show an initial effort
to study the acquisition of object affordances. We discuss theimportance of sensorimotor coordination as a required step not
only for the control of action but also, and more importantly, forperceptual development.

I. I NTRODUCTION
Manipulation is a unique opportunity to study the interaction
between an artificial system and the environment. We focus on
manipulation not only as a means to perform useful practicaltasks but, also and in particular, because it offers the possibility to investigate active learning. Active learning refers, forexample, to the ability of an agent to autonomously performand guide the exploration of the environment. At a deeper
level, action can contribute to change the environment in a
direction that is best suited to the agent’s goal, for example,
to facilitate perception. In this context manipulation allows
the agent to collect information about objects by performing
specific actions on them [1]. Even very simple forms of action
like poking or pushing an object can be sufficient to thispurpose [2].
The sensorial experience of a humanoid robot can bequite rich including, for example, the possibility of multisensory perception: some features of objects are more naturally
perceived through senses other than vision. For example thesmoothness of a surface, the weight of an object and its
three dimensional structure are naturally determined throughtactile experience. This information is extracted by appropriate
exploratory actions. The importance of motor activity for perceptual development has been emphasized in developmental
psychology [3], [4]. Many researchers agree on the fact thatmotor development during infancy determines the timing ofperceptual development (for a review see [5]). For example
perception of object features such as volume, hardness, texture

and weight are unlikely to emerge before 6/9 months of age.
Haptic sensitivity of three dimensional shapes appears evenlater at around 12/15 months. It is perhaps illuminating that
this timetable fits surprisingly well with the development ofactions in infants: the ability to move the hand is requiredfor infants to begin manipulating objects and consequently
perceiving certain properties. Lederman and Klatzky [6] haveshown that adults make use of stereotyped hand movement(exploratory procedures) to determine certain properties ofobjects; different procedures were employed by subjects toassess different properties. The ability of infants to correctly
execute these procedures can determine their ability to perceive the associated object characteristic. Infants ability tointeract with objects is indeed quite limited at birth; earlyreaching in newborns is pretty inaccurate and only rarely resultin actual contact with the object [7]. At the age of three months
infants are more reliable in grasping objects, although grasp
is usually with the full hand open (as in the power grasp).
Only later on at 6/9 months of age infants become skilled
in handling objects and grasping them with differentiatedgrasp types [8]. Accordingly, perception of properties like
temperature, size and hardness can occur relatively early indevelopment, whereas properties requiring more dexterous
actions like texture or three dimensional shape would emerge
only later on.
Similarly, we are pursuing a developmental approach forthe design of a humanoid robot. Development of the robot
unfolds along three phases: learning about the body, learning
to interact with the environment, and learning to interpret
events. In the first phase the robot learns properties of its
body, which allow recognizing and controlling movement.
For example, the robot controls reaching movements by firstlearning the weight of its arm and to recognize the hand. Theseabilities are used in the next phase to initiate the interaction
with the environment and to learn about it. The robot begins
this exploration by reaching for objects and learning their
physical properties when it manages to grasp them [9]–[11].
The robot’s experience acquired during this interaction is usedin the third phase to interpret events around the robot bymatching expectations and perceptions (as for example in [1]).
We focus here on the two first phases: learning about the bodyand learning to interact with the environment. We describe how
the robot builds an internal model of its hand which allows
localizing it in the visual scene. The hand internal model isused to direct gaze towards the hand and to learn an inverse

Fig. 2.

Details of the hand of the Babybot.

of compliance and elasticity. Magnetic potentiometers provide
position and force feedback at each joint whereas force sensing
resistors on the palm and fingers provide tactile feedback (seefigure 1 and figure 2). A more detailed description of the hand
can be found in [12].
Fig. 1.

The robotic platform: The Babybot.

III. V ISUAL S YSTEM
model which can be used to control how to reach a point inspace. The robot uses these abilities to build a visual model ofthe objects it happens to grasp. The robot’s ability to interact
with the environment influences the visual attention system;
the visual model of the object grasped by the robot, in fact, isthen used as a top-down primer during the search of graspable
objects.
The rest of the paper is organized as follow. Section II describes the robotic platform. Section III describes the attention
system of the robot, the used model of objects and the method
to extract three-dimensional information about objects. Section
IV describes how the robot acquires its motor skills. Section
V presents an experiment where we show how the modules
described in the paper are integrated in a complete behavioral
system. Finally in Section VI we draw the conclusions.
II. T HE ROBOTIC PLATFORM
The experiments reported in this paper were performedby using an upper torso humanoid robot called Babybot(Figure 1). The Babybot consists of a head, an arm and a hand.
The head has five degrees of freedom, and it is equipped withtwo cameras, two microphones and a set of gyroscopes. The
cameras can pan independently and tilt around a common axis;
the remaining degrees of freedom allows the head to pan andtilt at the level of the neck. The arm is an industrial PUMA
260 manipulator. The hand is mounted on the arm end point.
It consists of a total of 16 degrees of freedom actuated by only
6 motors. Its five fingers are thus largely underactuated: thethumb and index are controlled independently by two motors
each, whereas the remaining two motors are connected to the
middle, ring and small finger which form a single virtual joint.
The coupling between each joint and the motors is achievedby means of springs which give the hand a certain degree

The robot visual system employs log-polar images as in[13]. The log-polar transformation, applied in this case to
the traditional rectangular images coming from the cameras,
mimics the distribution of the photoreceptors in the retina andthe topological mapping from the retina to the primary visualcortex. Log-polar images have a small central area with maximum resolution (fovea) and a continuously decreasing numberof pixels moving toward the periphery. In humans and primates
there is the need to move the sensor to take high resolution
snapshots of important points in the environment. Likewise, inorder to acquire information from the environment, the robot
has to move the cameras and place the fovea at interesting
locations in the visual scene possibly according to the task at
hand. In other words there is the need to have a module that
selects information for further visual processing.
In addition, another important requirement for a visualsystem apt to guide manipulation is that of segmenting objects from a possibly cluttered background. That is, we needboth the localization of the object and its segmentation. The
problem of segmentation is directly related to the problem ofdefining what an object is, that is to define which properties
distinguish an object from the background.
Our definition of “objecthood” is created in two steps:• the selection of a set of visual features that combined
appropriately can characterize any object of a certain set
and allow to segment it from the background;• the selection of a criterion for grouping features thatsegment and identify the objects uniquely;
which means, in practice, that we selected certain features
and a method for deciding when a specific feature belongs
to an object. The features we chose for our implementation
are colored blobs while the criterion is a consequence of
the action of the robot onto objects. In fact, by grasping anobject the robot has the possibility of observing it at will frommany points of view and likely with different backgrounds. It
is consequently easy to imagine a procedure that selects theconstant features across consecutive views of the same object.
By selecting blobs as features we do not propose to define
what is an object directly, but rather to consider a sort of“proto-objects”. They are a step above the mere features (e.g.
edges), possessing some but not all the characteristics of an
object; “proto-objects” in this view are clusters of points on
the image “naturally” grouped together. The idea of protoobjects has its roots in psychological [14] and neurobiologicalliterature. In fact it has been proposed that the synchronizationof visual cortical neurons can be the carrier of the perceptualgrouping phenomenon [15], [16].
In our implementation, we decided that the grouping acted
on color and intensity information and thus the grouping ofelementary feature leads to the extraction of colored blobs as
we mentioned earlier. To simulate the results of the process ofgrouping, we employed the watershed transform (rainfallingvariant) [17] on the edge map resulting from a preliminaryfeature extraction stage. As a consequence the image is segmented in regions of constant color or a constant gradient ofcolor. A segmentation of this type has been demonstrated tohappen in humans before the attention is deployed to the scene[18].
Further, following our definitions, the identity of an object
cannot be known without active manipulation, unless someother prior knowledge is inserted into the system. One possible
route for learning something about an object autonomously is
by means of action. The robot can go beyond the concept ofproto-objects, learning a model of any object that is manipulated. In particular, objects are seen here as a collection of
proto-objects and their spatial relations. Our solution goes by
allowing the robot to manipulate objects and acquire different
views of them, and, using the probabilities of occurrence,calculate the probability the collection of blobs being currently
fixated is one of the objects the robot is searching for. Then,
using these same probabilities, the figure-ground segmentation
can be attempted. The complete description of the visual
attention model and segmentation procedure can be found in[19].
The segmentation mask is used together with a binoculardisparity estimation algorithm which can be used to extract
three-dimensional information about the object. The mask
defines a region of interest around the object, where a depth
map is estimated, and eventually the orientation in space ofthe object can be extracted and used to guide the behavior of
the robot.
In order to achieve a good detection of the object orientation, we developed a fast and robust binocular disparity
estimation algorithm, which can work in real-world conditions.
The algorithm is based on the work by Van Meerbergen et al.
[20], where, given a scanline, all the possible matches between
pixels are analyzed by exploring a graph (using dynamic
programming) built by assigning a cost to each position pairand each occlusion. The algorithm works at nearly frame rate

Fig. 3. A section of the graph: the columns have constant disparity (m− n).

especially when running on a small portion of the image (apair of images).
With respect to the original formulation of the algorithm
we relaxed the hypothesis of similar extension (over the
scanline) of the pixel patterns: something that makes sensewhen the cameras are quasi-parallel, but on the other handfails dramatically when the cameras are converging and theobjects are very close compared to the interocular distance.
The consequences in this last conditions are that a surface canbe very different in shape between the two images of the stereo
pair. As a result of our changes a short sequence of pixels in
one image can match a long one in the other image, which is,
as we said, reasonable in the case of our robot.
Each node of the graph represents a pixel pair, one on theleft scanline (Lx ) and one on the right one (Ry ), starting fromthe nodes containing either (L0 ) or (R0 ) or both (the firstpixels of the scanline) and ending with the nodes containing
LN , RN or both, where N is the length in pixels of thescanline. Each node is connected with all the following nodes
according to these rules:
1) Disparity Range: δmin ≤ x − y ≤ δmax2) Ordening: Given another pair (Lx0 , Ry0 ), if x0 ≥ x theny 0 ≥ y, assuming that there cannot be duplicate nodes
3) Continuity: the nodes following (Lx , Ry ) are all thenodes containing (Lx+1 ) or (Ry+1 ) that respect theprevious two constraints
Each arc has an associated cost:c = |lum(Lx) − lum(Ry )| + kβ + αwhere lum represents the luminance of a pixel, β is a cost(linear) associated to disparity jumps (if a node has disparity
δi and the following one has disparity δj , then k = |δi − δj |)and α takes into account the differences along the vertical dimension (to penalize large discontinuities in the final disparitymap). Once the graph is built, the minimum cost to traverse it
from beginning to end is found using dynamic programming.
The disparity map is then constructed by considering thepairing of pixels along the minimum cost path. An example ofthe construction of the graph when considering two successive
levels is shown in figure 3.
Since the complexity of dynamic programming, for eachgraph (i.e. each scanline), is O(m) – where m is the total

Fig. 5.

Fig. 4. The Disparity Algorithm Left: The Stereo Pair, Right: The Final
Disparity Map, masked. The mask image comes from the attention algorithm.

number of arcs in the graph and it is proportional to thelength of the scanlines – we considered only the portionof the image around the object of interest, segmenting theobject itself by using the information coming from the saliency
algorithm. This reduces both m and the total number of lines
to be processed. To increase the robustness, once computed
the disparity map Dl−r (displacement of the pixels in the left
image compared to the ones in the right one), we used it todetect the object position on the right image, according to theformula:Dl−r (x) = Dr−l (x − Dl−r (x))(1)
This result is then used to segment the object on the rightimage. The images are swapped (left with right) and thedisparity is computed again. This new estimate is finally used
to validate and correct the previous one. An example of amasked depth map is shown in figure 4.
IV. T HE B ODYHumans become skillful at controlling their own body after
a long period of postnatal development and probably thousandsof trials. As we discussed in the introduction, however, motor
development is extremely important and enables the correct
perceptual development of the child. For this reason the robot
spends the first phase of its artificial development learning how
to correctly control the head and the arm to perform various
tasks such as visual tracking and reaching for a visually
identified target.
Control of the body requires implicit knowledge of its
structure (relative position of the limbs, their size, etc.) as wellas its dynamical characteristics (e.g. the weight of the bodysegments). The ensemble of this knowledge is called bodyschema; experiments in neuroscience have given support tothe existence of a body-schema in the primate brain [21], [22].
Graziano and co-workers have found neurons in the motor
cortex of the monkey, which code the position of the hand in
the visual field.

Hand segmentation: an example.

On the other hand, developmental psychologist have been
trying to understand the mechanisms which allow the brain toacquire such a representation. As roboticists we are interested
in the same mechanisms as they allow the system (biological or artificial) to autonomously acquire and maintain allparameters required to the control of action and avoid manualestimation and calibration. For this reason the problem has
been studied by many authors [1], [23], [24].
We follow here an approach similar to the one of [1],[24] where repeated self-generated actions are exploited forlearning. We programmed the robot to perform a periodic
movement of the wrist. This motion is observed by the robot
visually and motorically. In the former case the visual motion
is computed by image differencing with an adaptive modelof the background. In the latter case the robot computes thefirst derivative of the encoder feedback. The period of motionof each pixel in the motion image is compared to that of theencoder. Pixels whose motion is periodic and whose period
matches that of the joints are selected and grouped together toform the segmentation of the hand. Figure 5 shows an exampleof the result of this procedure.
We used this procedure to train a neural network to estimate
the position of the hand in the visual field given the current
robot posture (arm and head joint configuration). Anotherneural network learns the approximate shape and orientationof the hand given the same segmentation information. Figure
6 show the result of this procedure.
The role of vision during reaching is still debated [25],although experimental results suggest that the sight of thehand is not required for children to start reaching for an object[26], [27] and it is used only relatively late in development toactually adjust the trajectory of the hand during action [28].
Sight of the hand, however, might be used to acquire eyehand coordination. By tracking the hand the robot builds
a mapping between the position of the arm and the corresponding head configuration when fixation is achieved. The
hypotheses is that reaching starts by first fixating the object;in this condition the fixation point coincides with the target
and uniquely identifies its position with respect to the body.
The arm motor command can be obtained by a transformation
between the head and arm joint angles, that is by mapping
motor variables into motor variables:
qarm = f (qhead )(2)where qarm and qhead are head and arm posture respectively.

Fig. 6. Learning to localize the hand in the visual field. The convergence of
the approximation error as result of training of the neural network (left) anda few exemplar frames of the subsequent prediction (right).

This mapping implicitly implements the inverse kinematics ofthe arm and it can be learnt when the robot looks at its hand:that is, the robot can maintain the fixation of the hand whilemoving the arm randomly in the workspace. Every time thearm stops and the eyes have achieved a stable fixation of thehand, a new pair of arm-head positions is acquired and used
as a training sample to the neural network approximating the
mapping of equation 2. The robot uses the mapping to reachfor visually identified objects as soon as a few samples are
acquired.
The actual trajectory is computed by linearly interpolating
the motor command and the current joint position. The trajectory results in a set of small changes that are effected by a PD
controller with gravity compensation. A procedure to learn thegravity component is explained in [12].
V. I NTERACTION
We present here a grasping behavior based on the modules
described in the previous sections. The interaction with theenvironment starts when an object is placed in the robot’s
hand; the robot detects the object by using the tactile sensors
on the palm (see figure 7 frame 1). When pressure on thepalm is sensed the fingers close in a stereotyped grasping
action. The intrinsic elasticity of the hand (see section II)
facilitates grasping, because the fingers automatically adapt
to the shape of the object. The robot starts the exploration of
the object by bringing it close to the cameras in four different
positions and orientations (frames 2-3). During the exploration
the robot keeps fixation on the object by tracking the hand.
At each position a few frames are acquired and processed
as explained in section III to train the model of the object.
As the exploration is completed the object is dropped on thetable. The robot exploits now the visual model of the object
to search for it again (meanwhile the object might have been
moved elsewhere by the experimenter) in the visual scene.
The search procedure is driven by a top-down attention
module whose contribution exploits the knowledge the robot
just acquired about the object. In practice, this happens byselecting the blob whose features better match those of theobject’s main blob and performing a saccade motion towards
it. After the saccade the object is in the fovea (frame 4 andFig. 7.

The execution of a grasping experiment.

7) and its model is matched against the blobs that are now
fixated. If the match is positive grasping starts otherwise thesearch continues. The disparity map of the segmented object
is computed to determine the orientation of the object (frames
5 and 8); two different actions are then attempted to maximize
the possibility to successfully grasp the object. If the principal
axis is oriented horizontally the robot moves the hand above
the object, otherwise the hand approaches the object fromthe side (frames 6 and 9). To determine if the grasping is
successful, the robot checks the weight of the object and its“consistence” in the hand (the shape of the fingers around theobject). In case of failure another grasping trial is attempted,
otherwise the robot waits for a new object to be placed in thepalm.
VI. D ISCUSSION
We have shown results on two phases of the acquisition of
sensorimotor coordination in a upper body humanoid robot.
The robot includes a visual attention system employing topdown and bottom-up information. The former is introduced in
the system beforehand, whereas the latter is modulated by the
robot’s interaction with the environment.
We have shown the importance of the interaction between
the environment and the robot for learning. This was demonstrated indirectly, when the robot exploited self-produced
actions to explore its own body, and directly when the robot
actively explored the visual properties of the objects it grasped.
In the experiment discussed here, we start to link different
actions to different objects to investigate the possibility for the
robot to autonomously learn which actions are more suitablefor different contexts (different objects or environment). Although far from completed, this is meant to enlighten us about
the possibility of autonomously enriching the robot’s knowledge of the world. This is not only relevant for action, butalso from a perceptual point of view. Indeed, actions establish
a link between events and the causes that have generated them.
In other words by acting in the world an “active” agent canlink the actions it performs with their consequences. This link
can be used in two ways i) for planning, to select the particularaction required to bring about a desired consequence and ii) forinterpretation, to understand the meaning of an attended event.
In the first case, the advantage to use such a representationis that, sometimes, it might be convenient to express goals inperceptual terms. For example pushing an object in a particular
direction can be represented by means of the resulting visualmotion in the image plane [2]. In the second situation, the only
available information is the sensorial experience associatedwith the event. In this case the robot can search its own
experience for an event that closely matches what is observedand can select the action(s) that generated those perceptual
experience. For example the sound of an object that hits
the floor can be associated with the action of dropping it.
Either problems, planning and interpretation, are interesting
and challenging, and luckily the solution to both appears tobe tightly intertwined with sensorimotor development.
It is fair to say that the system developed so far, althoughcomplex, still manifests a certain degree of brittleness perhaps
associated to the amount of “handcrafted” components wewere nonetheless forced to use to reach this level of functioning in a reasonable amount of time. For instance, the choiceof color blobs as features clearly limits the visual system in a
way that sometimes prevents the robot from perceiving certain
object characteristics. Also, in some other circumstances theresidual error in reaching goes unnoticed to the robot thateventually fails to grasp the object reliably. On the object
recognition side, objects composed of only a few blobs areeasily mistaken for other blobs in the background since asingle color (or certain blob combinations) is clearly notdiscriminative enough (the background might present similar
combinations).
We are aware of many of these limitations and, in fact,
our ongoing work is exactly aimed to improving the overall
performance of the robot both motorically and perceptuallywith a particular emphasis on the manipulation abilities wedeem fundamental for autonomous development.
ACKNOWLEDGMENTS
This work was supported by European Union grants RobotCub (IST-2004-004370) and ADAPT (IST-2001-371173).
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