Posts Tagged ‘conditioned reflex’

1979 – “Rodney” Self-Programming Robot – David L. Heiserman (American)


"Rodney", the Self-Programming Robot is based on the book How to Build Your Own Self-Programming Robot by David L. Heiserman [TAB, 1979].


ByRamiro Molinaon September 18, 2013
 This book is geared towards those that have good knowledge of electronics and are willing to jump into a project that involves CPU based control. It outlines how to build a wheeled robot controlled by an Intel 8085 CPU, programmed by hand in binary using an array of switches that bumbles around a room on its own.


ByBenjamin Graylandon November 26, 2000
If you have an interest in robotics, and a decent knowledge of electronics, then this book is certainly worth reading. Despite its age, the information it provides is applicable today.
Heiserman tells of his own robots, specifically Rodney, who can program himself. One example given was of Heiserman handicapping Rodney, by scratching his processors and removing one of his wheels – Rodney learned to move about efficiently in a short period of time, with no assistance. Similar anecdotes are spread throughout the book.
But most importantly, the book tells the reader how they can construct a robot similar to (or exactly the same as) Rodney. Schematics, wiring diagrams and so forth fill a large portion of the book – providing a clear method for construction.
Overall, this is certainly an interesting book. Even if you don't plan to build yourself a robot, the anecdotes are both entertaining and amazing enough alone.


Classes Of Robotic Self-Learning. Source: here.

It is useful to define intelligence as in robotics according to David L. Heiserman 1979 in regards to the self-learning autonomous robot, for convenience here called "Rodney".

    While Alpha Rodney does exhibit some interesting behavioral characteristics, one really has to stretch the definition of intelligence to make it fit an Alpha-Class machine. The Intelligence is there, of course, but it operates on such a primitive level that little of significance comes from it. ….the essence of an Alpha-Class machine is its purely reflexive and, for the most part, random behavior. Alpha Rodney will behave much as a little one-cell creature that struggles to survive in its drop-of-water world. The machine will blunder around the room, working its way out of menacing tight spots, and hoping to stumble, quite accidentally, into the battery charger.

    In summary, an Alpha-Class machine is highly adaptive to changes in its environment. It displays a rather flat and low learning curve, but there is virtually no change in the curve when the environment is altered.

    (2) BETA CLASS

    A Beta-Class machine uses the Alpha-Class mechanisms, but extends them to include some memory – memory of responses that worked successfully in the past.

    The main-memory system is something quite different from the program memory you have been using. The program memory is the storage place for Rodney’s basic operating programs-programs that are somewhat analogous to intuition or the subconscious in higher-level animals. The main memory is the seat of Rodney’s knowledge and, in the case of Beta-Class machines, this means knowledge that is grained only by direct experience with the environment. A Beta-Class machine still relies on Alpha-like random responses in the early going but after experiencing some life and problem solving, knowledge in the main memory becomes dominant over the more primitive Alpha-Class reflex actions.

    A Beta-Class machine demonstrates a rising learning curve that eventually passes the scoring level of the best Alpha-Class machine. If the environment is static, the score eventually rises toward perfection. Change the environment, however, and a Beta-Class machine suffers for a while, the learning curve drops down to the chance level. However, the learning curve gradually rises toward perfection as the Beta-Class machine establishes a new pattern of behavior. Its adaptive process requires some time and experience to show itself, but the end result is a more efficient machine.


    A Gamma-Class robot includes the reflex and memory features of the two lower-order machines, but it also has the ability to generalize whatever it learns through direct experience. Once a Gamma-Class robot meets and solves a particular problem, it not only remembers the solution, but generalizes that solution into a variety of similar situations not yet encountered. Such a robot need not encounter every possible situation before discovering what it is suppose to do; rather, it generalizes its first-hand responses, thereby making it possible to deal with the unexpected elements of its life more effectively.

    A Gamma-Class machine is less upset by changes and recovers faster than the Beta-Class mechanism. This is due to its ability to anticipate changes.

Robotics: Robot Intelligence: An Interview With A Pioneer
Posted here on 2008-06-06 @ 19:28:20 by r00t.


A short and informal email interview with a pioneer in the field of hobbyist robotics, David L. Heiserman.

Mr. Heiserman is the author of six volumes on the subject, published by TAB Books over a span of 11 years, from 1976 to 1987. These books describe, in detail, several robotics and simulation projects he developed during those years. Each was written and designed in such a manner as to allow the reader the ability to follow along and construct each project themselves.

However, the books aren't plans so much as they are guides. They form a complete encyclopedia for a compelling subject of study, which Mr. Heiserman has termed "Robot Intelligence" and/or "Machine Intelligence":

Build Your Own Working Robot – #841 (ISBN 0-8306-6841-1), HB, © 1976
How to Build Your Own Self-Programming Robot – #1241, (ISBN 0-8306-9760-8), HB, © 1979
Robot Intelligence…with experiments – #1191, (ISBN 0-8306-9685-7), HB, © 1981
How to Design & Build Your Own Custom Robot – #1341, (ISBN 0-8306-9629-6), HB, © 1981
Projects in Machine Intelligence For Your Home Computer – #1391, (ISBN 0-8306-0057-4), HB, © 1982
Build Your Own Working Robot – The Second Generation – #2781, (ISBN 0-8306-1181-9), HB, © 1987

I first read these books as a boy in grade school, and continued to study them periodically through high school. As an adult (now almost 35 years old – where did the time go?), I collected the set for my library. Along the way, I wondered what Mr. Heiserman did with his robots, and whether he planned on publishing anything more about them or his experiments. This interview and other email conversations with him have helped to answer these  questions.

PG: What, and/or who, inspired you to pursue the research of machine intelligence?

DH: I saw the robots in sci-fi films of the 50s and 60s, and I wondered how it would be possible to build one.

PG: Was Buster the initial platform for your research, or were there prior (but unpublished) platforms and/or systems you used prior to Buster?

DH: There was a prior version in 1963. I can't remember the name, but it was strictly radio controlled — vacuum tubes, no less.

PG: During the period your books on robotics and machine intelligence were published, TAB Books seemed to provide a haven for similar authors. Did they provide or do anything special to encourage this?

DH: No.

PG: Were you ever in contact with any of the other robotics experimenters (published by TAB or otherwise) during the period your books were published?

DH: No.

PG: Rodney seemed to anticipate the experiments carried out in "Robot Intelligence" and "Machine Intelligence". Were these projects inter-related?

DH: The books are pretty much a technology-based sequence. I had no idea about doing machine intelligence when I did the book on Buster.

PG: Did you ever bring together the software concepts developed in "Robot Intelligence" and "Machine Intelligence" with an actual hardware platform, or did you view the software environments you created as a better avenue for development of your ideas on machine intelligence?

DH: "Projects" was an attempt at hardware implementation, but I was more interested in computer simulations by this time. I never published my work for several weak reasons; one of which was that I was beginning to catch so much nasty flack from the amateur and quasi-professional AI community. I won't go into all of that, but let's just say I am enjoying some quiet satisfaction today.

PG: Why was the decision made to create the second generation Buster as a "hard-coded" robot, rather than continue with programmable machines as represented by the earlier Rodney?

DH: Well, I think it was because I was losing a segment of people who were not sophisticated enough to do any programming.

PG: What are the major differences between Buster as described in the original "Build Your Own Working Robot", and the Buster described in "Build Your Own Working Robot – The Second Generation"?

DH: Second Generation had better hardware designs.

PG: Whatever happened to Buster (I-III)?

DH: Buster I is somewhere down in the crawlspace of my house. The others were scrapped or given away a long time ago.

PG: What about Rodney?

DH: I gave him to a high school science class. I imagine it is gone.

PG: Do you have any current photos of Buster and/or Rodney (assuming they still exist)?

DH: No.

PG: Were any other later hardware platforms built (but left unpublished)?

DH: Rodney had a short-lived expression as a commercial product sometime in the early-to-mid 80's. It was the RB5-X, manufactured by RB Robot Corp in Golden, Colorado. I was rather well compensated for the work, but the company and my compensation soon evaporated.

PG: Are you still involved in robotics and/or machine intelligence as a hobby or otherwise?

DH: No. But I like to tinker with my own version of artificial neural networks.

PG: Do you intend on writing any further books on robotics in the future?

DH: Not as a hobby machine. Over the years, I've used my models of machine intelligence to play with ideas about extraterrestrial intelligence.

PG: Are there any thoughts or advice you would give to today's robotics and/or machine intelligence enthusiasts?

DH: Let a machine think for itself. Let a community of machines think for themselves and share their knowledge and skills.

But keep your hand on the plug.

I feel that Mr. Heiserman's work is still relevant for today's robotics hobbyist, especially for those interested in machine learning. His techniques and programming methodologies can be easily applied to modern microcontroller and PC-based systems. There are many avenues available to explore in this research, and Mr. Heiserman has forged a path ahead of us to follow. If you are interested in robotics, you owe it to yourself to pick up a volume or two of his books, and explore.

Andrew L. Ayers, March 2008

The RB5X Connection:

Heiserman also wrote some software for the personal robot RB5X.  From an interview …

RN: Did you ever consider taking any of your robot designs commercial as kits or assembled robots?

DLH: I never did it on my own initiative, but Rodney appeared on the market as RB5-X. It was advertised as educational tool, and we had a couple of RB5s running around in the science center here in Columbus. The company was RB Robot, Inc., in Golden CO. When RB when bankrupt, someone else bought the rights and inventory. I don't think the machine is around anywhere these days. I was just a token consultant for the company, anyway.

David Hieserman had already built "Buster" the robot, but was developing "Rodney" the "Self-Programming" robot at the time. RB5X software utilized "Rodney" technology.

The RB5X robot comes with what the company calls Alpha and Beta level self-learning software. This "Artificial Intelligence" software, developed by David Heiserman allows your RB5X robot to learn from it's experiences.

Self-Learning Software / Artifical Intelligence
The RB5X comes complete with "Alpha" and "Beta" levels of self-learning software, which which empowered the robot to absorb and employ information from its surroundings. Developed by leading robotics author David Heiserman, this software allows RB5X to progress from simple random responses to an ability to generalize about the features of its environment, storing this data in its on-board memory.
Self-Learning: This small, first step toward true "intelligence" enables the robot to learn from its own mistakes. For example, you could set the RB5X down in a room and let it roam about randomly. It will probably run into walls several times, perhaps a desk, and maybe even a person. As it rolls around the room, it will "learn" in its own computer-like fashion where the obstacles are in a room, thus avoiding them in the future. The self-learning software are on "Alpha" and "Beta" levels, which were developed by the robotics author David Heiserman for the purpose of giving robots a simple way to "learn" from their experiences, somewhat like humans do.

See other early Mobile Robots here.


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W. Grey Walter’s Tortoises – Self-recognition and Narcissism

Self-recognition and the Mirror Dance

[Image source: An Imitation of Life,  Scientific American, May 1950, p42-45.]

7 . Self-recognition. The machines are fitted with a small flash-lamp bulb in the head which is turned off automatically whenever the photo-cell receives an adequate light signal. When a mirror or white surface is encountered the reflected light from the head-lamp is sufficient to operate the circuit controlling the robot's response to light, so that the machine makes for its own reflection; but as it does so, the light is extinguished, which means that the stimulus is cut off — but removal of the stimulus restores the light, which is again seen as a stimulus, and so on. The creature therefore lingers before a mirror, flickering, twittering, and jigging like a clumsy Narcissus. The behaviour of a creature thus engaged with its own reflection is quite specific, and on a purely empirical basis, if it were observed in an animal, might be accepted as evidence of some degree of self-awareness. In this way the machine is superior to many quite 'high' animals who usually treat their reflection as if it were another animal, if they accept it at all.

Source: p115, W. Grey Walter, The Living Brain, 1953 -see chapter 5 – Totems, Toys, and Tools

What can be seen or determined in the photo of Elsie below?  

1. tracer candle visibility;

2. low batteries (because it enters the hutch which is strategically placed to the right of the mirror).

Figure 7. Elsie performs in front of a mirror, but is probably responding to the candlelight rather than to her pilot light. [RH 2010 -Most earlier comments by others are of this rather un-clear image of the so-called 'mirror dance'.]

Prior to the release of the clearer Life image of Elsie performing the 'mirror dance' (see pic below) Holland in "Legacy of Grey Walter" describes it as follows:

Recognition of self
A pilot light is included in the scanning circuit in such a way that the headlamp is extinguished whenever another source of light is encountered. If, however, this other source happens to be a reflection of the headlamp itself in a mirror, the light is extinguished as soon as it is perceived and being no longer perceived, the light is again illuminated, and so forth. This situation sets up a feedback circuit of which the environment is a part, and in consequence the creature performs a characteristic dance which, since it appears always and only in this situation, may be regarded formally as being diagnostic of self-recognition. This suggests the hypothesis that recognition of self may depend upon perception of one’s effect upon the environment.

The below from Discussions on Child Development,  1971, see Book II 1954-56 p35-6.


With Fig. 6 we come to some of the refinements which emerged only some time after these creatures had been made. This mode of behaviour and the next one were, quite frankly, surprising to us though, of course, we ought to have been able to predict them. Fig. 6 illustrates the situation when a creature of this type is confronted by its reflection in a mirror. It has on its nose a small pilot light, put in originally to tell us what was happening inside; it is so arranged
that it is turned off when the creature sees another light; that is, it tells us when the photo-tropistic mechanism is in operation.
In this case, the light which the creature was allowed to see was its own pilot light in the mirror. In this situation, the act of 'seeing' it makes it automatically extinguish the light which it sees. The apparent stimulus light having been extinguished, it turns it on again, then off and so on, so that you get a characteristic oscillation. You can see how peculiar and regular it is by the zigzag going up the side of the mirror. This is an absolutely characteristic mode of behaviour, which is seen always and only when the creature is responding to its own reflection. This is an example of the situation I described in the second proposition, where the reflexive circuit includes an environmental operator; in such a situation you get a characteristic mode of behaviour which occurs always and only when the model is reacting to itself.


“The creature therefore lingers before a mirror, flickering, twittering and jigging like a clumsy Narcissus” (Grey Walter, 1963, p. 115). Grey Walter interpreted this famous mirror dance as evidence of self-recognition.

The drawing of the famous `mirror dance’ in `An imitation of life’ [from Scientific American] is nothing like the regular alternation between the tortoise's approach and avoidance as shown in the photograph, being an altogether more irregular and complex trajectory. There may well have been a mirror dance that could have been argued to be a form of self-recognition, but unfortunately this photograph cannot be said to be a record of it. The brightest light visible to the camera, and presumably to the photocell, is the candle on the tortoise’s back and its reflection in the mirror. The trace is far more likely to reflect the alternation of behaviour pattern P (approach to the reflected candlelight) with behaviour pattern O (obstacle avoidance on contact with the mirror). We can be sure that Walter used this image as an example of the mirror dance because it appears in the form of a diagram in the transcript of a talk he gave in 1954 (Walter 1956b); the text matches closely the account given in `Accomplishments of an artefact’. Interestingly, the description of the mirror dance in de Latil’s book also matches this photograph rather than Grey Walter’s original description and Bernarda Bryson Shahn’s sketch.

For most people, with regards to the image above (see figure 7), one could hardly refer to this behaviour as "flickering, twittering and jigging like a clumsy Narcissus". However, you could do so to the above illustration by Bernarda Bryson (partner and later married to the artist Ben Shahn), as illustrated in Scientific American (Walter, W. Grey, "An Imitation of Life," Scientific American, May 1950, p42-45.). The above illustration is actually of Elmer, and not Elsie as is the below photo. This also gives more credence to Grey's use of the word Narcissus, being the son of a Greek god who became obsessed by his own image. [Elmer scans clockwise, the opposite of Elsie and the bump aviodance traverse therefore is from right to left. see here.]

[Narcissus : In Greek mythology, a beautiful youth who fell in love with his own reflection. He was the son of the river god Cephissus and the nymph Leriope. His mother was told by a seer that he would have a long life, provided he never saw his own reflection. His callous rejection of the nymph Echo or of his lover Ameinias drew upon him the gods' vengeance: he fell in love with his own image in the waters of a spring and wasted away. The narcissus flower sprang up where he died.]

Although Elmer was then long gone, Grey Walter continued to use this more interesting description of self-recognition along with the below image, although it didn't and couldn't match with the sometimes erratic behaviour of the original tortoise, Elmer  and could no longer be reproduced with the newer models.

In my opinion, in the cycloidal trace seen above, the 'bottom' of the cycloid appears flattened and bright spots at the start of the cycloid 'flats' appear. To me, this is indicative of 'bump' avoidance behaviour, not self-recognition.  When I visited the Bristol Robot Labs in 2009 to see the replica tortoises, the comment was passed to me that they were unable to satisfactorily reproduce the self-recognition behaviour as described by Grey Walter.

A relatively recent , clearer image of the so-called 'mirror dance' as released by Life Magazine.

A comment on Time-Lapse Photographs in General:
In interpreting all the time-lapse photographs, there are several aspects to keep in mind.
As already mentioned in pervious posts on the Tortoises, the cycloidal gait makes Elsie traverse to the right as her scanner turns in a counter-clockwise direction. Elmer, on the other hand, scans clockwise and because of the trailing action of the rear-wheels, will veer to the left. I must say, though, that the illustrations suggest that with no light source to track towards, Elmer tends to move in a forward direction and not sideways.
Most of the pictures show Elsie heading towards a light, either a candle or the hutch light, sometime a light out of sight near the camera.
Where you see two identical Elsies, it is actually the photographer’s technique of photographing Elsie at the start of the run, then  Elsie at the end of the run. There are not two separate Tortoises except where they look physically different i.e. Elmer has the ‘scaled’ Bakelite sheeting shell. The single trajectory is also an indicator of only a single Tortoise being traced.

Notice also that the flame of the target candle is placed at the same height as the PEC (the Photo-Electric Cell) in the scanning turret. 

1963c- Cybernetic Dogs – Fred Chesson (American)

ROBOTICS: Featuring An Automated Pavlovian Dog!
Developed many years ago, in the "Pre-IC Age" these Robot Rovers could simulate such Classical Pavlovian Responses as: CONDITIONING, EXTINCTION, SPONTANEOUS RECOVERY, LEARNING CURVES and HIGHER-ORDER CONDITIONING.

Three-deck stepping-relays comprised the main elements of the dog's memory. A few transistors were used for "eye" and "ear" sensors, plus a "tail-wagging power amplifier."

Frederick W. Chesson

I knew of the April, 1961 "troubles" at SJ, but it was only when I was working in the Middletown area c 1961-69 that I regularly commuted through Berlin and got regular glimpses of the place and heard about it from fellow workers that I had any inclination to wonder what went on there. In that general period, I had developed the "Automated Pavlovian Dog" teaching-machine (also on my web site) that led to a connection with the Psychology Dept. at Wesleyan University. The "dog" was shown there and to a number of schools, hoping to build up my psych-lab construction business. I also attempted to interest Mr. Francis, knowing of his background, but by then in the late 1960s he had become excessively suspicious of my innocent motives, that resolved to "keep tabs" on what further incidents went on at SJ…which were all-too forthcoming over the next few years until the place finally closed for good.
Fred W. Chesson. E-mail 15 April 2006

The experiments with dogs relating to Classical Conditioning by Dr. Pavlov, earning the Nobel Prize for Medicine and Physiology in 1904, have been simulated over the years, culminating with today's extensive computer programs.
    The robot dogs shown in the photograph were developed by the author in the early Sixties, when the teaching-machine "fad" was approaching its heady zenith. At the time of the design, relay logic still had a cost advantage over the contemporary RTL gates, but some transistors were employed for the "eyes" and "ears" of the automated canines.
     Pavlov's experiments into Classical Conditioning underly much of modern learning theory; hence, if a robot, android, or humanoid is to learn, it is desirable to know what conditioning is all about. On a basic level, Pavlov rang a bell, then fed the dog, measuring the animal's response by the amount of saliva generated. After a while, the bell alone could evoke a salivatory reaction. On a human level, do our mouths not water at the mere aroma of a tasty pie? Or even at the verbal cue: "Dinner's ready!"…? But should the announcement prove false or premature, our anticipatory responses will diminish markedly. They can, however, be readily restored, along with our faith in human nature.
    Thus, the electro-mechanical dog was designed to perform the following simulations, which will be examined: conditioning (learning), extinction (forgetting), spontaneous recovery, higher order conditioning, learning curves, memory of stimuli occurrences, and stimuli hierarchy.
    In operation of the simulator, pressing the RESET switch puts the robot dog at an untrained level (electronic brainwash!). Salivation being somewhat difficult to imitate, the response to feeding was represented by having the dog wag its tail, a readily observable act of canine satisfaction. To hold the interest of younger students, the feeding stimulus was simulated via a plastic bone having a concealed magnet. When the magnet end of the bone was in proximity to the dog's "nose," a reed switch was closed, activating a tail-wagging power transistor and solenoid.
    Via a microphone and photocell, the dog could "hear" and "see." Normally, the audio stimulus was dominant, activating a Schmitt-trigger delay for a preset time interval. If the food stimulus was presented during this period, an AND gate caused this coincidence to be recorded by the Conditioning Event Counter, a ten-point stepping relay. (Today's equivalent probably being a CMOS type 4017 decimal-decoded counter chip.) Thus, when a preset number of coincidences had been registered, a relay flip-flop circuit caused the dog to now wag its tail to the sound stimulus as well as to food.
    So long as occasional sound-food coincidences, (reinforcement), occurred, the conditioned state would be maintained. But after another preset number of sound-stimuli without food following, (anticoincidence), say five, the flip-flop resets the dog to an unconditioned state, and it must be retrained.
    Sometimes, the experimenters found their animals would recover their condition, (spontaneous recovery), without any apparent external action.
This is similar to being given a telephone number in the afternoon, then forgetting it by night, only to have it suddenly come to mind the next morning, apparently released from some buffer-storage in the subconscious.
In the simulator, the spontaneous recovery function could be cut in and its "latent period" set by a potentiometer. Should normal conditioning then be re-established before it can act, it is reset for future use. Once it has acted, however, it is of a one-shot nature; following a second extinction, true conditioning must follow for the SR circuit to be reset.
    After conditioning and extinction, Pavlov found that his dogs not only relearned faster, but that their conditioned response was more resistant to extinction. This learning curve holds true in human education, as anyone who has learned a mathematical equation or foreign language will agree. Learning something the second time around nearly always is quicker and seems to stick longer as well.
    The learning curve simulation required multi-level stepping-relays in the original model, whose pick-off points were determined in connection with the original settings for conditioning and extinction counts. Thus, the original number of four coincidences would be reduced to three and then only one, while the anti-coincidences for extinction might be increased from five to six or
seven, and then to eight or ten.
    When the living dog has been very well trained to salivate to the sound of the bell, it was found that the bell as well as food could be employed to condition him to a new stimulus, such as light. This is called Higher-Order Conditioning, and represented the simulator's highest accomplishment, being activated by the learning curve counter.
    While the above model and its concepts are quite elementary, they still furnish a base upon which increasingly diverse and subtle forms of learning behavior may be simulated and explored. It has been found, for example, that conditioning is more resistant to extinction when every trial stimulus is not always rewarded. Such variable reinforcement scheduling, could lend itself readily to microprogramming applications.

"Way back in the dim '60s, in the midst of the Teaching Machine Era, I came up with a Pavlovian Dog demonstrator. (Two were actually built)  Responding to food (magnetic bone) sound and light stimuli, the concepts of Conditioning, Extinction, Learning Curves, Spontaneous Recovery and Higher-Order Conditioning were presented. (All done with relay logic and memory back then!)"
Fred W. Chesson

The Interface Age1978 article does not include the images above.

1976 – “Buster” Robot Animal – David L. Heiserman (American)

Although built using a child's electric car as the chassis, the fully functional Buster was a true Cybernetic Animal, showing reflexes, phototropism, and hunger / recharging modes. He could operate totally autonomously if so desired, but had manual overrides via a remote panel or remote control via an acoustic adapter.

No CPU chips here. Op-amps, TTL digital logic gates, comparators and 555-type timers.


This one-of-a-kind book offers complete instructions-plans, schematics, logic circuits,
and wiring diagrams-for building Buster, the most lovable (and mischievous) mechanical pet in the world! He'll serve you coffee or bring you the morning papers.
He'll forage for his own "food" and scream when he can't find it. His "curiosity" will get him into one plight after another, but Buster has the capacity to get himself out of trouble just as easily as he got into it! Not a project for novices, Buster is a sophisticated experiment in cybernetics. You build him in phases, and watch his personality develop as you add progressively more advanced circuitry to his mainframe. 238 pps., 117 illus.

Build Your Own Working Robot by David L. Heiserman


Buster is rather hard to describe in a few words. Part of the trouble with trying to describe Buster is that he ( or it) is two different things at the same time: he is both a machine and an evolutionary process. What's more, Buster is unique as a machine and quite unusual as a process.
As a machine, Buster represents the highest-order machine that technology can produce today. The lowest-order machine can be represented by simple hand tools such as hammers. screwdrivers, and pliers. The next order then takes the form of slightly more complicated labor-saving devices such as motors and engine-driven vehicles. Basic computer systems represent yet a higher order of machinery—machines that can save humans both mental and physical energy.
Buster is much more than any of these machines. He is much more than a tool, a man-controlled machine, or a computer system. Buster is a machine that is capable of setting its own goals and achieving them within the limitations of its own logical and physical abilities. And unlike any of the lower classes of machine, Buster can be fully operational without human intervention. Of course Buster can interact with a human operator, provided he doesn't have any other needs that are more urgent at the time. The completed Buster system can, in principle, live a long and active life in the total absence of human company. Lots of simpler machines can run without human intervention, too; but they cannot set their own goals.
One of the essential keys to Buster's unique position in the world of machines is his built-in animal-like reflex system. Every animal has a reflex system of some sort that mainly serves as a mechanism for survival or self-preservation; and most animal behavior is motivated by the needs of survival. Buster has a survival-oriented reflex system ; and whenever his energy cells become "hungry," for example. he takes action appropriate for recharging them.
Buster also has a need for activity. His primary goal in life. aside from keeping himself nourished, is to move about. He wanders around for hours on end. poking into corners and running headlong across the floor. If Buster's human doesn't take all the proper precautions, Buster can accidentally disable himself ; but as long as the accident isn't one that causes serious physical damage, Buster eventually gets himself out of the predicament or else begins crying for help.
Of what use is Buster? The question is not really appropriate. It's like asking what use is a puppy. Aside from the technical challenge of building such a system. Buster's real "use" lies in playing with him and watching him at work. Buster can be trained to do tricks and fetch a newspaper. but so can a puppy ( and for less money). The motivation for building such a system must come from the experimenter's own constitution—there must be a desire to work first-hand with the highest class of machine available today.
Buster is also a process. Unlike most other electronic projects. the system doesn't have to be complete before he comes alive. Buster evolves stepwise through this book. each step in the process adding more detail to his animal-like behavior.
This evolution-oriented program has the distinct advantage of letting you. the experimenter, reap some of the benefits of your time. labor. and cash outlay long before the program is completed. Once the basic mainframe, power supply. and power control systems are built. you can add whatever functions that time. finances, and moods dictate. And all the while, you'll have a machine that is fun and educational.
The Buster development program can be divided into three basic phases: Buster I, Buster II, and Buster III. Completing each one of these phases marks a major advance in Buster's modes of behavior; and for the sake of convenience, Buster is named according to his stage of development.
Buster I
Buster I is a wheeled machine that can be driven and steered by means of a simple control panel. Buster I can be run forward and in reverse at three different speeds. and turned left or right at two different steering angles. The control panel is connected to the machine via an umbilical cord. Although the machine is still run by a human operator, he can cause quite a stir among people who have never seen anything resembling a real robot. Besides. Buster I is fun to play with.
Buster II
The first half of the Buster II phase of the program  are concerned with developing Buster's autonomic reflex system and "brain" power. Buster has the capacity for making logical decisions of his own, but he has no way to implement his notions and needs in a physical way.
The first, and most important, reflex system is completed. Here, Buster II is given a set of touch sensors and a control system that lets him make an appropriate motion reflex whenever he blunders into a solid object. This blunder reflex mode takes priority over any other on-going activity, including direct commands from the human operator. Buster II is also given the ability to run ahead at full speed whenever he is not executing a blunder sequence. Buster, in other words, becomes an independent creature at this point in the program. Included is circuitry for sensing low battery levels and signaling the human operator whenever a low-battery condition arises. This is only the first portion of a complete hunger mode that will be completed as part of the Buster III phase. The hunger alarm board also doubles as a special blunder alarm that sounds whenever Buster becomes trapped between two immovable objects.
The umbilical cord is finally cut. You'll spend considerable time and effort working on an up-to-date acoustical data communications system that lets you communicate with Buster via remote control.
Buster III
The Buster III phase of the program opens with a discussion of a generalized tracking function. Whereas Buster II is characterized by some reflex responses and independent activity, the main point of the Buster III phase is to give him an active goal-seeking capability. The tracking interface system can be used wherever Buster is supposed to follow or track down a target object.
The hunger alarm system in Buster II merely sensed a low-battery condition and caused Buster to whistle for his master. With the tracking interface now available, Buster no longer has to call for help whenever his batteries begin running low—he simply seeks out his battery charger and plugs himself in.
Buster III is a truly independent creature. He can wander about for hours on end, blundering away from solid objects and running at full speed across the floor and when the batteries run critically low, he immediately tracks down the battery charger. Once the batteries are recharged, he backs out of his "nest" and resumes his feverish activity. You can take over control via the remote or direct terminals, but Buster III always retains his reflex capability that overrides just about anything his master tells him to do.
Anything added to the Buster II system from this point on is simply icing on the cake, e.g. a line-tracing function that lets Buster follow any sort of light-colored line on the floor and suggests some other tracking functions that can direct Buster to respond when he is called.
The final chapter introduces a proposed Buster IV system: one where a microprocessor brain is added to his basic reflex and goal-seeking modes of behavior. The Buster II system is upward-compatible with just about any sort of modern data system; given the appropriate kinds of sensors, Buster can become as much a mechanical animal as your talent, imagination, and resources allow.

[Note: RH 2010 – The final chapter on a proposed Buster IV with a microprocessor brain did not eventuate. The closest we get to is in the acoustic data link so “the whole Buster system can be placed under the control of a more sophisticated minicomputer or microprocessor system that is too bulky to be included in the mainframe assembly.”]

Heiserman also wrote some software for the personal robot RB5X.

See a transcript of a 2008 interview with David Heiserman here.

See other early Mobile Robots here.


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1972 – “CYCLOPS” – L. C. Galitz (British)

"CYCLOPS" (CYbernetically  Controlled Light Oriented and Powered System) , built by L. C. Galitz as a construction project for The Radio Constructor [later renamed to Radio & Electronics Constructor from Jan 1973 onwards].

Cyclops is one of the last construction projects for a fully featured cybernetic model subscribing to the conditioned reflex approach to a 'learning machine'. Galitz is heavily influenced by Grey Walter's tortoises, in both M. speculatrix and M. docilis forms. His text shows likness to Walter's book, "The Living Brain".

 pdf's here – part1, part2, part3, part4, part5, part6, mods part1, and mods part2.

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