Introduction
Biology is widely regarded as an important source of inspiration
for robotics. Animals are seen as offering working examples of
robust, embedded autonomous agents, and their neural circuitry,
and sensor and motor structures, are viewed as providing models
for designing similar components for robots. The premise of this
paper is that robotics can gain further insights from biology
by taking a closer interest in evolutionary history - the study
of the phylogenetic relationships between animals and the nature
of evolutionary change from one animal form to another. Invaluable
insights for robotics should be gained if we can understand how
complex neural circuits were derived from simpler ones, a question
that can only answered by investigating the evolutionary history
of nervous systems. Of course, brains and behaviour don't make
good fossils. This gives evolutionary neurobiologists the difficult
task of inferring the architecture of early nervous systems from
clues found in comparative and developmental studies. Only occasionally
does the fossil record provide any direct evidence of the behaviour
of ancient animals. Here, however, we are concerned with fossil
evidence of exactly this sort which has provided important insights
into the early evolution of animal spatial behaviour. Perhaps
unsurprisingly, we see that the first nervous systems generated
behaviour with some remarkable similarities to that of recent
behaviour-based robots.
Trace Fossils
The common ancestor of all modern, bilaterally symmetric, metazoans
(multicellular animals) was probably a roundish worm that lived
on the ocean floor during the Precambrian period 550 to 600 million
years ago (mya) (Valentine, 1994). Such creatures left no actual
fossils as they had virtually no hard body-parts. Fortunately,
however, they did leave a fossil record of sorts-some of the tracks,
trails, and burrows that these early invertebrates left in the
sediment were preserved forming what are now called trace fossils
(see Note 2). The commonest forms of trace fossil record foraging
trails left on, or just below, the ocean bed. The earliest traces
reflect simple 'scribbling' behaviours, with tracks that often
cross themselves, and indicate relatively crude and inefficient
foraging methods. By the early Cambrian however, 530-544 mya,
more regular trails appear that form spirals or 'meanders' that
loop back on themselves without crossing. Complex burrows also
begin to appear around this time with multiple levels and branches.
These changes reflect three important developments: an increase
in the diversity of animals, improvements in burrowing capabilities,
and most importantly, an increase in the complexity of neural
circuits. The beginning of the Cambrian is, of course, also recognised
as marking the origin of the contemporary metazoan phyla. The
Cambrian 'explosion' saw the rapid emergence, over the course
of ten to twenty million years, of a diversity of body forms equipped
with relatively complex sensory and nervous systems. Trace fossils
therefore represent our primary source of insights into the sequence
of evolutionary events that anticipated the appearance of the
modern fauna.
Computer models of trace fossils
In attempting to infer the perceptuo-behavioural capabilities
of the ancient animals that left fossil traces, it seems reasonable
to seek the simplest mechanisms that will reproduce the observed
patterns. Following Braitenberg's (1986) advice that "when
we analyze a mechanism we tend to overestimate its complexity"
the methodology of synthetic psychology-building model systems
that generate similar behaviours-seems an appropriate strategy.
In fact, synthetic approaches were applied some time ago to the
understanding of trace fossil behaviour, the computer simulations
of Raup and Seilacher (1969) standing out as an early, and rarely
acknowledged, example of what might now be termed Artificial Life
(see Note 3).
The most consistent fossilised foraging patterns were formed in
areas of the sea bed with an even distribution of food particles
in the sediment. This environment favors compact trails with maximal
coverage and minimal recrossing of existing tracks. Compared with
straight-line movement a meandering pattern also helps to keep
the animal within its preferred environment and may reduce the
likelihood of encountering predators. Raup and Seilacher based
their models of these trace fossils on a combination of three
reactive behaviours: thigmotaxis that makes the animal
stay close to previously formed tracks; phobotaxis that
causes it to avoid crossing existing tracks; and strophotaxis
that causes it to make 180° turns at various intervals.
Their simulations demonstrated that the interaction between these
behaviours is sufficient to generate the tightly coiled meandering
patterns characteristic of many foraging trails. Figures 1-3,
taken from Raup and Seilacher (1969), show some typical foraging
trails generated by their program together with the trace fossils
they were designed to emulate.
The principle of a complex behaviour pattern emerging from the
competitive interaction of a number of simple reactions is a characteristic
that Raup and Seilacher's work clearly shares with the behaviour-based
robotics approach of Brooks and others (e.g. Brooks, 1986, Maes,
1992). Indeed, the meandering behaviour generator is not unlike
a robot wall-following mechanism in which the object being followed,
rather than being a fixed contour, is the trail of disturbed sediment
generated by the animal's own movements.
Figures 1-3. Trace fossil meanders and comparable computer
output. (From Raup & Seilacher, 69)
Figure 2 has the interesting feature that the thigmotaxis response
is particularly weak-following a U-turn the animal takes some
time to restore contact with its earlier track. This relatively
inefficient foraging behaviour, a characteristic of early fossils,
is taken by Raup and Seilacher as evidence that thigmotaxis and
phobotaxis are "genetically distinct behavioural reactions".
Like wall-following in behaviour-based robots (see, e.g. Maes,
1992) the foraging meander is seen as an emergent pattern arising
from the environment-mediated interaction of two distinct behavioural
competences. Evidence for flexibility in the foraging behaviour
is demonstrated by the fact that the "lobes" of the
meandering patterns (the straight sections between turns) are
not always of constant length. Seilacher (1967) speculated that
this may sometimes be due to contact with an obstacle which triggers
a higher priority "avoid" behaviour. It therefore seems
likely that the activity of these animals was controlled by a
hierarchy of behavioural competences.
Seilacher (Seilacher, 1967; Raup and Seilacher, 1969; Seilacher,
1986) suggested a number of further models for different types
of foraging trace, and proposed that the variation between some
fossil traces, and their increased efficiency over the course
of evolution, can be modelled by manipulating key parameters of
the various component reactions. For instance, the turning radius
of the animal, the mean distance between tracks, and the relative
strengths of phobotaxis and thigmotaxis, can each be varied to
generate trails with different characteristic patterns and varying
degrees of foraging efficiency. One of the implications of these
studies is that evolution operated as much on the sensory and
motor systems used to implement the reactive behaviours as on
the behaviours themselves.
Robot models of trace fossils
A step towards enhancing the realism of trace fossil modelling,
that would introduce the constraints of genuine sensorimotor coordination,
is to model the generation of fossil trails using a mobile robot.
We are currently engaged in some preliminary investigations of
this nature using a customised Lego robot to generate and follow
trails across the laboratory floor. The sediment feeders we are
attempting to model probably used chemical and mechanical sensory
systems to detect and follow their tracks and burrows. However,
as a first approximation to these mechanisms we are using light
sensors to detect a trail of paper which is dispensed by the robot
as it moves. As illustrated in figure 4 the two arms of the robot
each carry a pair of light detectors.
Figure Four: The Lego Robot. The motorised dispenser on the
back of the robot releases a stream of paper when the robot is
moving. Two light detectors on each of the side arms measure reflected
light from the floor and control the thigmotaxis (toward track)
and phobotaxis (away from track) behaviours.
Figure 5 shows a simulation of behaviour generated by the
combination of phobotaxis (avoid track re-crossing) and thigmotaxis
(follow existing track). Thigmotaxis is triggered when the value
of the outermost sensor drops below a threshold, and phobotaxis
when the value of the innermost sensor goes above a threshold.
Without strophotaxis (U-turns) the robot's behaviour is simply
to spiral outwards following the pattern of its paper trail (compare
the right-hand picture of a spiralling trace fossil).
Figure 6 shows the effect of adding the strophotaxis (U-turn)
behaviour. Changes in the parameters of the behaviours, particularly
the relative strengths of thigmotaxis and phobotaxis, generate
meanders of varying compactness. In the bottom right picture an
avoid behaviour has been added that overrides the meandering behaviour
in the vicinity of an obstacle.
Figure 6: Meanders generated using thigmotaxis, phobotaxis,
strophotaxis, and (bottom-right only) avoid obstacle behaviours.
A 'Cambrian explosion' of behaviour-based robots?
The value of these demonstrations perhaps lies less in discovering
that relatively simple mechanisms can be used to implement robot
'foraging' trails and more in simply pointing out the similarities
between the sensorimotor behaviour of ancient animals and that
of simple reactively-controlled behaviour-based robots. This similarity
locates the behaviour of such robots at a grade similar to animals
of the early Cambrian (see Note 4). This period of perhaps less
than twenty million years saw the explosive development of many
different body forms and complex nervous systems. The organisms
of this fauna achieved a great diversity of methods of locomotion,
had an abundance of different sensory mechanisms including compound
eyes, and possessed a wide range of behavioural repertoires including
predation (Conway Morris, 1989; Miklos, 1993). In other words,
many of these animals had very mobile and active lifestyles, were
capable of effective sensing in different modalities, and exhibited
complex and appropriate reactions to varied stimuli. Comparative
and paleo-neurobiological studies indicate that 'groundplans'
for the neural circuitry of the different phyla were established
in this period that placed significant constraints on subsequent
evolution. For instance, the basic pattern of insect nervous systems
was probably present in Arthropod ancestors of the Cambrian, and
has since shown primarily quantitative rather than qualitative
change (see Note 5). It also appears likely that the basic plan
for the vertebrate nervous system was established at an early
stage (Jerison, 1973; Stahl, 1977; Sarnat and Netsky, 1981; Hodos,
1982), and within 100 million years of the initial Cambrian explosion.
Miklos (1993) who has referred to this period of rapid evolution
as a "big bang" in the evolution of complex nervous
systems, suggests that:
"Complex brains were unlikely to have been painstakingly
'wired-up' synapse by synapse over hundreds of millions of years.
We are faced with the exciting prospect that nervous systems can
be constructed rapidly".
That the evolution of nervous systems in the early Cambrian metazoa
proceeded at such a cracking pace should give encouragement to
the designers of robot control systems. However, a number of cautions
should be entertained with regard to the prospect of an imminent
'explosion' of behaviour-based robots. First, we should recognise
that robotics currently lacks building materials with the versatility
and intelligence of the eukaryotic cell (itself the outcome of
three billion years of evolution). Second, much work in the design
of robot control circuitry is not far above the level of specifying
individual synapses - such methods can expect slow progress and
bear few similarities with the sophisticated development processes
that control gene expression in neural circuitry (see Note 6).
Finally, we might consider whether an explosion of behaviour-based
robots has already occurred in that a wide variety of platforms
that exhibit mobility and reactive behaviour have already been
built and demonstrated. This is not to suggest that we have achieved
the morphological or neural complexity of the early Cambrian fauna,
but we may not be far off replicating their grade of intelligent
behaviour.
Conclusion
Trace fossil research demonstrates that the evolutionary history
of early invertebrates contains interesting parallels with current
work in behaviour-based robotics. This observation encourages
the belief that a close examination of early metazoan evolution
could provide further valuable insights for an evolution-inspired
robot design process. For instance, Brooks Brooks, 1986 has proposed
building complex robot control systems by progressively incrementing
an initially simple system with extensive testing and debugging
of each intermediate architecture. This 'layering' process has
been offered as roughly analogous to the processes of natural
evolution (Brooks, 1991). An examination of the evidence concerning
the sequence of evolutionary events that produced complex nervous
systems should allow us to evaluate and refine this analogy, and
determine strategies for robot design that might more accurately
reflect the evolutionary history of intelligent life on earth.
Notes
1. This Paper appeared as: Prescott, T.J. & Ibbotson, C. (1997) "The early evolution of spatial behaviour: robot models of trace fossils" in Proc. AISB workshop on "Spatial Reasoning in Mobile Robots and Animals", Manchester 1997. Technical Report Series, Department of Computer Science, Manchester University, ISSN 1361 - 6161. Report number UMCS-97-4-1.
3. For a contemporary approach to computer modelling of trace fossils look here.
4. Seilacher (1967, 1986) has suggested that the complexity of trace fossil behaviour increased gradually for 100 million years or so after the start of the Cambrian period, however, recent finds have caused this conclusion to be revised and it is now thought likely that diversity increased during the Cambrian radiation and has been relatively constant since (Crimes, 1992; Raff, 1996).
5. Edwards (Edwards, 1977; Edwards and Palka, 1991) describes the evolution of the nervous systems of insects as having been "astonishingly conservative", despite remarkable variations in body plans, with perhaps the most significant trend being towards miniaturization of the neural circuitry in some species, and the most variation being in the relative volume of sensory processing.
6. Modellers and robot-builders are now beginning to take an interest
in simulating the processes of neural development (e.g. Dellaert
and Beer, 1996). Progress in the area could have important consequences
for the automated construction and evolution of complex control
systems.
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