BabyTigers-98: Osaka Legged Robot Team
Noriaki Mitsunaga and Minoru Asada and Chizuko Mishima
Dept. of Adaptive Machine Systems, Osaka University, Suita, Osaka, 565-0871, JapanAbstract. The Osaka Legged Robot Team, BabyTigers-98, attended
the First Sony Legged Robot Competition and Demonstration which washeld at La Cite La Villeta, a science and technology museum in Parisin conjunction with the second Robot Soccer World Cup, RoboCup-98,July, 1998. This article describes our approach to the competition. The
main feature of our system is to apply a direct teaching method to behavior acquisition for four-legged robots as one of the robot learningissues. Since the robots recognize objects in the field by color information, the color calibration is another issue. To cope with color changes
due to lighting conditions, we developed an interactive color calibration
tool with graphic user interface. Based on the color calibration and thelearning by teaching methods, the robot successfully exhibits basic skills
such as ball approaching and kicking.

1Introduction

The Osaka Legged Robot Team, BabyTigers-98, attended the First Sony Legged
Robot Competition and Demonstration which was held at La Cite La Villeta, ascience and technology museum in Paris in conjunction with the Second Robot
Soccer World Cup Competition and Conference, RoboCup-98, July 2-9, 1998.
The final goal of the Legged Robot Project in our group is to establish
the methodology to acquire behaviors for team cooperation in the RoboCup
context from the interactions between the legged robots through multi sensormotor coordinations. The desired behaviors can be categorized into three levels;a basic level, a basic cooperation level, and a higher team cooperation level. In
this article, we briefly explain our first step for the first level skill acquisition
with preliminary results. That is behavior acquisition by direct teaching.
The most fundamental feature of the legged robot is that it moves by its
four legs (12 DOFs), which is quite different from conventional mobile robots (2or 3 DOFs). From a viewpoint of sensor-motor learning and development, multi
modal information and multi DOFs control should be established simultaneously,that is, affecting each other, the sensory information is abstracted and the multijoint motions are well coordinated at the same time [1]. However, it seems very
difficult for artificial systems to develop the both together. Our goal is to design
such a method.
From our experiences on robot learning, we realized that the number of trials
by real robot is limited and a good trade-off between computer simulations andreal robot experiences is essential for good performance. However, the computersimulation of the legged robots seems difficult to build, then we decided to adopt
a direct teaching method in order to reduce the number of trials by real robot.
Since the robot detects objects in the field using color information such as aorange ball, aqua-blue and yellow goals, color calibration is another issue for therobot to robustly detect objects in the field and then to learn by teaching. Since
color calibration during the game seems difficult to realize due to the limitationof on-board computation power, we adopt an off-line color calibration method.
To make the calibration process much easier, we developed an interactive color
calibration tool with graphic user interface.
This article is structured as follows. First, we show an interactive off-line color
calibration system we developed. Next, we describe the method of robot learningby direct teaching. Finally, we show some experimental results and discuss thefuture issues.

2Vision System

2.1

Color detection

In the legged league field [3], 7 colors (aqua-blue, yellow, orange, blue, red, pink,green) are used and robots need to detect all of them. The Sony’s legged robot has
the color detection facility in hardware that can handle up to 8 colors at framerate (60-80[ms]). To detect colors with this facility, we need to specify each color
in terms of subspace in YUV color space. YUV subspace is expressed in a tablecalled Color Detection Table(CDT). In this table, Y are equally separated into
32 level and in each Y level we specify one rectangle (umin i , vmin i ),(umax i , vmax i )(i = 1, ..., 32).
The color observed by the robots dynamically changes due to the robot
and/or object motions. The reason is that it depends on various kinds of parameters such as, the color of object itself, the color of light source, the surface
material of object, the surface orientation of object, the angle between the view
line of the camera and the beam of the light and so on. In order to cope withcolor changes, YUV subspaces should be large enough to involve the changes,
but small not to include different colors. Then we developed an interactive tool
so that we can make and modify CDTs that can handle expansion and reductionof the CDT easily.
Basic procedures are:
1. To make a CDT, we specify multiple pixels that should be treated as thesame color in an image taken by the legged robot. Each pixel has a pair of(y, u, v) value or a point in YUV space. According to the value of y, eachpoint is classified into 32 Y level. In each y level, (umin i , vmin i ),(umax i , vmax i )(i = 1, ..., 32) are set to indicate the bounding box that includes all points
in that y level (see Fig.1(a)).
2. To add a point in YUV to the CDT which should be treated as the samecolor, expand the bounding box of the corresponding y level to include thepoint (see Fig.1(b)).

VVPoints already included
at this Y level.

Points to be included
at this Y level.

Previous bounding box at
this Y level.
New points to be addedat this Y level.
New bounding box at
this Y level.

Bounding box at
this Y level.

OUO(a) Make a boundingbox of the CDT

U(b) Add a point to theCDTFig. 1. Making a CDT first time and expansion of a CDT

3. To delete a point in YUV from the CDT which should not be treated as thesame color, shrink the bounding box of the corresponding y level to exclude
the point. To exclude the point, we have four options (see Fig.2) and we takethe one which is the least reduction of the area (in this case Fig.2(a)).

VVVVPrevious bounding box at
this Y level.
Point to be removed fromthis Y level
One candidate ofnew rectangle.

OU(a)

OU(b)

OU(c)

OU(d)

Fig. 2. Deleting a point from CDT(four options)

We developed a GUI tool written in Tcl/TK to realize the above procedures
easily. The appearance of the tool and emulated color detection is shown in Fig.3.
We show an example of CDT construction with images shown in Fig.4, whichshows ball images observed by the robot from different positions.
First we specify some pixels in Fig.4(a) and make a CDT. The distribution in(a) making color detection table withthe tool(b) emulation of color
detection

Fig. 3. GUI tool to make CDTs

YUV space projected to UV plane (at y level 16) is shown in Fig.5 (the boundingbox is also shown). Distributions in YUV space of the ball color in the picture
Fig.4(a) and (b) are shown in Fig.6. It indicates that the distribution in Fig.4(a)does not cover the one in Fig.4(b). That is, the CDT constructed by Fig.4(a)does not cover the color distribution of the ball in Fig.4(b) (see Figs.8(a) and(b)). Then, we specify some pixels that do not seem to be detected in Fig.4(b)and add to the CDT. Fig.7 shows the expansion of the bounding box in y level
16. After all of addition, the robot can detect Fig.4(b) as Fig.8(c).
2.2

Object detection

Since the legged league field includes multiple objects, some of which have thesame color, and observed images are noisy, object detection is carried out withnoise reduction based on a priori knowledge of the environment.
First, using the color detected image, pixels in the same color are connected
into regions by their 8-neighbors. At the same time, area sizes, centroids andbounding boxes of the regions are calculated. Next, the small regions less than
4 pixels are deleted because the minimum size of the smallest object (ball) in
the image plane is 4. Then, the order of object detection is determined, consid-

(a) ball picture (A)(b) ball picture (B)Fig. 4. Pictures of a ball-0.15
points in y level 16
points other than in y level 16
bounding box of y level 16-0.2

V-0.25

-0.3

-0.35

-0.4

-0.45
0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

U

Fig. 5. Distribution of the ball color (A) in UV spacepoints included in the ball picture A
points included only in the ball picture B
Y
0.8
0.7
0.6
0.5
0.4
0.3
0.2

-0.2
0.2

0.3

U

0.4

0.5

V-0.4

Fig. 6. Distribution of the ball color in YUV space-0.16points in picture (A)old boundary box
points to add
new boundary box

-0.18
-0.2
-0.22

V-0.24
-0.26
-0.28
-0.3
-0.32
-0.34
-0.36
0.3

0.35

0.4

0.45

0.5

0.55

U

Fig. 7. Distribution of the ball color in UV space y level 16(a) ball picture (A):color detection with the
CDT made with theball picture (A)(b) ball picture (B):color detection with theCDT made with theball picture (A)(c) ball picture (B): color detection with theCDT made with theball picture (A) and(B)Fig. 8. Result of color detection with CDTs (emulation by software)

ering the importance of object, easiness of color discrimination and visible constraints of multiple object colors. Constraints means that maximum number ofobjects that can be observed simultaneously is known, orange(1), aqua-blue(3),yellow(3), green(3), pink(3), blue(3) and red(3). This can also be used to noise
reduction. The order we used is as follows.
1. ball
2. poles: (top color)pink(bottom color)aqua-blueaqua-bluepink
pinkyellow
green
pink
pink
green3. goals:
4. robots:aqua-blue goal
yellow goal
blue robots
red robots

The color of robot’s body and head was blue or red, of which saturation was
low and difficult to detect. That is why we put them in low priority in object
detection.

3Behavior Acquisition

In the competition, we assigned the roles of attacker and goal keeper to threerobots, that is, two attackers and one goal keeper. In the following, we explain
how the behaviors for the goal keeper and the attackers are obtained.
3.1

Goal-keeper

We adopted a strategy for the goal keeper to save the goal by positioning itselfin front of the goal. The angles of the visible area is 53 degrees in width and41 degrees in height. It means that the robot cannot see as many goals andlandmark poles to determine its position. The result of color detection is smallin size (88 × 59 pixels), and the poles are small (diameter is 0.1[m] and heightis 0.2[m]). So, the robot rotates its head to enlarge the visible field, and decides
its movement depending on rough estimation of its position. Since the landmark
poles near the opponent goal is difficult for it to observe, the robot compares
the angles between its own goal and four poles near own goal in front of its own
goal, and determines which direction to move. Further, the robot tracks the ballfor a while when it is near the ball.
3.2

Attacker

The behavior of two attackers is obtained by direct teaching. More precisely,
the robot learns its behavior by classifying the data taught by a human trainerfrom a viewpoint of information theory. A human trainer can teach the robot
via serial console with PC.
First, the robot collects test data, pairs of an action command given byhuman trainer and the sensory information during the action execution in every
300ms. Next, C4.5 [2] is applied to the test data to extract rule sets. Then, thevalidity of the rule sets are checked against test data by applying the rule sets.
Specifications of the data for shooting skill acquisition are as follows:– action command forward, backward, left-shift, right-shift, left-rotation,and right rotation (20 degs/sec): these abstracted action commands will bedecomposed into more primitive motor commands in future.

– sensory information head direction (rad.) and image features of both theball and the goal in the observed 88x59 image: area (pixels), position (x-ycoordinates), bounding rectangle (x-y coordinates of corners), height, andwidth. See Figure 9.
– training position and sampling rate: initial positions of direct teachingby serial line connection are evenly distributed in the field heading the goal,
and the sampling rate is 300ms. One trial from the initial position to thegoal takes about 10 seconds.
In the experiments, 740 pairs of an action command and sensory information
for training data to make rule sets, and 500 pairs for test data to check thevalidity of the rule sets are given. The both data are obtained in the same mannerstarting from the similar initial positions, but individual pairs are different fromeach other trial by trial. The number of rules obtained is about 30, and typicalones for forward(F), left-rotation (LR), and right-shift (RS) are shown below.
Figure 9 shows the typical situation of right-shift motion.
Forward: BallArea>56 LeftBallArea>49 Right- BallArea<=40
HeadDir>-.14 Rotation: HeadDir>.52 Shift: BallYcen>8
HeadDir<=.37
HeadDir<=1.10
BallXmin>3
GoalXmin<=11BallWidth<=11
HeadDir<=-.24
GoalArea<=665
GoalXmax<=64Gxmax
HeadDirBxmin(Bxc,Byc)

Fig. 9. Typical situation to take Right-Shift motion

The following table indicates a confusion matrix showing where the missclassification of the training cases occur. Due to the wrong teaching by a humantrainer (actually, she has not got used to teaching), this matrix includes many

miss-classifications, especially LEFT-TURN does not have any correct classifications. We are planning to skill up teaching and also to use more training datato construct robust classification. Generalization is one of the big issue of directteaching, and EBL seems one alternative to solve the problem. These are underinvestigation.
classified as: LEFT
111
14

1

4

LEFT-TURN
13

RIGHT
38
20
3

RIGHT-TURN
216
151
13

FORWARD
49934
91

BACKConclusion

This paper has described the implementation of vision system and strategies
used in BabyTigers-98. Although it was not the best, the resultant behaviors
were encouraging. The followings are future issues.
1. The current color calibration system includes an off-line interactive process,therefore, it cannot cope with dynamic changes in color during the match.
Full autonomous on-line color calibration system should be developed.
2. Direct teaching seems useful under the condition that the robot sensation
can be informed to the human trainer. However, it does not generally true,and often perceptual aliasing happens. As a short range research issue, therobot should reconstruct its state space so that the instructions given by thetrainer can be consistent and effective. As a longer one, both the robot andthe trainer should learn each other, that is, the robot build the trainers modeland vice versa. Through the model developing process, the robot learns how
to behave according to the instructions, and the trainer learns how to teachit.
3. Currently, only the basic skills have been obtained and not the cooperative
ones yet. Direct teaching should be useful for such behavior acquisition. Mutual learning (co-evolution) process is indispensable to obtain the cooperativeand competitive behavior in dynamic multi-agent environment.

References
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2. Quinlan, J. R., “C4.5: Programs for Machine Learning,” The Morgan Kaufmann
Series in Machine Learning, 1993.
3. Veloso, M., Uther, W., Fujita, M., Asada, M. and Kitano, H., “Playing Soccer with
Legged Robot,” Proceedings of the 1998 IEEE/RSJ International Conference onIntelligent Robots and Systems, Vol. 1, pp. 437–442, 1998.
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