Using Natural Selection to Evolve a Robot Robot Books

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Courtesy of New Scientist Magazine

By Clive Davidson

THE SETTING IS SIMPLE: a rectangular room with its walls painted black except for one white square and one white triangle. In the center of the room, a video camera hangs from a shaft connected to an overhead gantry. Below it is a plastic disc connected to a circle of eight, touch-sensitive bumpers. Motors on the gantry begin to hum, the camera is driven forwards, backwards and from side to side as it explores the room like an animated Cyclops. After a few moments to orient itself, it advances steadily towards the triangle and then comes to a stop. The experiment has been a success.

This "robot" is the brainchild of a three-man team at the Evolutionary Robotics Group at the University of Sussex. The simple task it has successfully completed - finding the white triangle in a landscape populated only by one other feature - does not look like much, as the team members freely confess. But as they explain, "the significant factor is how we got the robot to solve the problem".

Better bred

The Sussex team used a new approach which might eventually succeed where others have failed in building autonomous robots that can move around complex environments. Today, the robot can manage to move around only its blackwalled room: in the future, the goal is a robot that can handle the ever-changing, real-world environments of offices, warehouses and hospitals.

The Sussex robot was not programmed to distinguish between a triangle and a square. Its primitive nervous system was not even wired up so it would pick out geometric objects. Instead, the robot's brain was made to "evolve". It was "bred" from a population of robots with randomly designed motor control and vision systems. After being made to perform in the experimental environment, the "fittest" individuals in the population - in this case, the ones which came closest to stopping in front of the triangle - were allowed to "survive", "breed" and "spawn" the next generation. From a starting population of 30 robots, it took just 30 generations of artificial evolution over two days before the successful triangle-seeking individual appeared.

This method of creating intelligent robots differs greatly from the way robot-builders have proceeded in the past. The traditional artificial intelligence (Al) approach has attempted to program a robot with sets of rules and knowledge about the world in which it lives. This places a tremendous burden on the programmer to reduce everything that the robot needs to know into a set of nonambiguous, utterly precise statement.

A more recent approach has used neural networks - computer systems modeled loosely on the human brain. The programmer does not write a series of instructions for the network to carry out. Instead, the machine learns by trial and error to carry out the correct actions when given certain inputs. During the learning process, the properties of the network are gradually adjusted until a particular input produces the right output. This "connectionist" approach has been successful in creating machines that can solve certain specific problems and has the advantage that the programmer does not have to specify exactly how a problem is to be solved.

But the problem with both these approaches is that they are difficult to apply to a wide range of situations. Each set of rules or learned connections relate only to specific problems. Building robots that can deal with the complexities of the ever-changing, real world is thus very difficult. As the Sussex team puts it, "If a robot is interesting, then it's probably too difficult to design." That is why they have turned to "evolving" their software.

Inman Harvey, Phil Husbands and Dave Cliff are using genetic algorithms as the basic tool for building their robots. This technique first invented by researcher John Holland in the 1960s while he was working at the University of Michigan, loosely applies the principles of Darwinian evolution to computer code. This approach sees strings of code which specify certain structures or behaviors - for example, the way visual sensors on the robot's body connect to one another - as analogous to chromosomes.

At the start of the process, a number of these chromosomes are generated randomly. The robots that carry them will "behave" in a variety of different ways. A "fitness test" is then used to evaluate how well they solve a particular problem, in this case finding the way to the white triangle in a black-walled room. Code-bearing chromosomes from the fittest pair of robots are brought together and run through programs which simulate the recombination of genetic elements that takes place during reproduction by real organisms. The resulting pool of "offspring" is put through the fitness test once again - and so on for generation after generation.

Cyclops' Eye

The Sussex group did not, of course, start right at the beginning with the electronic equivalent of the primordial slime. They began with a species of robot which can move and see in a rudimentary way. Their robot Cyclops doesn't even have wheels or legs - it consists of just a video camera eye suspended from a gantry over the scene of the experiment and connected to a computer. Stepper motors allow the gantry to move horizontally and vertically and move the eye from place to place. Around the eye is a set of eight bumpers. These tell the robot if it collides with an object and where the object is.

This "virtual robot", hanging from the ceiling may seem far from the kind of robot that could move around on its own. But it isn't really: it is just simpler to work with in the laboratory. If the team wanted to take the robot out into the real world, it would be easy enough to use the commands that control the gantry motors to control motors on real wheels.

The process of evolutionary selection is carried out on the controller - the computer that processes information from the sensors and controls the motors which move the robot about. Input from the video camera is first processed to simulate a group of simple photoreceptors: that is, the video image, which covers a wide field, is divided into a set of smaller patches. Each patch simulates a simple eye that just looks in one direction and detects light or dark. The actual number and position of these simple eyes varies from individual to individual.

Once the virtual robot was built and the laboratory setting was complete - a job that took over a year - evolution could take place at high speed. In just two days, the robot controllers were run through 30 generations. During that short time the robot brain evolved to the point where it could easily guide the robot to the white triangle.

The Sussex team were pleased with such rapid success, particularly because other researchers had thought the task might prove impossible. Before they pulled it off, Rodney Brooks, a roboticist at the Massachusetts Institute of Technology had dismissed the notion, believing that it would be too difficult to test the results of artificial evolution on physical robots. "The number of trials needed to test individuals precludes using physical robots for testing the bulk of the control programs produced for them by genetic means," he wrote in a paper given at the First European Conference on Artificial Life in Paris in 1991. But now Brooks says of the Sussex researchers: "They found ways around all of the things I said were too hard." He is incorporating some of the British ideas into Cog, Brooks' attempt to build a childlike humanoid.

The Sussex team were also agreeably surprised when they turned to study the design of the robot which emerged after evolution had done its work. Its control system consists of computer code which created 11 artificial neurons: three for visual input, five connection neurons, and three for motor outputs. The design of its visual system was even more unexpected. Two of the robot's visual sensors were positioned in the center of the robot's field of view, largely overlapping. The third was placed higher up the robot, but more or less directly above the first two. The robot identified the triangle by seeking an area where its two lower overlapping sensors detected brightness - the lower part of the triangle - while its upper sensor detected only the darkness of the walls outside the tapering shape of the triangle.

The real challenge now is to see whether the robots can evolve considerably farther. If you compare the visual task they solved with computers that can find and read car numberplates from motorway video camera images, or measure and count particles in microscope images, they are still at a primitive stage.

The team is currently moving the robots one step up the evolutionary ladder by getting them to find the same triangle when the scene is lit by spotlights, flashing off and on like disco lights. If the robot can evolve to cope with this changing world, then it may move on to bigger things. Step by step, it could reach the stage where it could deal with the more complex shifting scenes inside an office, warehouse or hospital. New skills will have to be added by the experimenters along the way if the robot is to be autonomous. It will need a memory, for example. That will enable it to find its way back to wherever it can recharge its batteries.

After a few million generations of robotic evolution, who knows, the lumbering little robot of today might be a handy hospital porter - or even Robocop.

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