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Robotics & System 4
by Robert Campbell 2006
by Robert Campbell 2006
The following demonstrates the value of System 4 as it may be applied to robotics and computer generated
Artificial Intelligence (AI).
Introductory Comments:
a). It is not necessary to see deeply into the dynamics of each of the nine Terms of System 4 to apply it to
AI. The System can be understood in more superficial levels of abstraction if the meanings of the System
Terms are simply accepted as valid. The overall pattern can more readily be applied to computer programming
in directly practical ways.
b) For a robot that can navigate irregular terrain a hexapod robot has obvious advantages for applications
such as a Mars rover. Moreover there is a mechanical linkage system that can be used for each pair of legs, so
that a single motor can activate each pair to walk. The spine of the hexapod can be articulated to make turns
by two additional motors operating spinal joints between each pair of legs thus enabling the robot to avoid
obstacles. System 4 allows for simulated strides that alternate with strides that react directly to sensory input.
The simulated strides are called Regenerative and involve an anticipated plan over a series of strides. The
reactionary strides are called Expressive and respond to immediate sensory input one stride at a time. These
Regenerative and Expressive strides must be mutually reconciled in an ongoing fashion. The same principles
can be applied to grasping and manipulating articles, such as a baby learning to grasp articles.
c) Elements of experience are learned piecemeal and gradually assimilated into more coherent complex
actions. Each element of experience can be considered a unit memory. For a baby, grasping with the fingers is
one of the first things we learn. We are born much more helpless than other animals and must learn nearly
everything through conscious effort even before we have language to assist us. Proprioceptive simulation, as
in the regenerative mode, is indispensable to this learning process. The proprioceptive nervous system tells us
the body’s position oriented in space and proprioceptive neuromuscular spindles, the tiny sensory organs
located throughout the muscles of the body are structured to allow simulation of anticipated actions. Learning is
more than just a causal process of successive responses to external stimuli. It also involves anticipation of a
future desired result and a process of simulation to acheive it. Language greatly enhances our abilities to
simulate experience in abstraction and formulate far reaching plans that neverthelss require continual
adjustment.
d) Practical applications of AI in robots can be one of several avenues through which we may become
more conscious of how the cosmic order works. At this time in human history, with so much potential conflict
looming ahead, I believe that we need to expand our horizons beyond vested interests. We need a more
universal context within which to constructively express our many diverse concerns. What I call the involutionary
variant of the cosmic order leads inexorably to fragmentation and conflicts of interest, to the ultimate benefit of
no one and to the detriment of all. Whatever your sentiments in this regard I do not wish to attach any idealistic
override to this offering of ideas freely given.
In carefully studying what follows, patient reference to System 4 on this website will facilitate an initial overall
grasp of System 4 and how it works.
System 4 and Robotics:
The following simplifies the essence of System 4 as much as possible as it relates to a virtual robot. Keep in
mind that language is limited in the degree to which it can describe how the System works, so that meanings
must be interpreted contextually. The description that follows relates quite directly to the task of generating AI
in a robot.
It helps if we have simple mechanical linkages established for legs to begin with. We do not have to explore the
evolution of legs as the invertebrates did before evolution settled on a quadruped limb structure for all
vertebrates. We can assume a hexapod for walking stability and the simplest linkages to make it easy. Any
linkage method may be used of course but others necessitate proprioceptive organs and make the simple act
of walking more complex.
As explained on the website there is a System 4 hierarchy involving 4 active Centers(C) that implicitly give
direction to one another as follows:
(C1)IDEA->(C2)KNOWLEDGE->(C3)ROUTINE->(C4)FORM
(C1) IDEA can be regarded as the electronic activity in a computing program in a specific instance.
(C2) KNOWLEGE is manifest in the program itself as it relates to the hardware.
(C3) ROUTINE is the specific virtual routines that are being animated.
(C4) FORM is how the above Routines determine the orientation of the Form of virtual concepts, such as the
change in position of a robot with respect to the environment (whether it is a virtual robotic movement or a
virtual perceptual idea derived from the environment.)
The hierarchy above is specified by the Primary Universal Term (Term 9) but for simplicity we can initially set
the Universal Terms aside for the purposes here, and consider only the Six Particular Terms that relate directly
to six specific structural elements that occur in every creative activity. These six Terms consist of 6 of the 9
ways that four active Centers can relate to one another with respect to their inside and outside, but we shouldn’
t need this for now also. Each Term has a meaning implicit within it and we will take this meaning for granted.
The six Terms transform into one another in a specific repeating sequence that we will also take for granted as
follows:
T1->T4->T2->T8->T5->T7->T1->T4-> etc. ( the six step sequence keeps repeating)
The meaning implicit within each of the Terms is as follows:
1.-T1 - Perception of need in relation to response capacity.
2.-T4 - Ordered sensory input alternately from the environment & simulated.
3.-T2 - Creation of idea as a potential action response or creative concept.
4.-T8 - Balanced response to sensory stimuli as a motor output (eg to muscles or robot motors)
5.-T5 - Action sequence (eg muscular or motor driven) with proprioceptive feedback
6.-T7 - Sequence encoded as a unit memory for recall to T1 and another sequence.
The above Term transformations alternately go through an Expressive and then a Regenerative sequence, so
there are 12 transformations, each called a Step. In the human nervous system each Step coincides precisely
with a synapse in the way the nervous system is structured to work. So we have a means to follow initial
sensory inputs through the sequence synapse by synapse for any process of integrated sensory perception,
conceptual thought, or resultant action. In the case of integrating visual sensory images Systems higher than
System 4 are involved, since virtual images begin with System 5. We will focus here only on System 4. We
should also be able to follow the same sequence in constructing a virtual robot.
There are three Particular Sets simultaneously transforming through each pathway through the nervous
system, each Set being one Step apart. The regenerative sequence in each case concerns a proprioceptive
simulation of an anticipated future act, whereas the expressive sequence is a programmed active response
driven causally as a reaction to direct sensory input. Since the three Sets are out of step in the sequence there
is always an anticipated future that must be reconciled with a casually driven input from the past. In this way
System 4 spans and integrates past and future. The two modes are mutually related and so must be mutually
reconciled with one another. This process can integrate history in the broadest sense.
We can list the 12 Steps for each of the 3 Sets as shown below. This allows us to easily see which Terms in the
Expressive and Regenerative Modes interact in each Step. The regenerative Terms are shown in red:
Step Set 1 Set 2 Set 3
1 T8E T7R T4E
2 T5E T1R T2E
3 T7E T4R T8E
4 T1E T2R T5R
5 T4E T8E T7R
6 T2E T5E T1R
7 T8E T7E T4R
8 T5R T1E T2R
9 T7R T4E T8E
10 T1R T2E T5E
11 T4R T8E T7E
12 T2R T5R T1E
New sensory input from the environment comes via T4E in Set 3 in Step 1. Sensory input T4E is always
tensionally coupled to memory recall T7R to begin a related simulation sequence that will anticipate an
appropriate response. Memory recall must always be coupled to sensory input in order for our thoughts,
feelings, and actions to be relevant to ongoing circumstantial input. This must also be reconciled with the
previous action sequence T8E (simultaneous motor instructions to muscles or motors) in order for there to be a
smooth transition from sequence to sequence.
(Sequence illustrations can be sent to provide more detailed information on this, albeit very condensed. It takes
a lot of study to understand this fully as it relates to human experience, but most of this can be set aside for a
robot.)
So let us see how this will relate to a virtual robot so far. It has 3 paired sets of legs that move in symmetrically
mirrored strides. Let us consider paired movements one Step at a time according to how the linkages of legs
are designed. (The linkage design can be provided on request.)
1. Front and rear pairs: As the leg on one side raises to step forward the leg on the other side pushes
down and moves backward to move the robot forward. Since this can be accomplished by mechanical linkage
with a single motor for each pair of legs we do not have to compute motions for each joint segment in each leg.
But we do need to set the distance that each step involves, so that the feet that follow will not trip into the feet
ahead.
2. Middle pair: At the same time the middle leg on the opposite side raises to step forward with the leg on
the other side pushing down and back.
3. The next step is the mirror image of the first.
4. So the front and back motors would work in identical patterns and the middle motor would work in synch
but in a mirrored pattern. This can be easily programmed as a transmitted motor pattern T8E in Step 1 above.
It keeps repeating and operating switching to activate motors to move limbs accordingly as in T5E in Step 2.
Every other Step has a T8E term and alternate Steps have either a T5E or a T5R term.
5. Let us assume that the robot has a scanning device to identify obstacles ahead that it must avoid in
order to walk to a preprogrammed destination that is given by certain coordinates. In Step 1 the scanning
device provides sensory input T4E for obstacles a number of estimated strides ahead. For example it may be
that the way ahead is clear in Step 1 for seven more strides but probably not for eight more strides. So a
memory term T7R is recalled in Step 1 that begins a motor simulation T1R in the CPU in Step 2. Let us say that
the scanning device identifies that size of the obstacle to be circumvented, so the T7R will have to recall
synchronous motor patterns for all of the motors involved in such a way that they are integrated into a turning
maneuver of so many degrees per stride. This turning maneuver is a programmed memory of previous turning
maneuvers taken and which may or may not be adequate to avoid the obstacle within eight strides, or it may be
too sharp of a turn.
6. Let us assume that the robot has two articulated joints in its spine, one between each pair of legs. There
is a motor that regulates the alignment of each spinal joint laterally but not vertically and which keeps the spine
longitudinally straight when the robot is walking straight. The robot's feet (and/or joint segments) also have a
certain amount of flexibility built into them to allow turns up to a maximum amount per stride. So the program
recalled to enact a simulation will have taken this into account and not exceed a certain turning radius that
could cause feet to drag or conflict, but it could be a lesser turning radius. We don’t want the robot going out of
its way unnecessarily.
7. So in Step 2, T1R is doing a motor simulation that will redirect the robot over several strides in the
future, while T2E is also generating a turning idea in the CPU from direct external sensory input provided in
T4E. But this latter turning idea is simply a reactionary response to the obstacle ahead without benefit of a
simulation to see if the turn is sufficient or too much. There may also be a second obstacle further ahead to
avoid so the robot has to pick a course through. The reactionary or expressive idea T2E generated by direct
sensory input may indicate a turn that is too fast. It can only try to make the turn in one stride according to the
perceived angle it needs to turn, and cannot simulate the turn stride by stride over a planned future course. It
is also limited by the maximum turn that can be taken in one stride. So in Step 2 the motor simulation T1R may
exchange inputs to and from T2E. Both are executed in the CPU. The motor simulation only relates to the
adjustments to spinal alignments with possible necessary adjustments to length of stride. The T2E term thus
relates to a more simplistic motor pattern that will tend to get the turn over with as quickly as possible but it can
be modified by some input from the simulation.
8. The motor simulation T1R is not the actual simulation however. It only indicates a tentative motor pattern
that will hopefully be adequate over several strides. The actual simulation takes place in T4R in Step 3 where
the next few stride positions are simulated in relation to the obstacle with simulated sensory feedback as to
projected Step positions in relation to the obstacle. The perspective of the obstacle changes with the robot's
position. A future path is charted that should be adequate but that will require Step by Step adjustments as the
path opens around obstacles.
9. This simulated sensory feedback in T4R is tensionally coupled to a new memory term T7E which
incorporates motor patterns in the element of stride technique recalled that will be consistent with the
simulation. It is a programmed automatic response from the computer memory that will fall within the parameters
prescribed by the simulated sensory feedback. At the same time a consistent pattern of motor instructions T8E
in Set 3 will be sent to operate switches and regulators for motors to perform a stride in T5R in Step 4.
10. In Step 4 the motor programs have been selected from previous related experience that also falls
within current simulated parameters, so Knowledge (C2) directs Idea (C1) in a Regenerative T5R term rather
than an Expressive T5E term where C1 and C2 exchange places. So the switch from Expressive to
Regenerative modes takes place here. When completed this action pattern becomes stored as a T7R memory
in Step 5. In Step 5 a related action pattern memory will be recalled simultaneously. In other words a memory is
being stored at the same time that a new but related memory is being recalled. The recalled pattern may differ
from the pattern being stored in some aspects since the recalled pattern is coupled to new sensory input T4E
in Set 2 that is synchronous with it in Step 5. Memory recall is always tensionally linked to sensory input.
11. T7E in Step 3 transforms into T1E in Step 4. T1E readies the necessary elements of the robot to
receive new input from the environment. The scanning device must be readied, pointed and focused to take
another “snap shot” of obstacles ahead in T4E in Step 5.
12. At the same time T2R in Step 4 is the new simulated idea as a planned sequence of strides consistent
with the simulation in T4R in Step 3 that anticipates avoiding the obstacle. This planned sequence of strides
translates into a specific motor pattern T8E in Step 5. In this case T8E is the next stride in the planned
sequence of strides. Subsequent planned motor pattern turning strides will require revision with respect to both
circumventing the obstacle from a new perspective and getting back on course to the intended destination,
because T2R terms alternate with T2E terms and the perspective from which sensory input comes keeps
changing.
Five Steps is sufficient to illustrate how System 4 can be used to guide the robot. (In Step 5 new sensory input
comes via T4E in Set 1.) This has obvious advantages over methods that attempt to preprogram the robot’s
path from start to finish. Any number of contingent obstacles that may crop up can be accommodated Step by
Step and stride by stride. This process is greatly facilitated by the mechanical linkages of the hexapod that
eliminate the need for proprioceptive organs in order to simulate and compute leg joint segment by joint
segment movements in the simple process of walking.
When it comes to grasping and carrying things the robot would have to be fitted with arms and hands. Guiding
these to specifically grasp identified objects and manipulating or moving them in desired ways could be done in
a couple of ways, both of which amount to dependence on proprioceptive feedback. Proprioceptive devices can
be fitted to provide sensory feedback to a second scanning device in the “eyes” of the robot, like little
transmitters to a scanning receiver. The System 4 Steps would then follow as above for walking, but with more
complex simulations and movements involved.
In humans Expressive modes and Regenerative modes are mutually influenced and become automated over
time (at the spinal level for behavioral patterns), if they are suitable behaviors of practical value. This 12 Step
sequence thus forms the basis of the learning cycle spanning past and future. It works synchronously through
any number of parallel pathways through the body at once, as in moving both hands synchronously to perform
an integrated task. All parallel pathways have the same number of System 4 Steps and the nervous system has
evolved this way synapse by synapse in all vertebrate quadrupeds, with the same number of corresponding
synapses in each pathway, from reptiles to humans. All of these parallel pathways must be integrated by the
unique Universal Sets associated with each species and each human being.
It can work in a similar way in a robot. Regenerative simulated action patterns reconciled with Expressive action
patterns, and vice versa, can be stored as unit memories of action sequences as they happen. In humans we
learn to do things piecemeal, little by little, putting the pieces together into integrated sequences that span
space and time. It can work the same way in robot within the more limited context of an electronic memory. This
represents a basic level of learning for the robot, including some limited degree of creative expression.
When it comes to using the hands and fingers for doing tasks, then a second level of simulation along the lines
of how the cerebellum and cerebral hemispheres work would be valuable if not indispensable, involving
interacting CPUs in a robot. It would still follow along the same lines of System 4 Step by Step. Much more on
this can be sent to anyone interested.
We do not need to physically act to think of course, so all of the above can relate equally well to generating
conceptual Forms rather than behavioral Forms in a human being. At the conscious level this happens in the
cerebral hemispheres with emotional input from the ancient limbic system. Memory recall is in fact most
fundamentally dependent upon the reptilian part of the cerebral hemispheres. (We remain biologically
anchored to our biospheric roots and we can draw upon ancient emotional patterns of behavior that require
appropriate tailoring to suit the needs of social circumstance. We must restrain and modify our most primal
appetites in socially acceptable ways.)
It should be possible to include and program some such analogous second order CPU operating in a robot,
albeit limited in its creative abilities by the limitations implicit in electronic memories.
Artificial Intelligence (AI).
Introductory Comments:
a). It is not necessary to see deeply into the dynamics of each of the nine Terms of System 4 to apply it to
AI. The System can be understood in more superficial levels of abstraction if the meanings of the System
Terms are simply accepted as valid. The overall pattern can more readily be applied to computer programming
in directly practical ways.
b) For a robot that can navigate irregular terrain a hexapod robot has obvious advantages for applications
such as a Mars rover. Moreover there is a mechanical linkage system that can be used for each pair of legs, so
that a single motor can activate each pair to walk. The spine of the hexapod can be articulated to make turns
by two additional motors operating spinal joints between each pair of legs thus enabling the robot to avoid
obstacles. System 4 allows for simulated strides that alternate with strides that react directly to sensory input.
The simulated strides are called Regenerative and involve an anticipated plan over a series of strides. The
reactionary strides are called Expressive and respond to immediate sensory input one stride at a time. These
Regenerative and Expressive strides must be mutually reconciled in an ongoing fashion. The same principles
can be applied to grasping and manipulating articles, such as a baby learning to grasp articles.
c) Elements of experience are learned piecemeal and gradually assimilated into more coherent complex
actions. Each element of experience can be considered a unit memory. For a baby, grasping with the fingers is
one of the first things we learn. We are born much more helpless than other animals and must learn nearly
everything through conscious effort even before we have language to assist us. Proprioceptive simulation, as
in the regenerative mode, is indispensable to this learning process. The proprioceptive nervous system tells us
the body’s position oriented in space and proprioceptive neuromuscular spindles, the tiny sensory organs
located throughout the muscles of the body are structured to allow simulation of anticipated actions. Learning is
more than just a causal process of successive responses to external stimuli. It also involves anticipation of a
future desired result and a process of simulation to acheive it. Language greatly enhances our abilities to
simulate experience in abstraction and formulate far reaching plans that neverthelss require continual
adjustment.
d) Practical applications of AI in robots can be one of several avenues through which we may become
more conscious of how the cosmic order works. At this time in human history, with so much potential conflict
looming ahead, I believe that we need to expand our horizons beyond vested interests. We need a more
universal context within which to constructively express our many diverse concerns. What I call the involutionary
variant of the cosmic order leads inexorably to fragmentation and conflicts of interest, to the ultimate benefit of
no one and to the detriment of all. Whatever your sentiments in this regard I do not wish to attach any idealistic
override to this offering of ideas freely given.
In carefully studying what follows, patient reference to System 4 on this website will facilitate an initial overall
grasp of System 4 and how it works.
System 4 and Robotics:
The following simplifies the essence of System 4 as much as possible as it relates to a virtual robot. Keep in
mind that language is limited in the degree to which it can describe how the System works, so that meanings
must be interpreted contextually. The description that follows relates quite directly to the task of generating AI
in a robot.
It helps if we have simple mechanical linkages established for legs to begin with. We do not have to explore the
evolution of legs as the invertebrates did before evolution settled on a quadruped limb structure for all
vertebrates. We can assume a hexapod for walking stability and the simplest linkages to make it easy. Any
linkage method may be used of course but others necessitate proprioceptive organs and make the simple act
of walking more complex.
As explained on the website there is a System 4 hierarchy involving 4 active Centers(C) that implicitly give
direction to one another as follows:
(C1)IDEA->(C2)KNOWLEDGE->(C3)ROUTINE->(C4)FORM
(C1) IDEA can be regarded as the electronic activity in a computing program in a specific instance.
(C2) KNOWLEGE is manifest in the program itself as it relates to the hardware.
(C3) ROUTINE is the specific virtual routines that are being animated.
(C4) FORM is how the above Routines determine the orientation of the Form of virtual concepts, such as the
change in position of a robot with respect to the environment (whether it is a virtual robotic movement or a
virtual perceptual idea derived from the environment.)
The hierarchy above is specified by the Primary Universal Term (Term 9) but for simplicity we can initially set
the Universal Terms aside for the purposes here, and consider only the Six Particular Terms that relate directly
to six specific structural elements that occur in every creative activity. These six Terms consist of 6 of the 9
ways that four active Centers can relate to one another with respect to their inside and outside, but we shouldn’
t need this for now also. Each Term has a meaning implicit within it and we will take this meaning for granted.
The six Terms transform into one another in a specific repeating sequence that we will also take for granted as
follows:
T1->T4->T2->T8->T5->T7->T1->T4-> etc. ( the six step sequence keeps repeating)
The meaning implicit within each of the Terms is as follows:
1.-T1 - Perception of need in relation to response capacity.
2.-T4 - Ordered sensory input alternately from the environment & simulated.
3.-T2 - Creation of idea as a potential action response or creative concept.
4.-T8 - Balanced response to sensory stimuli as a motor output (eg to muscles or robot motors)
5.-T5 - Action sequence (eg muscular or motor driven) with proprioceptive feedback
6.-T7 - Sequence encoded as a unit memory for recall to T1 and another sequence.
The above Term transformations alternately go through an Expressive and then a Regenerative sequence, so
there are 12 transformations, each called a Step. In the human nervous system each Step coincides precisely
with a synapse in the way the nervous system is structured to work. So we have a means to follow initial
sensory inputs through the sequence synapse by synapse for any process of integrated sensory perception,
conceptual thought, or resultant action. In the case of integrating visual sensory images Systems higher than
System 4 are involved, since virtual images begin with System 5. We will focus here only on System 4. We
should also be able to follow the same sequence in constructing a virtual robot.
There are three Particular Sets simultaneously transforming through each pathway through the nervous
system, each Set being one Step apart. The regenerative sequence in each case concerns a proprioceptive
simulation of an anticipated future act, whereas the expressive sequence is a programmed active response
driven causally as a reaction to direct sensory input. Since the three Sets are out of step in the sequence there
is always an anticipated future that must be reconciled with a casually driven input from the past. In this way
System 4 spans and integrates past and future. The two modes are mutually related and so must be mutually
reconciled with one another. This process can integrate history in the broadest sense.
We can list the 12 Steps for each of the 3 Sets as shown below. This allows us to easily see which Terms in the
Expressive and Regenerative Modes interact in each Step. The regenerative Terms are shown in red:
Step Set 1 Set 2 Set 3
1 T8E T7R T4E
2 T5E T1R T2E
3 T7E T4R T8E
4 T1E T2R T5R
5 T4E T8E T7R
6 T2E T5E T1R
7 T8E T7E T4R
8 T5R T1E T2R
9 T7R T4E T8E
10 T1R T2E T5E
11 T4R T8E T7E
12 T2R T5R T1E
New sensory input from the environment comes via T4E in Set 3 in Step 1. Sensory input T4E is always
tensionally coupled to memory recall T7R to begin a related simulation sequence that will anticipate an
appropriate response. Memory recall must always be coupled to sensory input in order for our thoughts,
feelings, and actions to be relevant to ongoing circumstantial input. This must also be reconciled with the
previous action sequence T8E (simultaneous motor instructions to muscles or motors) in order for there to be a
smooth transition from sequence to sequence.
(Sequence illustrations can be sent to provide more detailed information on this, albeit very condensed. It takes
a lot of study to understand this fully as it relates to human experience, but most of this can be set aside for a
robot.)
So let us see how this will relate to a virtual robot so far. It has 3 paired sets of legs that move in symmetrically
mirrored strides. Let us consider paired movements one Step at a time according to how the linkages of legs
are designed. (The linkage design can be provided on request.)
1. Front and rear pairs: As the leg on one side raises to step forward the leg on the other side pushes
down and moves backward to move the robot forward. Since this can be accomplished by mechanical linkage
with a single motor for each pair of legs we do not have to compute motions for each joint segment in each leg.
But we do need to set the distance that each step involves, so that the feet that follow will not trip into the feet
ahead.
2. Middle pair: At the same time the middle leg on the opposite side raises to step forward with the leg on
the other side pushing down and back.
3. The next step is the mirror image of the first.
4. So the front and back motors would work in identical patterns and the middle motor would work in synch
but in a mirrored pattern. This can be easily programmed as a transmitted motor pattern T8E in Step 1 above.
It keeps repeating and operating switching to activate motors to move limbs accordingly as in T5E in Step 2.
Every other Step has a T8E term and alternate Steps have either a T5E or a T5R term.
5. Let us assume that the robot has a scanning device to identify obstacles ahead that it must avoid in
order to walk to a preprogrammed destination that is given by certain coordinates. In Step 1 the scanning
device provides sensory input T4E for obstacles a number of estimated strides ahead. For example it may be
that the way ahead is clear in Step 1 for seven more strides but probably not for eight more strides. So a
memory term T7R is recalled in Step 1 that begins a motor simulation T1R in the CPU in Step 2. Let us say that
the scanning device identifies that size of the obstacle to be circumvented, so the T7R will have to recall
synchronous motor patterns for all of the motors involved in such a way that they are integrated into a turning
maneuver of so many degrees per stride. This turning maneuver is a programmed memory of previous turning
maneuvers taken and which may or may not be adequate to avoid the obstacle within eight strides, or it may be
too sharp of a turn.
6. Let us assume that the robot has two articulated joints in its spine, one between each pair of legs. There
is a motor that regulates the alignment of each spinal joint laterally but not vertically and which keeps the spine
longitudinally straight when the robot is walking straight. The robot's feet (and/or joint segments) also have a
certain amount of flexibility built into them to allow turns up to a maximum amount per stride. So the program
recalled to enact a simulation will have taken this into account and not exceed a certain turning radius that
could cause feet to drag or conflict, but it could be a lesser turning radius. We don’t want the robot going out of
its way unnecessarily.
7. So in Step 2, T1R is doing a motor simulation that will redirect the robot over several strides in the
future, while T2E is also generating a turning idea in the CPU from direct external sensory input provided in
T4E. But this latter turning idea is simply a reactionary response to the obstacle ahead without benefit of a
simulation to see if the turn is sufficient or too much. There may also be a second obstacle further ahead to
avoid so the robot has to pick a course through. The reactionary or expressive idea T2E generated by direct
sensory input may indicate a turn that is too fast. It can only try to make the turn in one stride according to the
perceived angle it needs to turn, and cannot simulate the turn stride by stride over a planned future course. It
is also limited by the maximum turn that can be taken in one stride. So in Step 2 the motor simulation T1R may
exchange inputs to and from T2E. Both are executed in the CPU. The motor simulation only relates to the
adjustments to spinal alignments with possible necessary adjustments to length of stride. The T2E term thus
relates to a more simplistic motor pattern that will tend to get the turn over with as quickly as possible but it can
be modified by some input from the simulation.
8. The motor simulation T1R is not the actual simulation however. It only indicates a tentative motor pattern
that will hopefully be adequate over several strides. The actual simulation takes place in T4R in Step 3 where
the next few stride positions are simulated in relation to the obstacle with simulated sensory feedback as to
projected Step positions in relation to the obstacle. The perspective of the obstacle changes with the robot's
position. A future path is charted that should be adequate but that will require Step by Step adjustments as the
path opens around obstacles.
9. This simulated sensory feedback in T4R is tensionally coupled to a new memory term T7E which
incorporates motor patterns in the element of stride technique recalled that will be consistent with the
simulation. It is a programmed automatic response from the computer memory that will fall within the parameters
prescribed by the simulated sensory feedback. At the same time a consistent pattern of motor instructions T8E
in Set 3 will be sent to operate switches and regulators for motors to perform a stride in T5R in Step 4.
10. In Step 4 the motor programs have been selected from previous related experience that also falls
within current simulated parameters, so Knowledge (C2) directs Idea (C1) in a Regenerative T5R term rather
than an Expressive T5E term where C1 and C2 exchange places. So the switch from Expressive to
Regenerative modes takes place here. When completed this action pattern becomes stored as a T7R memory
in Step 5. In Step 5 a related action pattern memory will be recalled simultaneously. In other words a memory is
being stored at the same time that a new but related memory is being recalled. The recalled pattern may differ
from the pattern being stored in some aspects since the recalled pattern is coupled to new sensory input T4E
in Set 2 that is synchronous with it in Step 5. Memory recall is always tensionally linked to sensory input.
11. T7E in Step 3 transforms into T1E in Step 4. T1E readies the necessary elements of the robot to
receive new input from the environment. The scanning device must be readied, pointed and focused to take
another “snap shot” of obstacles ahead in T4E in Step 5.
12. At the same time T2R in Step 4 is the new simulated idea as a planned sequence of strides consistent
with the simulation in T4R in Step 3 that anticipates avoiding the obstacle. This planned sequence of strides
translates into a specific motor pattern T8E in Step 5. In this case T8E is the next stride in the planned
sequence of strides. Subsequent planned motor pattern turning strides will require revision with respect to both
circumventing the obstacle from a new perspective and getting back on course to the intended destination,
because T2R terms alternate with T2E terms and the perspective from which sensory input comes keeps
changing.
Five Steps is sufficient to illustrate how System 4 can be used to guide the robot. (In Step 5 new sensory input
comes via T4E in Set 1.) This has obvious advantages over methods that attempt to preprogram the robot’s
path from start to finish. Any number of contingent obstacles that may crop up can be accommodated Step by
Step and stride by stride. This process is greatly facilitated by the mechanical linkages of the hexapod that
eliminate the need for proprioceptive organs in order to simulate and compute leg joint segment by joint
segment movements in the simple process of walking.
When it comes to grasping and carrying things the robot would have to be fitted with arms and hands. Guiding
these to specifically grasp identified objects and manipulating or moving them in desired ways could be done in
a couple of ways, both of which amount to dependence on proprioceptive feedback. Proprioceptive devices can
be fitted to provide sensory feedback to a second scanning device in the “eyes” of the robot, like little
transmitters to a scanning receiver. The System 4 Steps would then follow as above for walking, but with more
complex simulations and movements involved.
In humans Expressive modes and Regenerative modes are mutually influenced and become automated over
time (at the spinal level for behavioral patterns), if they are suitable behaviors of practical value. This 12 Step
sequence thus forms the basis of the learning cycle spanning past and future. It works synchronously through
any number of parallel pathways through the body at once, as in moving both hands synchronously to perform
an integrated task. All parallel pathways have the same number of System 4 Steps and the nervous system has
evolved this way synapse by synapse in all vertebrate quadrupeds, with the same number of corresponding
synapses in each pathway, from reptiles to humans. All of these parallel pathways must be integrated by the
unique Universal Sets associated with each species and each human being.
It can work in a similar way in a robot. Regenerative simulated action patterns reconciled with Expressive action
patterns, and vice versa, can be stored as unit memories of action sequences as they happen. In humans we
learn to do things piecemeal, little by little, putting the pieces together into integrated sequences that span
space and time. It can work the same way in robot within the more limited context of an electronic memory. This
represents a basic level of learning for the robot, including some limited degree of creative expression.
When it comes to using the hands and fingers for doing tasks, then a second level of simulation along the lines
of how the cerebellum and cerebral hemispheres work would be valuable if not indispensable, involving
interacting CPUs in a robot. It would still follow along the same lines of System 4 Step by Step. Much more on
this can be sent to anyone interested.
We do not need to physically act to think of course, so all of the above can relate equally well to generating
conceptual Forms rather than behavioral Forms in a human being. At the conscious level this happens in the
cerebral hemispheres with emotional input from the ancient limbic system. Memory recall is in fact most
fundamentally dependent upon the reptilian part of the cerebral hemispheres. (We remain biologically
anchored to our biospheric roots and we can draw upon ancient emotional patterns of behavior that require
appropriate tailoring to suit the needs of social circumstance. We must restrain and modify our most primal
appetites in socially acceptable ways.)
It should be possible to include and program some such analogous second order CPU operating in a robot,
albeit limited in its creative abilities by the limitations implicit in electronic memories.
Robotics & System 4
