Putting It to
the Test: The Robot's First Exposure to the World:
What should happen when we place a baby robot in the world for the first time?
After thinking about it, I'm thinking that many of the connections which we are so obvious that we take them utterly for granted must be part of extensive and intensive early learning on the part of an infant. For example, there is the concept of "solid", and the relationship between open space and our ability to move through it freely, versus objects through which we can't move freely.
Presented with a meaningless blur, how can the robot learn the concept of "objects"?
The first thing the robot's brain will have to do will be to generate a 3-D map. It will do this by by triangulating on the features which it has detected and has registered frame by frame as it moves around. This "restlessness" will be pre-programmed.
The robot will be attracted toward the most "interesting" featuresfeatures which move, features which make sounds, features which are brightly colored. This kinetropism, phonotropism, and chromotropism will be instinctivethat is, pre-programmed. Also, the robot will be excited and experiencing what in a living organism would be pleasure. Its clock rate will be up, its self-appraisal will be positive (whatever we can make that mean), and it will be totally absorbed in its exploration. The infant robot will have no emotional defensesno parent part, no ability to repress, and no real ego. Sensory inputs will be at full volumeuninhibited. Everything will be recorded, set up in unique categories, cross-correlated (at first only on the basis of temporal proximity), and stored at full detail (see below for a discussion of "full detail"). At this stage, it will be driven by pre-programmed urges. It will be drawn to the first, most colorful feature it sees. Here, we encounter the first disconnect. How does the computer establish a connection between desire and motor output? I am envisioning a platform with four-parallel-wheel steering so that it can move in any direction it pleases. One could make it flail around, like an infant, until it learns to coordinate its motor activity. However, motor activity itself must be associated with desire to move in certain ways. It may be that this instinct may have to be pre-programmed at least the first time we try it.
It will try to grasp the feature, if the feature is reachable. Here again, it must somehow have learnedto coordinate its manipulators so that it can reach and grasp objectsbefore it can grasp anything. Once the robot has examined a reachable, handle-able feature/objectfelt its weight and its shape, squeezed it, banged it to see what noise it makes, heard its name, (if we want to give it names even at the outset) and examined it on all sidesthen, for its first level of understanding about the world, it will consider the feature known andwill lose interest in it, moving on to the next feature. It will store a tactile map together with its visual imagery. In the animal kingdom, for obvious reasons, a short attention span prevails among the young of all species so a short attention span will be pre-programmed into our baby robot. (It will grow out of this when it grows up.) If the feature is not "handle-able", then the robot will examine the feature from all available directions and move on. If the feature changes, either while it is conducting its exploration or before it sees the feature/object again, then it will re-examine the object. If the object changes during its first examination, it will tentatively associate the change with whatever else is going on at the same time. If the same sequence of events occurs repeatedly or simultaneously, then the robot's little mind will establish a causal link between these events and the change in the object for potential subsequent prediction of this change. The robotic mind will constantly be seeking associations between events and/or objects, and at the same time, it will be correcting or refining these correlations, given inconsistent sequences of events. This will permit it to predict a change in the object, given the beginning of the associated sequence.
If the object is unchanging, then each successive time the robot encounters the object, it will spend less time examining the object than it has before. (Repetitive reinforcement is very important and comforting to infants and may have to do with wicked surprises in the world.)
The learning of general concepts and strategies must be an important part of the initial exploration of the world. Concepts like gravity must to be learned, and must present as unwelcome surprises. (Once a few objects have fallen, an anticipatory cause-effect pattern from remembered sequences should become established. "What I tell you three times is true." I do this, that happens; I do this, that happens.)
When the robot lets go of something, it will fall to the floor. The noise should startle the robot (and make an indelible impression). The robot should then interrupt its environment-exploration program and pick up the object because it did the unexpected. The robot would probably re-examine the object, see nothing unusual, and lose interest, dropping it again. Once again, it would pick up the object, examine it cursorily (shortening the sequence), and drop it again. The next (fourth) time, it should more or less eliminate the examination and should start picking up the object and dropping it until it becomes evident that it is the letting go of the object that triggers its fall. An anticipatory sequence would have been set up, with gradual or rapid elimination of the examination until only the essence of a cause-effect relationship would be left. If it were voiced, it might be "I pick up the object; I let go of it; it falls down and goes boom!" However, the first time the robot picked up the object, it picked the object up off the table, so picking it up off the floor is not a common element. The object won't fall until the robot releases it, so the releasing of it must trigger the fall. Not only that but the changing-of location-and-the-noise occurs just after the moment when the robot releases the object. So releasing the object becomes the only common denominator and therefore the probable cause of the change and the noise. (The robot's vision system may track the object by increasing the update rate to 30 frames a second and the resolution to either one or six minutes of arc the instant it detects that the object is rapidly changing location. (The object will move 0.21" then 0.85", then 1.92", then 3.41", then 5.3", then 7.7", then 10.45", then 13.65", and then 17.28", appearing 8 to 9 frames.) The robot would repeat the lifting and dropping until, after a few trials, it assumed the cause/effect relationship (in the form of a generic anticipatory sequence). When the robot has established the expectation that the object will fall when the robot releases it, the robot will lose interest in the object. Note the inferences which have been set up in the form of the anticipatory sequence after three or four experiments.
A lower-priority, unsolved puzzle would remain for the robot regarding why the object hadn't already fallen on the floor but instead, was sitting on the table when the robot picked it up. What happens next is negotiable. The robot could defer solving that puzzle and resume its exploration of its environment. Or it could follow up, by putting the object back on the table and observing that the object didn't fall. An adult could lift the object and put it back on the table and then the robot could try to imitate it. But how will the robot come up with imitation? It must first "objectify" its environment. One way to carry out imitation would be to be to put the robot through the motions, which it could then replay, as opposed to expecting it to try to imitate someone else. Another way might be through the parallel between the robot's recognizing its manipulator and recognizing someone else's. We might want to begin by using a manipulator that looked just like its own. It could lift the object off the table and drop it on the floor. It would do this several times until it established the consistency of the results. Then it could play with the object, dropping it from different heighths and noting the time it took to fall and the varying loudness with which it hit the floor. It could lift the object to varying heighths and drop it on the table. It could slide the object around on the table. It could slide the object over the edge of the table and discover that the object dropped without the action of being held and then released. In other words, it would begin to discover that falling is a behavior of unsupported objects and not of its releasing the object. One possibility might be to try to install an expert system for learning and discovery.