308 Donnett and Smithers 
Neuronal Group Selection Theory: 
A Grounding in Robotics 
Jim Donnett nd Tim Smithers 
Department of Artificial Intelligence 
University of Edinburgh 
5 Forrest Hill 
Edinburgh EH1 2QL 
S COTLAND 
ABSTRACT 
In this paper, we discuss a current attempt at applying the organi- 
zational principle Edelman calls Neuronal Group Selection to the 
control of a real, two-link robotic manipulator. We begin by moti- 
vating the need for an alternative to the position-control paradigm 
of classical robotics, and suggest that a possible avenue is to look 
at the primitive animal limb 'neurologically ballistic' control mode. 
We have been considering a selectionist approach to coordinating 
a simple perception-action task. 
1 MOTIVATION 
The majority of industrial robots in the world are mechanical manipulators m often 
arm-like devices consisting of some number of rigid links with actuators mounted 
where the links join that move adjacent links relative to each other, rotationally 
or translationally. At the joints there are typically also sensors measuring the 
relative position of adjacent links, and it is in terms of position that manipulators 
are generally controlled (a desired motion is specified as a desired position of the end 
effector, from which can be derived the necessary positions of the links comprising 
the manipulator). Position control dominates largely for historical reasons, rooted 
in bang-bang control: manipulators bumped between mechanical stops placed so as 
to enforce a desired trajectory for the end effector. 
Neuronal Group Selection Theory: A Grounding in Robotics 309 
1.1 SERVOMECHANISMS 
Mechanical stops have been superceded by position-controlling servomechanisms, 
negative feedback systems in which, for a typical manipulator with revolute joints, a 
desired joint angle is compared with a feedback signal from the joint sensor signalling 
actual measured angle; the difference controls the motive power output of the joint 
actuator proportionally. 
Where a manipulator is constructed of a number of links, there might be a ser- 
vomechanism for each joint. In combination, it is well known that joint motions 
can affect each other adversely, requiring careful design and analysis to reduce the 
possibility of unpleasant dynamical instabilities. This is especially important when 
the manipulator will be required to execute fast movements involving many or all 
of the joints. We are interested in such dynamic tasks, and acknowledge some suc- 
cessful servomechanistic solutions (see lAndersson 1988], who describes a ping pong 
playing robot), but seek an alternative that is not as computationally expensive. 
1.2 ESCAPING POSITION CONTROL 
In Nature, fast reaching and striking is a primitive and fundamental mode of con- 
trol. In fast, time-optimal, neurologically ballistic movements (such as horizontal 
rotations of the head where subjects are instructed to turn it as fast as possible, 
[Hannaford and Stark 1985]), muscle activity patterns seem to show three phases: 
a launching phase (a burst of agonist), a braking phase (an antagonist burst), and a 
locking phase (a second agonist burst). Experiments have shown (see [Wadman et 
aI. 1979]) that at least the first 100 mS of activity is the same even if a movement is 
blocked mechanically (without forewarning the subject), suggesting that the launch 
is specified from predetermined initial conditions (and is not immediately modified 
from proprioceptive information). With the braking and locking phases acting as 
a damping device at the end of the motion, the complete motion of the arm is 
essentially specified by the initial conditions m a mode radically differing from tra- 
ditional robot positional control. The overall coordination of movements might even 
seem naive and simple when compared with the intricacies of servomechanisms (see 
[Braitenberg 1989, Nahvi and Hashemi 1984] who discuss the crane driver's strategy 
for shifting loads quickly and time-optimally). 
The concept of letting insights (such as these) that can be gained from the biolog- 
ical sciences shape the engineering principles used to create artificial autonomous 
systems is finding fayour with a growing number of researchers in robotics. As it is 
not generally trivial to see how life's devices can be mapped onto machines, there is 
a need for some fundamental experimental work to develop and test the basic the- 
oretical and empirical components of this approach, and we have been considering 
various robotics problems from this perspective. 
Here, we discuss an experimental two-link manipulator that performs a simple ma- 
nipulation task m hitting a simple object perceived to be within its reach. The 
perception of the object specifies the initial conditions that determine an arm mo- 
310 Donnett and Smithers 
tion that reaches it. In relating initial conditions with motor currents, we have been 
considering a scheme based on Neuronal Group Selection Theory [Edelman 1987, 
Reeke and Edelman 1988], a theory of brain organization. We believe this to be 
the first attempt to apply selectionist ideas in a real machine, rather than just in 
simulation. 
2 NEURONAL GROUP SELECTION THEORY 
Edelman proposes Neuronal Group Selection (NGS) [Edelman 1978] as an organiz- 
ing principle for higher brain function m mainly a biological basis for perception m 
primarily applicable to the mammalian (and specifically, human) nervous system 
[Edelman 1981]. The essential idea is that groups of cells, structurally varied as a 
result of developmental processes, comprise a population from which are selected 
those groups whose function leads to adaptive behaviour of the system. Similar 
notions appear in immunology and, of course, evolutionary theory, although the 
effects of neuronal group selection are manifest in the lifetime of the organism. 
There are two premises on which the principle rests. The first is that the unit of 
selection is a cell group of perhaps 50 to 10,000 neurons. Intra-group connections 
between cells are assumed to vary (greatly) between groups, but other connections 
in the brain (particularly inter-group) are quite specific. The second premise is that 
the kinds of nervous systems whose organization the principle addresses are able to 
adapt to circumstances not previously encountered by the organism or its species 
[Edelman 1978]. 
2.1 THREE CENTRAL TENETS 
There are three important ideas in the NGS theory [Edelman 1987]. 
A first selective process (cell division, migration, differentiation, or death) 
results in structural diversity providing a primary repertoire of variant cell 
groups. 
A second selective process occurs as the organism experiences its environment; 
group activity that correlates with adaptive behaviour leads to differential 
amplification of intra- and inter-group synaptic strengths (the connectivity 
pattern remains unchanged). Prom the primary repertoire are thus selected 
groups whose adaptive functioning means they are more likely to find future 
use  these groups form the secondary repertoire. 
Secondary repertoires themselves form populations, and the NGS theory ad- 
ditionally requires a notion of teentry, or connections between repertoires, 
usually arranged in maps, of which the well-known retinotopic mapping of 
the visual system is typical. These connections are critical for they correlate 
motor and sensory repertoires, and lend the world the kind of spatiotemporal 
continuity we all experience. 
Neuronal Group Selection Theory: A Grounding in Robotics 311 
2.2 REQUIREMENTS OF SELECTIVE SYSTEMS 
To be selective, a system must satisfy three requirements IReeke and Edelman 1988]. 
Given a configuration of input signals (ultimately from the sensory epithelia, but for 
'deeper' repertoires mainly coming from other neuronal groups), if a group responds 
in a specific way it has matched the input [Edelman 1978]. The first requirement of 
a selective system is that it have a sufficiently large repertoire of variant elements to 
ensure that an adequate match can be found for a wide range of inputs. Secondly, 
enough of the groups in a repertoire must 'see' the diverse input signals effectively 
and quickly so that selection can operate on these groups. And finally, there must 
be a means for 'amplifying' the contribution, to the repertoire, of groups whose 
operation when matching input signals has led to adaptive behaviour. 
In determining the necessary number of groups in a repertoire, one must consider 
the relationship between repertoire size and the specificity of member groups. On 
the one hand, if groups are very specific, repertoires will need to be very large in 
order to recognize a wide range of possible inputs. On the other hand, if groups are 
not as discriminating, it will be possible to have smaller numbers of them, but in 
the limit (a single group with virtually no specificity) different signals will no longer 
be distinguishable. A simple way to quantify this might be to assume that each 
of N groups has a fixed probability, p, of matching an input configuration; then a 
typical measure [Edelman 1978] relating the effectiveness of recognition, r, to the 
number of groups is r = 1 - (1 - p)/v (see Fig. 1). 
log N 
Figure 1: Recognition as a Function of Repertoire Size 
From the shape of the curve in Fig. 1, it is clear that, for such a measure, below 
some lower threshold for N, the efficacy of recognition is equally poor. Similarly, 
above an upper threshold for N, recognition does not improve substantially as more 
groups are added. 
3 SELECTIONISM IN OUR EXPERIMENT 
Our manipulator is required to touch an object perceived to be within reach. This is 
a well-defined but non-trivial problem in motor-sensory coordination. Churchland 
proposes a geometrical solution for his two-eyed 'crab' [Churchland 1986], in which 
312 Donnett and Smithers 
eye angles are mapped to those joint angles (the crab has a two-link arm) that 
would bring the end of the arm to the point currently foveated by the eyes. Such 
a novel solution, in which computation is implicit and massively parallel, would be 
welcome; however, the crab is a simulation, and no heed is paid to the question of 
how the appropriate sensory-motor mapping could be generated for a real arm. 
Reeke and Edelman discuss an automaton, Darwin III, similar to the crab, but 
which by selectional processes develops the ability to manipulate objects presented 
to it in its environment IReeke and Edelman 1988]. The Darwin III simulation 
does not account for arm dynamics; however, Edelman suggests that the training 
paradigm is able to handle dynamic effects as well as the geometry of the problem 
[Edelman 1989]. We are attempting to implement a mechanical analogue of Darwin 
III, somewhat simplified, but which will experience the real dynamics of motion. 
ILl EXPERIMENTAL ARCHITECTURE AND HARDWARE 
The mechanical arrangement of our manipulator is shown in Fig. 2. The two links 
have agonist/antagonist tendon-drive arrangement, with an actuator per tendon. 
There are strain gauges in-line with the tendons. A manipulator 'reach' is specified 
by six parameters: burst amplitude and period for each of the three phases, launch, 
brake, and lock. 
forearm 
, tendons 
% 
% 
% 
elbow 
shoulder 
upper-arm 
 
 
I ' 
upper-arm   upper-arm 
left actuator right actuator 
forearm J ,,, forearm 
left actuator right actuator 
Figure 2: Manipulator Mechanical Configuration 
Neuronal Group Selection Theory: A Grounding in Robotics 313 
At the end of the manipulator is an array of eleven pyroelectic-effect infrared de- 
tectors arranged in a U-shaped pattern. The relative location of a warm object 
presented to the arm is registered by the sensors, and is converted to eleven 8-bit 
integers. Since the sensor output is proportional to detected infrared energy flux, 
objects at the same temperature will give a more positive reading if they are close 
to the sensors than if they are further away. Also, a near object will register on 
adjacent sensors, not just on the one oriented towards it. Therefore, for a single, 
small object, a histogram of the eleven values will have a peak, and showing two 
things (Fig. 3): the sensor 'seeing' the most flux indicates the relative direction 
of the object, and the sharpness of the peak is proportional to the distance of the 
object. 
(object distant 
and to the left) 
(object near and 
straight ahead) 
Figure 3: Histograms for Distant Versus Near Objects 
Modelled on Darwin III [Reeke and Edelman 1988], the architecture of the selec- 
tional perception-action coordinator is as in Fig. 4. The boxes represent repertoires 
of appropriately interconnected groups of 'neurons'. 
Darwin III responds mainly to contour in a two-dimensional world, analogous to the 
recognition of histogram shape in our system. Where Darwin III's 'unique response' 
network is sensitive to line segment lengths and orientations, ours is sensitive to the 
length of subsequences in the array of sensor output values in which values increase 
or decrease by the same amount, and the amounts by which they change; similarly, 
where Darwin III's 'generic response' network is sensitive to presence of or changes 
in orientation of lines, ours responds to the presence of the subsequences mentioned 
above, and the positions in the array where two subsequences abut. 
The recognition repertoires are reciprocally connected, and both connect to the mo- 
tor repertoire which consists of ballistic-movement 6-tuples. The system considers 
'touching perceived object' to be adaptive, so when recognition activity correlates 
with a given 6-tuple, amplification ensures that the same response will be favoured 
in future. 
314 Donnett and Smithers 
4 WORK TO DATE 
As the sensing system is not yet functional, this aspect of the system is currently 
simulated in an IBM PC/AT. The rest of the electrical and mechanical hardware 
is in place. The major difficulty currently faced is that the selectional system will 
Become computationally intensive on a serial machine. 
WORLD 
J s.,ampling ,of 
the worlo 
FEATURE FEATURE 
DETECTOR COR RELATOR 
COMBINATION 
RESPONSES 
(UNIQUE) 
classification 
couple 
I nettn$ ! COMBINATION 
RESPONSES 
 (GENERIC) 
motor map 
MOTOR 
ACTIONS 
Figure 4: Experimental Architecture 
For each possible ballistic 'reach', there must Be a representation for the 'reach 
6-tuple'. Therefore, the motor repertoire must become large as the dexterity of the 
manipulator is increased. Similarly, as the array of sensors is extended (resolution 
increased, or field of view widened), the classification repertoires must also grow. 
On a serial machine, polling the groups in the repertoires must be done one at 
a time, introducing a substantial delay between the registration of object and the 
actual touch, precluding the interception by the manipulator of fast moving objects. 
We are exploring possibilities for parallelizing the selectional process (and have for 
this reason constructed a network of processing elements), with the expectation that 
this will lead us closer to fast, dynamic manipulation, at minimal computational 
expense. 
Neuronal Group Selection Theory: A Grounding in Robotics 315 
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