Servo - May 2015

particular motion pattern, and then when in that pattern again tries the previous successful response. If this response is again successful, its confidence level will be incremented to a maximum of five (adjustable). If not, its confidence level will be decremented with a lower limit of zero.

The third level of machine intelligence — Gamma — will try high confidence level (at least three) Beta responses from other motion patterns when in a motion pattern for which it has no memory of a Beta response, before reverting to Alpha. So, with each incremental level of intelligence — Alpha, Beta, and Gamma — the robot learns the motion patterns which maximize displacement sooner and sooner.

When you come up with a good solution, you remember it. When that solution can be applied elsewhere, it’s likely because of the experience you had in the past that you’re going to come up with that solution quicker.

How many projects start slow but once you get the hang of it, speed up? You try random stuff in your head, to which a lot of it you realize is stupid without even doing anything, but you had to run it through your head first. That’s learning!

When you learn something that works or moves you towards your goal, you keep doing it. That is the strategy of little Junior. In Rodney’s world, he looks at the mobility sensor from a Roomba caster with an emitter/detector (Figure 3). He waits a millisecond or two, and then looks again to see if he’s moved. If the sensor has changed from black to white (or vice versa), that means the robot is moving back or forth — our definition of success!

When the robot has success or achieves his goal (in this case, movement), he needs to recall the previous motion code so a facility has been created to remember that number. That is, the robot transfers its current motion pattern (the variable MotionCode) and reassigns that value to another variable, PastMotion

(PastMotion = MotionCode). So, if the newly selected random code is successful, Junior can remember the motion pattern from which it came and increment the confidence level of that response.

The original Beta code (no pun intended) can be downloaded at www.servomagazine.com/index.p hp/magazine/article/february2015 _Peavy.

Try something and observe the effect. If the effect is positive (that is, it moves you towards your goal), the next time you are in that situation and desire the same goal, you simply remember what you did. This is the way we work. So, why can’t robots self-program the same way?

Granted the “goal” here is contrived, but the process is genuine. The trick is to find relevant sensor data to build up a confidence level, memorize successful responses, and finally generalize this information using high confidence Beta responses before reverting to random actions again.

Is this serious? Is it real intelligence? Who knows? Who cares? It’s a real autonomous cybernetic creature. Have fun exploring the questions and as Heiserman says, “…. if someone asks you what your robot does and they don’t buy your answer, it’s their problem.”

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Figure 2. The original 1979 Rodney was an

8085 microprocessor controlled device with an eight-bit data bus, 12-bit address bus, and a load button with which you’d program the robot one byte at a time in binary.