THE shelves of Mark Tilden’s lab are lined with hundreds of
robots—robots that walk, robots that roll, robots that crawl suicidally
through minefields, even robots designed to float in space. Most are small
enough to sit on the palm of your hand, and seem almost toy-like. The impression
is reinforced by the green rubber dinosaur and the model of the Star
Wars robot R2-D2 which share shelf space with them.
But inside these tiny machines something intriguing is going on. Using simple
networks of a few transistors, resistors and capacitors, and avoiding
conventional computer processors entirely, Tilden has been able to coax complex,
adaptive behaviours out of his robots. They are brainless wonders.
This approach is at odds with mainstream robotics, which has grown up with
digital technology. Whether it’s a new, custom-built industrial robot or the
latest automaton to display artificial intelligence (AI), most cutting-edge
machines rely on microprocessors.
Advertisement
Tilden, who works at the Los Alamos National Laboratory in New Mexico,
believes this approach is doomed to failure. He calls it the Asimovian approach,
after the science-fiction writer Isaac Asimov, who inspired the first generation
of modern robot designers. It’s based on the idea “I’m a rational being,
therefore I should be able to design another rational being,” he says. The
trouble is that you have to increase processing power exponentially in order to
achieve a linear increase in ability. “To make a robot that makes you toast
costs $20 000. To get that same robot to butter your toast costs an
additional $300 000.”
He speaks from experience. Early on he tried to design a processor-based,
jack-of-all-trades house-cleaning robot. In the end the complexities of
programming the machine got the better of him. “Now I use it as a hat rack,” he
says. So Tilden has gone to the other extreme. His approach is to build cheap,
simple robots that are competent at a single task. If you want another task
performed, you design another simple robot.
In his home he has dozens of robots to do the cleaning. Some of them are
floor cleaners—they constantly skitter around, sweeping the floors and
delivering the dust to a central depot. Each can clean only a little bit, but
since there are several of them, and all they do all day is clean floors, the
floors stay immaculate. Others do nothing but glide up and down his windows,
cleaning as they go. “It’s a balanced robot environment,” he says. “It’s a robot
DZDz.”
A matter of timing
All this and not a microprocessor in sight. So how do these creatures work?
At the heart of them all is what Tilden calls a nervous net—for which he
holds the patent. The net is essentially a ring of artificial neurons made up of
pulse delay circuits. Each circuit consists of a resistor, a capacitor and an
inverter, and stores up charge until it reaches a certain threshold. Then it
fires. By hooking up the output of one neuron to the input of the next, Tilden
can send a timed pulse round the ring that pauses at each neuron in turn before
moving on to the next.
The central controller for, say, a four-legged robot, would take the shape of
a ring of four neurons, one for each leg. Each neuron would in turn connect to a
further four “leg neurons” that command two motors—one to move the leg up
and down, the other to manage motion forwards and backwards. A pulse passes
round the controller, but as each neuron fires it also causes the leg neurons to
fire, moving each leg in turn. For this “creature”, walking is not a carefully
programmed task, but something that simply emerges from the interplay of the
neurons.
Another clever aspect of the nervous net is that, so long as the motors take
their power from the same source as the control pulse, a simple form of feedback
is built into it. When a leg is slowed by an obstacle, the motor draws more
power, leaving less for pulse generation. This automatically slows down the
walking sequence until the leg is free to move again.
Unpredictable critters
In more complex applications, sensors can be added to enhance this feedback
effect. By injecting their own pulses into the ring, these sensors cause the
central control neurons to fire with different timings, modifying how the robot
moves. By properly tuning the timings, a pulse from a sensor which indicates
that the robot has hit an obstacle might make the legs move so they turn the
robot left or right to avoid the obstacle.
As their nervous nets become more complex, the robots’ behaviour becomes
harder to predict. Tilden designs the nets with particular behaviours in mind.
But then he has to tinker with the configuration, finding out through trial and
error which behaviours are produced by which adjustments.
To demonstrate his machines, Tilden puts a dozen in a big, shallow box and
carries them outside to a loading bay. The robots are all solar powered (most
have solar cells scavenged from old pocket calculators), and under the bright
New Mexico sun they perk up. They all have wheels, and their mission is to keep
moving. To do this, they head towards light sources and negotiate obstacles.
Each is surrounded by sensors—wires that form what look like all-round
bumpers—which tell the robot when it has hit an obstacle.
When the obstacle is the side of the box, the robots just keep turning until
they’re clear. But when the obstacle is another moving robot, getting clear can
be more difficult. It’s in these robot scrums that different “personalities”
become apparent from different nervous net configurations. Some robots avoid
fights and move away quickly. Others are more aggressive. They ram other robots,
repeatedly triggering their victims’ sensors and flooding them with a fast and
erratic flow of pulses. Eventually, the victims’ systems overload and freeze up
until a coherent series of pulses starts to flow again.
One robot is so aggressive it is self-destructive. It moves quickly,
banging repeatedly into any other robot it comes across. But it always ends up
overloading itself and stops in confusion. Tilden likens it to a guy in a bar
who doesn’t know when to stop picking fights.
But the nervous net approach is not limited to playing solar bumper cars.
Tilden and his colleagues have proposed launching hundreds of tiny 14-gram robot
satellites into space and using them to measure the magnetosphere, or equipping
each with a small imager and then combining images from the entire swarm to make
a single high-resolution picture
(New Scientist, This Week, 23 March 1996).
To keep themselves oriented correctly, these “satbots” would use
electromagnets connected to a light detector via a simple nervous net. The light
detector would point at the Sun, and if it wandered from this position the robot
would power up the electromagnets to realign it by pushing off the Earth’s
magnetic field.
In the lab, Tilden demonstrates a prototype that consists of three arms
arranged like a camera tripod. At the end of each arm is a coil of copper wire
that serves as an electromagnet. Tilden balances the robot on a stand and brings
a lamp closer. The satellite rocks as it tries to orient itself towards the
light. Gravity defeats it, but in space it would have no trouble.
In a more down-to-Earth application, Tilden has taken some of his walking
robots to the Yuma Proving Grounds in Arizona, to see if they could find and
dispose of landmines and unexploded ordnance. He reasoned that his machines have
a number of advantages for the work. For a start, they can travel across rough
terrain more easily than wheeled devices. The simplicity and robustness of their
circuitry also means his robots could work in conditions of heat, cold, mud or
dust that would defeat more complicated machines. And they are more likely to
survive an explosion. Even if two legs have been blown off, his robots can still
operate, dragging themselves to the next mine. Even if they are flipped over,
they can still crawl along upside down on their “knees”.
Robots don’t have to be geniuses to clear mines. One plan is to use numerous
cheap machines that would simply wander at random, stepping on mines and
exploding them. Or they could be fitted with sensors to detect the mines. They
would then either mark them for later retrieval, pick them up and carry them to
a central site, or simply blow them up.
Tilden says he ran into one unexpected problem. The soldiers who witnessed
the exercise, seeing a crippled robot continue to drag itself suicidally along
looking for more mines, felt sorry for the machine. This is one of the most
fascinating things about Tilden’s machines. His only design criterion is that
they are competent at their given task, yet in carrying out that task they
behave in such complex ways that observers ascribe personalities to
them—even intelligence. But is that justified? “At what point will
competence turn into intelligence? I don’t really care,” Tilden says. “Too many
people are working on intelligence out there.”
Beyond the best brains
The notion that intelligence could arise from such simple machines is not as
crazy as it may seem. It has been championed throughout the 1990s by one of the
gurus of robotics, Rodney Brooks of the Massachusetts Institute of Technology.
He rejected the idea of a central brain, preferring instead to distribute
control to simple components. He showed how complex behaviours arise from the
interaction of those components. In his six-legged robot, for example, each leg
is controlled independently and walking emerges by timing the actions of the
legs. Brooks argues that intelligence also emerges from the way
things—people and robots— interact with the world.
Tilden admits to being heavily influenced by Brooks. But Brooks relies on
digital logic and microprocessors, and this is where Tilden parts company with
him. “When something gets beyond a certain degree of complexity, no mind on the
planet, no amount of work, will get around it,” he says.
Tilden’s analysis of the problems of microprocessor-based robotics is overly
pessimistic, says Illah Nourbakhsh, an AI specialist and roboticist at Carnegie
Mellon University in Pittsburgh. Take Tilden’s assertion that processor-based
robots need exponentially more computing power to achieve linear increases in
ability. This is true in theory, says Nourbakhsh. But in practice robot
designers are getting better at figuring out algorithmic shortcuts, allowing the
robots to make decisions without having to work through near-infinite decision
trees. And advances in computing are also making a big impact, he says. “It’s
definitely the case that many of us in the robotics field do better and better
as computers get better.”
Robot rescue
Up to now, it is mostly processor-based robots that have found practical
uses. Researchers at Carnegie Mellon, for example, sent a processor-based
walking robot, Dante, into an active volcano in Alaska in 1994. On the other
hand, as Tilden points out, Dante eventually slipped and fell, and had to be
rescued by helicopter.
Relying too much on computers has led to undesirable consequences, says
Tilden. Many robots are not truly autonomous but are “puppets”—mechanical
bodies tethered by a data bus to a powerful workstation. Others exist only as
simulations within computers. “Do you know what it’s like to go to a robot
conference and not see one robot?” says Tilden. The real challenge, he argues,
is to make robots that can deal with the chaos of the everyday world. “The
inside of a computer is a perfect world,” he says. “The real world is fractal.
It’s complex. It’s dusty.”
In 1989, in a bid to get more people building real robots, Tilden founded the
BEAM Robot Games (BEAM stands for Biology, Electronics, Aesthetics and
Mechanics). In games held all over the world, contestants come together to see
whose robot can walk, jump, swim or fly the best. Tilden hopes that as more
people try out their own approaches, the robots will evolve into more efficient,
more competent machines.
Because BEAM robots are cheap and relatively easy to make, a large community
of enthusiasts has sprung up. On the Internet dozens of sites offer plans and
tips for building robots out of old calculators and cassette recorders. One of
the best is run by someone who says he’s still in high school.
Yet if Tilden’s robots are so great, why aren’t they being used for real
applications? It’s partly a schizophrenic public attitude to robots in general,
says Tilden. People expect a robot to be a sort of mechanical man, and nervous
net robots are far from that. Some are also afraid of robots—something he
calls “Terminator phobia”. When his own mother visits, he has to box up all of
his house-cleaning robots because they make her nervous. “My mom is convinced my
last words are going to be, ‘No, no! Back!'”
For his robots, there is also one specific limitation. So far, it’s been hard
to get high-level functioning out of his robots. While the nervous nets perform
many surprisingly complex behaviours, they’re all reflex. They will never be
able to learn, or conduct any sort of planning or decision making.
The obvious next step is to add some sort of brain to these
bodies—perhaps a head with a camera for an eye—and let it guide the
action. His analogy is a rider and horse: the rider guides the horse, but the
horse decides where to put its hooves. A conventional microprocessor could serve
as the rider, but you can hear the distaste in Tilden’s voice at the idea.
Besides, he says, it’s been tried and hasn’t worked well: conventional digital
processors don’t mesh well with nervous nets.
Tilden would rather use a neural network for the rider. His idea is to wire
up his artificial neurons in a complicated array that resembles the mesh of
neurons in the human brain. Like a nervous net, it gives complex performance
with relatively simple circuits. Unlike a nervous net, it is capable of changing
over time—it can learn
(see “Cell Wars”, New Scientist, 21 February, p 36).
The nervous net would still be there, controlling the body. But
the neural net would do the processing, figuring out which direction to go and
what task it wanted to accomplish.
One of Tilden’s first walking robots used a neural network. But it didn’t
work. He still has the machine around, and he fetches it. It starts to walk, but
when it runs into a difficult or confusing circumstance—in this case
Tilden pushing it off balance—it quickly becomes discouraged and, oddly,
sits down on its back end as if sulking. The interaction between neural net and
nervous net is too complicated, and tends to break down easily.
Tilden is working on a new design that he thinks will do a better job of
linking the neural and nervous nets. He hopes this will give rise to much more
complex, planned behaviours for his robots.
Nervous chess
By the summer, he hopes to have a robotic “chess set” that can play itself.
The game won’t be played a move at a time, but will be more like a structured
game of bumper cars. The pawns will be simple nervous-net robots battling enemy
pieces for possession of the board’s squares. But the more valuable
pieces—queen, bishops and rooks—will be capable of sensing what is
happening several squares away and planning their moves. The queen will be able
to direct pawns to areas where they are needed and, if she’s in trouble, will be
able to send a distress signal to rally her other pieces.
In the long run, Tilden’s ideas may gain ground. But in the short
term—unless you build one yourself—you might have to wait a long
time to see a nervous-net robot. Today, the idea with the best prospects is the
satbot, which is being funded by the US Defense Advanced Research Projects
Agency. So it could be that Tilden’s robots will soon be more common in space
than they are on the ground.
-
Further reading:
For details of BEAM robotics: the games, forthcoming events
and contacts, see http://sst.lanl.gov/robot/ -
Tilden’s nervous net patent is at
http://www.webconn. com/~mwd/beam/mwt/patent.html