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According to my Big Word Book, robotics is the study of the technology associated with design, fabrication, theory, and application of robots. A robot is a mechanical device that sometimes resembles a human being and is capable of performing a variety of commands.

So what else is new! We all know about robots, don't we? What's the point of bringing it up? And why here, especially? What's it got to do with simulation?

Yes, well, that's a point, isn't it. I mean, robots are obviously different from simulations and don't seem to have anything in common with them. After all, robots are material things that usually move around in real space. Simulations, on the other hand, are non-material "things" and are only representations of things that move around in space.

In a sense, robots are complements of simulations. This is true for simulations designed as trainers -- the so-called simulators. In a simulator, the trainee is a real person embedded in a non-real environment. But a robot is a non-real "person" embedded in a real environment. An airline pilot, for instance, would perform aircraft maneuvers in a ground version of the aircraft that's "flying" in a simulated air environment. And a robot could fly a real airplane in a real environment. (Automatic pilots?) Or, a trainee might learn to drive in a make-believe car in a make-believe city street, and an automobile-driver robot could be driving a real automobile on a real city street.

What's interesting is that the robot can perform actions (skills?) like a real person, and the robot can be "trained" like the person, even in simulators.  (See neural networks.) Also, you can compare robot and human skills. For instance, mobile robots perform mobile skills, like vacuuming a carpet, though they don't play sports, yet. And stationary or arm robots perform stationary skills, like handling parts on a production line, flying an airplane, or driving a car,

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Both trainees and robots have to interact with their environments, real or not. They interact by using sensors to observe their worlds and motor processes to direct the sensors and navigate through the worlds. Trainees have to use eyes and ears to gather information and perform skills to be applied eventually in the real world. Robots, similarly, have to use mechanized "eyes" and "ears" to "gather information" and perform "skills" in the real world.

Consider the observational and propulsive models that might be used by trainees and robots. Can these pattern recognition devices be the same for humans and robots?

Trainees and robots are made of different stuff: nerves, muscles, and bones, on the one side, and wires and optical-mechanical linkages, on the other, so they can't do things in exactly the same way. I.e., human and robotic procedures can't be the same. But the actions can be functionally the same. (For details of the structure-function characterization, click here.)

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Of special interest is the question whether the observational model for robots can ever be the same as that for humans. Not that it has to be, of course; robots don't have to "see" the way we do in order to be effective. Robotic models can be less powerful than the brain model overall and still do better than the brain in specific situations.

For instance, optical devices could be perfectly adequate for chores such as detecting faults in an automobile part in production, and could be far superior to human perception -- particularly if the measure is in terms of observational preciseness and exposure duration. Human sensors, or even more generally, sensors of living organisms, can't really be lauded for their exactness or their indefatigable nature.

The real value of the brain comes to the fore, not in mechanical or "automatic" processes, but in pattern recognition, particularly of complex objects, relationships, or behavior -- the ordinary, everyday "simple" things of life, like recognizing friends, being able to walk or to cross busy streets, or grasping social relationships.

The question draws attention to the intriguing fact that the human being -- along with other creatures (like dolphins) -- is able to recognize patterns easily in so many environments. The ubiquitous nature of our pattern recognition skills is awesome and highlights the failure of most theories devised to explain it -- the so-called classical theory. Here is what William E. Allman has to say in his book, Apprentices of Wonder, about classical theory.

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