快猫短视频

Santa Fe

AS THE yellow player intercepts the orange ball cannoning off the board that
surrounds the playing field, the blue goalkeeper moves out to cut down the
attacker鈥檚 angle on goal. But instead of shooting, the forward passes the ball
to a team-mate streaking up the left side of the field, who darts around the
sluggish goalkeeper and pops the ball into the net. Score one for the yellow
team from Newton Labs in Seattle, in a crushing 20-0 defeat of the blue SOTY
team from South Korea in last November鈥檚 final of the first ever Microrobot
Soccer Tournament. In just over a month, the teams will meet once again to try
to knock Newton Labs off the winner鈥檚 rostrum.

The MIROSOT, as it鈥檚 known, is the brainchild of Jong-Hwan Kim, his
colleagues and students at the Korea Advanced Institute of Science and
Technology (KAIST) in Taejon, South Korea. They hope football will become a
touchstone, stimulating the robot builder鈥檚 art in much the same way that chess
motivates artificial intelligence research. Robot football makes heavy demands
in all the key areas of robot technology: mechanics, sensors and intelligence.
And it does so in a competitive setting that people around the world can
understand and enjoy. The hope, of course, is that by discovering how to get a
robot to move with agility, see with acuity, and think perceptively in the
limited context of a football game, it will be possible to use the same
techniques to build robots to carry out other, more useful tasks.

Pitch battle

Here the analogy with chess-playing and AI starts to break down, since in
several decades of trying, there鈥檚 not much evidence that the methods developed
to allow computers to play chess to grandmaster level have any use beyond the
edge of a chessboard. But the robot builders of the MIROSOT believe firmly that
their case is different, probably because much of the challenge of robot
football centres on the hardware issues of mechanical motion and vision
processing rather than the pure information-processing problems that
characterise chess programs.

November鈥檚 tournament brought together 23 teams from nine countries on the
grounds of KAIST, where they competed for the Hanminjok Cup. The same basic
rules will apply for the second tournament, this June. Each team consists of
just three robots. And each player鈥檚 mechanics and 鈥渂rain鈥� must be packed into a
cube no larger than 7.5 centimetres on a side. Unlike a real football pitch, the
130-centimetre by 90-centimetre playing field is bounded on all sides like an
ice-hockey rink to prevent the ball鈥攁n orange-painted golf ball鈥攆rom
going out of play.

The teams gather information about the location of the ball and players from
TV cameras suspended above the pitch. That information goes first to an
off-pitch controller and then by radio to the robots themselves. And here is
where the first big difference appeared between the teams鈥攖hey vested
differing degrees of autonomy in the robots themselves. All but two groups of
researchers transmitted just positional information to their robots, allowing
the machines to find their own way to the ball. These robots carried on-board
sensors to help them avoid collisions with other machines.

Brainless

One of the two exceptions was the Newton Labs team, which went for a more
centralised approach. Its robots were 鈥渂rainless鈥�, and the offpitch controller
dictated their behaviour totally, telling them where to move and at what speed.
These robots had no collision-avoidance sensors. So the competition did not
simply set like against like: there was a 鈥済enerational conflict鈥� at work as
well, between the old-style centrally planned strategies, and the beginnings of
the more ambitious strategy of allowing robots to make their own decisions.

My own involvement with the MIROSOT stemmed from a chance encounter early
last year in Japan with Kim at a meeting on artificial life and robotics. I had
just given a talk on some football simulations that I鈥檇 carried out on Super
Bowl XXIX, the championship event that capped the 1994-95 season in the American
National Football League. These experiments consisted of playing the game
between the San Francisco 49ers and the San Diego Chargers 100 times in my
computer, using a program that rates all the players in the NFL by their
individual playing characteristics. My goal was to see if the odds quoted by the
Las Vegas bookmakers for the game bore any resemblance to statistical reality as
calculated by my computer*.

For me, the interesting theoretical point of this experiment is that football
is a classic example of what is termed a complex, adaptive system. These are
systems composed of a medium-sized number (up to a few hundred thousand) of
鈥渁gents鈥� that are intelligent and adaptive. This means they take actions based
on rules, and are adaptive in that they can change the rules or invent new ones
if they see that the old ones are not working. Furthermore, the agents must make
choices on the basis of incomplete information about what others in the system
are doing. Other good examples of such systems are financial markets, immune
systems and road-traffic networks, in which the agents are traders, molecules
and drivers respectively.

A number of researchers have studied the behaviour of these systems, mostly
in the virtual reality of computer memory (鈥淲hat if鈥�, 快猫短视频, 13
July 1996, p 36). Robot designers have a unique opportunity to go a step further
and build physical agents capable of interacting and adapting. Indeed, creating
groups of cooperating automatons is a new and growing area of research in
robotics. During my presentation in Japan I said there was no better test
problem for a robot builder鈥檚 tentative theory of a working complex, adaptive
system than a football game.

It was after this that Kim told me about the MIROSOT. My first reaction was
to bemoan the fact that I didn鈥檛 build robots. 鈥淣o problem,鈥� he replied. 鈥淵ou
can come and be one of the referees and make sure none of those damned robots
cheat.鈥� How could I refuse? So in early November, I packed my striped shirt, a
set of red and yellow cards, and a whistle, and an electromagnetic zapper for
frying the brains of any cheating robot, and headed for Taejon.

Piggybacked on the sporting competition was a symposium replete with
technical discussions of the approaches employed by competing teams to get their
robots to play a decent game of soccer. These problems centred on three main
areas. First, there are the purely mechanical problems, such as how to move the
robots about the field and get them to change direction quickly as the ball
rolls around. On top of that come the 鈥渟ensory鈥� problems concerned primarily
with seeing the ball and the other players. And finally there are strategic
problems associated with processing information about the game and designing
sensible strategies and tactics to win.

At this early stage in the development of microrobot football, researchers
have yet to work out the best answers to these problems. This in itself leads to
another difficulty: how should the teams combine their suboptimal solutions to
each problem in order to produce the best overall result? And all this within
the confines of a 7.5-centimetre cube. Newton Labs鈥� centralised approach used a
very sophisticated visual system and a rather primitive playing strategy (see
鈥淢aradona, eat your heart out鈥�). So the Seattle researchers chose to devote most
of the space in their robots to mechanical motion. Others, such as the SOTY team
from KAIST, tried to compensate for inferior visual systems by implementing more
elaborate playing strategies, through algorithms telling their players how to
react in different situations. These bigger brains, however, forced the KAIST
team to compromise on physical attributes such as speed.

One difficulty confronting all the teams was what one might term the 鈥減overty
of power problem鈥�. The power needed to operate the robot鈥檚 mechanics is
considerable, and each robot has to carry its own power source. Existing battery
technology being what it is, this limited the length of each half of a MIROSOT
game to five minutes.

These technological limitations make refereeing a robot soccer game an
experience in both humour and frustration. The robots鈥� vision systems are often
swamped by information and liable to be confused by certain colours, leading to
players pushing the ball into their own goal. At other times, goalkeepers stand
as motionless as the Sphinx while the ball slowly rolls past them into the net.
And if the referee were to follow the rules to the letter, the number of free
kicks for off-side and penalties for fouling an opponent by running into them
would slow play to a glacial crawl. A certain flexibility in interpreting the
rules is needed to keep things moving.

So what kind of solutions to the technical problems separated the winners
from the losers? The answer is as simple to state as it is difficult to
implement: emphasise speed and vision at the expense of brainpower. How
appropriate! Here is one place where robot soccer makes contact with its
real-life counterpart. It was clear from the demo game that opened the
tournament that the Newton Labs robots, with their superior vision and speed,
would run off with the cup. In their five games, the Newton robots won 12-3,
13-0, 15-1, 19-0 and 20-0. By comparison, no other team scored more than eight
goals in any game.

Laughable state

On the face of it, the Newton Labs victory does little to push forward the
development of autonomous agents, which is one of the underlying ambitions of
the MIROSOT organisers. The Seattle robots relied on a single brain that had
鈥渃omplete鈥� information about the state of play. By comparison, most losing teams
opted to let their robots play some part in decision-making based on 鈥渓ocal鈥�
information. And no robot in the competition appeared to be able to adapt its
tactics on the basis of what it had learnt during a game. So while the teams may
have been complex, they cannot yet be called complex, adaptive systems.

For the present, then, robot football appears to be in the laughable state
that computer chess was in during the 1950s. But no one is laughing at
chess-playing programs today, not even world champion Garry Kasparov, who was
given the scare of his life in a recent tournament with the reigning computer
champion. Whether a similar scenario will unfold with computer football is
debatable. No one expects robots ever to take the field against human
footballers. But at least the skills shown by the teams participating in MIROSOT
showed great potential for much more refined play.

During a discussion at the MIROSOT, the idea was raised that it might be
interesting to introduce a new class of play, in which human opponents control
one of the competing robot teams by using, say, a joystick, Nintendo-style. This
would introduce a component of human-machine competition that would add spice to
the tournament and further motivate the robot builders. In any case, everyone at
the MIROSOT went home enthused about the whole idea of robot football, vowing
not to let the Newton Labs team win in June. A new life form鈥攖he
microrobot footballer鈥攈as been created. The next MIROSOT should give us a
better indications about the evolutionary pathway this creature will follow.

* * *

Maradona, eat your heart out

BUILDING a machine with world-beating football skills into a 7.5-centimetre
cube takes a good deal of ingenuity. And the team from Newton Labs in Seattle
proved to be masters of improvisation (see
Diagram). The two Canon motors
that drive each robot should probably have been whirring inside someone鈥檚
photocopier. Salvaged from a surplus store, they were too big to sit opposite
one another without breaking the size constraints. So they had to be offset, and
connected to the wheels by a drive chain the team concocted from bits of
Lego.

With wheels mounted on the sides, each robot needed casters front and back
to stop it tipping. But the team鈥擜nne Wright, Randy Sargent, Carl Witty
and Bill Bailey鈥攃ould not find wheels small enough. They settled instead
for beating bits of drinks cans into sliders. Finding a small radio receiver
also proved a problem until Sargent found a pair of wireless headphones in
the local branch of Radio Shack. The receiver electronics fitted neatly on
each robot, while the transmitter was connected to the off-pitch controller.

No corners were cut, however, with the vision system, which tracks coloured
blobs in successive video frames and can identify their shapes. During the
matches, a video camera viewed the pitch from above. To this the team linked its
vision system, which told the master controller the whereabouts of the orange
ball, together with the position and orientation of the Newton
robots鈥攚hich had yellow rectangles on their lids鈥�60 times a second.
The team鈥檚 nearest rival could refresh its gaze only 10 times a second.

This system was a key to the team鈥檚 success. The controller continually
predicted the position of the ball and ran iterative calculations to decide the
best tactics for the two forwards. It calculated where the ball would be in,
say, a second鈥檚 time and then asked whether either robot could reach that
position and score a goal. If the answer was no, it moved on to calculate the
position two seconds ahead, and so on. 鈥淲e would run several hundred of these
simulations 60 times a second,鈥� says Sargent.

Eventually, the controller would radio to one of the forwards, telling it how
fast to run its motors in order to hit the ball into the net directly, or off
the side board. 鈥淲e bother about the angle at which we hit the ball, but don鈥檛
care too much about the velocity other than it should be moving as fast as
possible,鈥� says Wright. 鈥淲e like to keep the ball moving quickly so that nobody
else can deal with it.鈥�

Although the Newton robots had fast reactions, their aim was not always true.
Their jerky movements introduced errors that sometimes sent the ball rebounding
off the backboard. Yet on occasion, even this looked deliberate. 鈥淚f you look at
footage of the games, you鈥檇 swear they were passing. But it wasn鈥檛 intended,鈥�
says Sargent. It emerged simply because 1/60th of a second after the first
forward shot at goal, the controller was planning an intercept course for its
team-mate.

The Newton robots had only a few other rules. A forward not playing the ball
was ordered to retreat between the ball and its own goal. The forwards were also
subject to two programs that acted as 鈥渞epelling forces鈥�. One strong,
short-range force kept them away from other robots and the sides, while a weak,
long-range force kept each forward as far from its partner as possible.

The third robot, the goalkeeper, was directed by a simple program that kept
it moving from side to side, in line with the ball. The only change to this was
if the controller calculated that the ball would go near the goal, in which case
the keeper moved sideways to intercept it. 鈥淥ther teams had more complex
strategies,鈥� says Sargent. 鈥淪ome would move forward and back and go to meet the
ball. They didn鈥檛 seem to be so effective.鈥�