[Paleopsych] BBS: Webb, B (2001) Can robots make good models of biological behaviour?

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Webb, B (2001) Can robots make good models of biological behaviour?

  Barbara Webb
  Centre for Computational and Cognitive Neuroscience
  Department of Psychology
  University of Stirling
  Stirling FK9 4LA
  Scotland, U.K.
  [1]b.h.webb at stir.ac.uk


How should biological behaviour be modelled? A relatively new approach
is to investigate problems in neuroethology by building physical robot
models of biological sensorimotor systems. The explication and
justification of this approach are here placed within a framework for
describing and comparing models in the behavioural and biological
sciences. First, simulation models - the representation of a
hypothesis about a target system - are distinguished from several
other relationships also termed 'modelling' in discussions of
scientific explanation. Seven dimensions on which simulation models
can differ are defined and distinctions between them discussed:
  1. Relevance: whether the model tests and generates hypotheses
  applicable to biology.
  2. Level: the elemental units of the model in the hierarchy from
  atoms to societies.
  3. Generality: the range of biological systems the model can
  4. Abstraction: the complexity, relative to the target, or amount of
  detail included in the model.
  5. Structural accuracy: how well the model represents the actual
  mechanisms underlying the behaviour.
  6. Performance match: to what extent the model behaviour matches the
  target behaviour
  7. Medium: the physical basis by which the model is implemented

No specific position in the space of models thus defined is the only
correct one, but a good modelling methodology should be explicit about
its position and the justification for that position. It is argued
that in building robot models biological relevance is more effective
than loose biological inspiration; multiple levels can be integrated;
that generality cannot be assumed but might emerge from studying
specific instances; abstraction is better done by simplification than
idealisation; accuracy can be approached through iterations of
complete systems; that the model should be able to match and predict
target behaviour; and that a physical medium can have significant
advantages. These arguments reflect the view that biological behaviour
needs to be studied and modelled in context, that is in terms of the
real problems faced by real animals in real environments.


models; simulation; animal behaviour; neuroethology; robotics;
realism; levels.

[me3.jpg] Barbara Webb joined the Psychology Department at Stirling
  University in January 1999. Previously she lectured at the University
of Nottingham (1995-1998) and the University of Edinburgh (1993-1995).
  She recieved her Ph.D. (in Artificial Intelligence) from the
University of Edinburgh in 1993, and her B.Sc. (in Psychology) from
  the University of Sydney in 1988.

   1. Introduction

'Biorobotics' can be defined as the intersection of biology and
robotics. The common ground is that robots and animals are both
moving, behaving systems; both have sensors and actuators and require
an autonomous control system that enables them to successfully carry
out various tasks in a complex, dynamic world. In other words "it was
realised that the study of autonomous robots was analogous to the
study of animal behaviour" p.60 (Dean, 1998), hence robots could be
used as models of animals. As summarised by Lambrinos et al. (1997) et
al "the goal of this approach is to develop an understanding of
natural systems by building a robot that mimics some aspects of their
sensory and nervous system and their behaviour" (p.185).

Dean (op. cit.) reviews some of this work, as do Meyer (1997), Beer et
al. (1998), Bekey (1996), and Sharkey & Ziemke (1998), although the
rapid growth and interdisciplinary nature of the work make it
difficult to comprehensively review. Biorobotics will here be
considered as new methodology in biological modelling, rather than as
a new 'field' per se. It can then be discussed directly in relation to
other forms of modelling. Rather than vague justification in terms of
intuitive similarities between robots and animals, the tenets of the
methodology can be more clearly stated and a basis for comparison to
other approaches established. However, a difficulty that immediately
arises is that a "wide divergence of opinion exists concerning the
proper role of models" p. 597 (Reeke & Sporns, 1993) in biological

For example, the level of mechanism that should be represented in the
model is often disputed. Cognitivists criticise connectionism for
being too low level (Fodor & Pylyshyn, 1988), while neurobiologists
complain that connectionism abstracts too far from real neural
processes (Crick, 1989). Other debates address the most appropriate
means for implementing models. Purely computer-based simulations are
criticised by advocates of sub-threshold transistor technology (Mead,
1989) and by supporters of real-world robotic implementations (Brooks,
1986). Some worry about oversimplification (Segev, 1992) while others
deplore overcomplexity (Maynard Smith, 1974; Koch, 1999). Some set out
minimum criteria for good models in their area (e.g. Pfeifer, 1996;
Selverston, 1993); others suggest there are fundamental trade-offs
between desirable model qualities (Levins, 1966).

The use of models at all is sometimes disputed, on the grounds that
detailed models are premature and more basic research is needed. Croon
& van de Vijver (1994) argue that "Developing formalised models for
phenomena which are not even understood on an elementary level is a
risky venture: what can be gained by casting some quite gratuitous
assumptions about particular phenomena in a mathematical form?" p.4-5.
Others argue that "the complexity of animal behaviour demands the
application of powerful theoretical frameworks" (Barto, 1991, p.94)
and "nervous systems are simply to complex to be understood without
the quantitative approach that modelling provides" (Bower, 1992,
p.411). More generally, the formalization involved in modelling is
argued to be an invaluable aid in theorising - "important because
biology is full of verbal assertions that some mechanism will generate
some result, when in fact it wont" (Maynard Smith, 1988, p.231).

Beyond the methodological debates, there are also meta-arguments
regarding the role and status of models in both pure and applied
sciences of behaviour. Are models essential to gaining knowledge or
just convenient tools? Can we ever really validate a model (Oreskes,
et al, 1994)? Is reification of models mistaken i.e. can a model of a
process ever be a replica of that process (Pattee, 1989; Webb, 1991)?
Do models really tell us anything we didnt already know?

In what follows a framework for the description and comparison of
models will be set out in an attempt to answer some of these points,
and the position of biorobotics with regard to this framework will be
made clear. Section 2 will explicate the function of models, in
particular to clarify some of the current terminological confusion,
and define 'biorobotic' modelling. Section 3 will describe different
dimensions that can be used to characterise biological models, and
discuss the relationships between them. Section 4 will lay out the
position of robot models in relation to these dimensions, and discuss
how this position reflects a particular perspective on the problems of
explaining biological behaviour.

   2. The process of modelling

2.1 The "model muddle" (Wartofsky, 1979)

Discussions of the meaning and process of modelling can be found: in
the philosophy of science e.g. Hesse (1966), Harre (1970b),
Leatherdale (1974), Bunge (1973), Wartofsky (1979), Black (1962) and
further references throughout this paper; in cybernetic or systems
theory, particularly Zeigler (1976); and in textbooks on methodology -
recent examples include Haefner (1996), Giere (1997), and Doucet &
Sloep (1992). It also arises as part of some specific debates about
approaches in biology and cognition: in ecological modelling e.g.
Levins (1966) and Orzack & Sober (1993); in cognitive simulation e.g.
Fodor (1968), Colby (1981), Fodor & Pylyshyn (1988), Harnad (1989); in
neural networks e.g. Sejnowski et al (1988), Crick (1989); and in
Artificial Life e.g. Pattee (1989), Chan & Tidwell (1993). However the
situation is accurately summed up by Leatherdale (1974): "the
literature on models displays a bewildering lack of agreement about
what exactly is meant by the word model in relation to science"
(p.41). Not only model but most of the associated terms - such as
'simulation', 'representation', 'realism', 'accuracy', 'validation'
have come to be used in a variety of ways by different authors.
Several distinct frameworks for describing models can be found, some
explicit and some implicit, most of which seem difficult to apply to
real examples of model building. Moreover many authors seem to present
their usage as the obvious or correct one and thus fail to spell out
how it relates to previous or alternative approaches. Chao in 1960
noted 30 different, sometimes contradictory, definitions of 'model'
and the situation has not improved.

There does seem to be general agreement that modelling involves the
relationship of representation or correspondence between a (real)
target system and something else(1). Thus "A model is a representation
of reality" Lamb, 1987, p.91) or "all [models] provide representations
of the world" (Hughes, 1997, p. 325). What might be thought
uncontroversial examples are: a scale model of a building which
corresponds in various respects to an actual building; and the
billiard-ball model of gases, suggesting a correspondence of behaviour
in microscopic particle collisions to macroscopic object collisions.
Already, however, we find some authors ready to dispute the use of the
term model for one or other of these examples. Thus Kaplan (1964)
argues that purely sentential descriptions like the billiard-ball
example should not be called models; whereas Kacser (1960) maintains
that only sentential descriptions should be called models and physical
constructions like scale buildings should be called analogues; and
Achinstein (1968) denies that scale buildings are analogies while
using model for both verbal descriptions and some physical objects.

A large proportion of the discussion of models in the philosophy of
science concerns the problem that reasoning by analogy is not
logically valid. If A and A* correspond in factors x[1],,x[n], it is
not possible to deduce that they will therefore correspond in factor
x[n+1]. Underdetermination is a another aspect of essentially the same
problem if two systems behave the same, it is not logically valid to
conclude the cause or mechanism of the behaviour is the same; so a
model that behaves like its target is not necessarily an explanation
of the targets behaviour. These problems are sometimes raised in
arguments about the practical application of models, e.g. Oreskes et
al. (1994) use underdetermination to argue that validation of models
is impossible. Weitzenfeld (1984) suggests that a defence against this
problem can be made by arguing that if there is a true isomorphism
between A and A*, the deduction is valid, and the problem is only to
demonstrate the isomorphism. Similar reasoning perhaps explains the
frequently encountered claim that a model is "what mathematicians call
an isomorphism" (Black, 1962, p.222)a one to one mapping - of relevant
aspects (Schultz & Sullivan, 1972), or essential structure (Kaplan,
1964). Within cybernetic theory one can find formal definitions of
models (e.g. Klir & Valach, 1965) that require there to be a complete
isomorphic or homomorphic mapping of all elements of a system,
preserving all relationships.

However, this is not helpful when considering most actual examples of
models (unless one allows there "to be as many definitions possible to
isomorphism as to model" Conant & Ashby, 1991, p.516). In the vast
majority of cases, models are not (mathematical) isomorphisms, nor are
they intended to be. Klir and Valach (op. cit.) go on to include as
examples of models "photos, sculptures, paintings, filmseven literary
works" (p.115). It would be interesting to know how they intend to
demonstrate a strict homomorphism between Anna Karenina and "social,
economic, ethical and other relations" (op. cit.) in 19^th century
Russia. In fact it is just as frequently (and often by the same
authors) emphasised that a model necessarily fails to represent
everything about a system. For example Black (1962) goes on to warn of
"risks of fallacies of inference from inevitable irrelevancies or
distortions in the model" (p.223) but if there is a true isomorphism,
how can there be such a risk? A partial isomorphism is an oxymoron;
and more to the point, cannot suffice for models to be used in valid
deduction. Moreover this approach to modelling obscures the fact that
the purpose in modelling is often to discover what are the 'relevant
features' or 'essential structures', so model usage cannot depend on
prior knowledge of what they are to establish the modelling

2.2 What use are models?

"There are things and models of things, the latter being also things,
but used in a special way" (Chao, 1960, p.564)

Models are intended to help us deal in various ways with a system of
interest. How do they fulfil this role? It is common to discuss how
they offer a convenient/cost-effective/manageable/safe substitute for
working on or building the real thing. But this doesnt explain why
working on the model has any relevance to the real system, or provide
some basis by which relevance can be judged i.e. what makes a model a
useful substitute? It is easier to approach this by casting the role
of modelling as part of the process of explanation and prediction
described in the following diagram:

Figure 1: Models and the process of explanation

This picture can be regarded as an elaboration of standard textbook
illustrations of either the 'hypothetico-deductive' approach or the
'semantic' approach to science (see below). To make each part of the
diagram clear, consider an example. Our target - selected from the
world - might be the human cochlea and the human behaviour of pitch
perception. Our hypothesis might be that particular physical
properties of the basilar membrane enable differently positioned hair
cells to respond to different sound frequencies. One source of this
idea may be the Fourier transform, and associated notion of a bank of
frequency filters as a way of processing sound. To see what is
predicted by the physical properties of the basal membrane we might
build a symbolic simulation of the physical properties we think
perform the function, and run it using computer technology, with
different simulated sounds to see if it produces the same output
frequencies as the cochlea (in fact Bekesy first investigated this
problem using rubber as the technology to represent the basilar
membrane). We could interpret the dominant output frequency value as a
pitch percept and compare it to human pitch perception for the same
waveforms: insofar as it fails to match we might conclude our
hypothesis is not sufficient to explain human pitch perception. Or as
Chan & Tidwell (1993) concisely summarise this process, we theorise
that a system is of type T, and construct an analogous system to T, to
see if it behaves like the target system.

I have purposely not used the term 'model' in the above description
because it can be applied to different parts of this diagram.
Generally, in this paper, I take 'modelling' to correspond to the
function labelled 'simulation': models are something added to the
'hypothesis - prediction - observation' cycle merely as "prostheses
for our brains" (Milinski, 1991). That is, modelling aims to make the
process of producing predictions from hypotheses more effective by
enlisting the aid of an analogical mechanism. A mathematical model
such as the Hodgkin-Huxley equations sets up a correspondence between
the processes in theorised mechanism the ionic conductances involved
in neural firing and processes defined on numbers such as integration.
We can more easily manipulate the numbers than the chemicals so the
results of a particular configuration can be more easily predicted.
However limitations in the accuracy of the correspondence might
compromise the validity of conclusions drawn.

However, under the 'semantic' approach to scientific explanation
(Giere, 1997) the hypothesis itself is regarded as a model, i.e. it
specifies a hypothetical system of which the target is supposed to be
a type. The process of prediction is then described as demonstration
(Hughes, 1997) of how this hypothetical system should behave like the
target. Demonstration of the consequences of the hypothesis may
involve another level of representation in which the hypothesis is
represented by some other system, also called a model. This system may
be something already 'found' - an analogical or source model - or
something built - a simulation model (Morgan, 1997). Moreover the
target itself can also be considered a 'model', in so far as it
involves abstraction or simplification in selecting a system from the
world (Cartwright, 1983). This idea perhaps underlies Gordon's (1969)
definition of model: "we define a model as the body of information
about a system gathered for the purpose of studying the system" (p.5).

2.3 Theories, models, simulations and sources

While the usage of 'model' to mean the target is relatively rare, it
is common to find 'model' used interchangeably with hypothesis and
theory' (2): even claims that "A model is a description of a system"
(Haefner, 1996 p.4); or "A scientific model is, in effect, one or a
set of statements about reality"(Ackoff, 1962, p.109). This usage of
'model' is often qualified, most commonly as the theoretical model,
but also as the conceptual model (Rykiel, 1996; Ulinski, 1999; Brooks
& Tobias, 1996), sentential model (Harre, 1970a), abstract model
(Spriet & Vansteenkiste, 1982), or, confusingly, the real model (Maki
& Thompson, 1973) or base model (Zeigler, 1976). The tendency to call
the hypothesis a model seems to be linked to how formal or precise is
the specification it provides (Braithwaite, 1960), as hypotheses can
range from a vague qualitative predictions to Zeiglers (1976) notion
of a well-described base model, which involves defining all input,
output and state variables, and their transfer and output functions,
as a necessary prior step to simulation. The common concept of the
theoretical model is that of a hypothesis that describes the
components and interactions thought sufficient to produce the
behaviour: "the actual building of the model is a separate step"
(Brooks & Tobias, 1996, p.2).

This separate step is implementation (3) as a simulation, which
involves representing the hypothesis in some physical instantiation -
taken here in its widest sense i.e. including carrying out
mathematical calculations or running a computer program, as well as
more obviously 'physical' models. But as Maki & Thompson (1973) note:
"in many cases it is very difficult to decide where the real model
[the hypothesis] ends and the mathematical model [the simulation]
begins" (p.4). Producing a precise formulation may have already
introduced a number of 'technological' factors that are not really
part of the hypothesis, in the sense that they are there only to make
the solution possible, not because they are really considered to be
potential components or processes in the target system. Grice (cited
in Cartwright, 1983) called these "properties of convenience" and
Colby (1981) makes this a basis for distinguishing models from
theories: all statements of a theory are intended to be taken as true
whereas some statements in a model are not.

Simulation (4) is intended to augment our ability to deduce
consequences from the assumptions expressed in the hypothesis: "a
simulation program is ultimately only a high speed generator of the
consequences that some theory assigns to various antecedent
conditions" (Dennett, 1979, p.192); "modelshelpby making predictions
of unobvious consequences from given assumptions" (Reeke & Sporns,
1993, p.599). Ideally, a simulation should clearly and accurately
represent the whole of the hypothesis and nothing but the hypothesis,
so conclusions based on the simulation are in fact correct conclusions
about the hypothesis. However, a simulation must also necessarily be
precise in the sense used above, that is, all components and processes
must be fully specified for it to run. The formalization imposed by
implementation usually involves elaborations or simplifications of the
hypothesis to make it tractable, which may have no theoretical
justification. In other words, as is generally recognised, any actual
simulation contains a number of factors that are not part of the
'positive analogy' between the target and the model.

In the philosophy of science, discussion of 'simulation' models has
been relatively neglected. Rather, as Redhead (1980) points out the
extensive literature on models in science is mostly about modelling in
the sense of using a source analogy. A source (5)  is a pre-existing
system used in devising the hypothesis. For example, Amit (1989)
describes how concepts like 'energy' from physics can be used in an
analogical sense to provide powerful analysis tools for neural
networks, without any implication that a 'physics level' explanation
of the brain is being attempted. Though traditionally the source has
been thought of as another physical system (e.g. a pump as the source
of hypotheses for the functioning of the heart) it is plausible to
consider mathematics to be a source. That is, mathematical knowledge
provides a pre-existing set of components and operations we can put in
correspondence to the hypothesised components and operations of our
target. Mathematics just happens to be a particularly widely
applicable analogy (Leatherdale, 1974).

It is worth explicitly noting that the source is not in the same
relation to the hypothesis as the technology, i.e. what is used to
implement the hypothesis in a simulation. Confusion arises because the
same system can sometimes be used both as a source and as a
technology. Mathematics is one example, and another of particular
current relevance is the computer. The computer can be used explicitly
as a source to suggest structures and functions that are part of the
hypothesis (such as the information processing metaphor in cognition),
or merely as a convenient way of representing and manipulating the
structures and functions that have been independently hypothesised. It
would be better if terms like computational neuroscience that are
sometimes used strongly in the source sense computation as an
explanatory notion for neuroscience - were not so often used more
loosely in the technology sense: "not every research application that
models neural data with the help of a computer should be called
computational neuroscience" (Schwartz, 1990, p.x). Clarity is not
served by having (self-labelled) computational neuroethologists e.g.
Beer (1990) and Cliff (1991) who apparently reject computation as an
explanation of neuroethology.

2.4 Biorobotic models

Figure 1 suggests several different ways in which robots and animals
might be related through modelling. First, there is a long tradition
in which robots have been used as the source in explaining animal
behaviour. Since at least Descartes (1662), regarding animals as
merely complex machines, and explaining their capabilities by analogy
with man-made systems has been a common strategy. It was most
explicitly articulated in the cybernetic approach, which, in Weiner's
subtitle to "Cybernetics" (1948), concerned "control and communication
in the animal and the machine". It also pertains to the information
processing approaches common today, in which computation is the source
for explaining brains. Much work in biomechanics involves directly
applying robot-derived analyses to animal capacities, for example
Walker (1995) attempts "to analyse the strengths and weaknesses of the
ancient design of racoon hands from the point of view of robotics"

Second, animals can be regarded as the source for hypotheses in robot
construction. This is one widely accepted usage of the term
biorobotics sometimes called 'bio-mimetic' or 'biologically-inspired'
robotics. For example Ayers et al. (1998) suggest "the set of
behavioural acts that a lobster or lamprey utilises in searching for
and identifying prey is exactly what an autonomous underwater robot
needs to perform to find mines". Pratt & Pratt (1998b) in their
construction of walking machines "exploit three different natural
mechanisms", the knee, ankle and swing of animal legs to simplify
control. The connection to biology can range from fairly exact copies
of mechanisms e.g. Franceschini et al.'s (1992) electronic copy of the
elementary motion detection circuitry of the fly, to adopting some
high level principles e.g. using the ethological concept of 'releasing
stimuli' to control a robot via simple environmental cues (Connell,
1990) or the approach described in Mataric (1998).

For the following discussion, however, I wish to focus on a third
relationship: robots used as simulations of animals, or how "robots
can be used as physical models of animals to address specific
biological questions" (Beer et al., 1998, p.777). The potential for
building such models has increased enormously in recent years due to
advances in both robot technology and neuroethological understanding,
allowing "biologists/ethologists/neuroscientists to use robots instead
of purely computational models in the modelling of living systems"
Sharkey & Ziemke, 1998, p.164)

The following criteria have been adopted for the inclusion of work in
what follows as biorobotic modelling, to avoid the necessity of
discussing an unmanageably large body of work in robotics and
biological modelling:

It must be robotic: the system should be physically instantiated and
have unmediated contact with the external environment; the
transduction is thus constrained by physics. The intention is to rule
out purely computer-based models (i.e. where the environment as well
as the animal is represented in the computer); and also computer
sensing systems that terminate in descriptions rather than actions.
This somewhat arbitrarily discounts verbal behaviour (e.g. visual
classification) as sufficient; but to do so is consistent with most
peoples understanding of 'robotic'.

It must be biological: one aim in building the system should be to
address a biological hypothesis or demonstrate understanding of a
biological system. The intention is to rule out systems that might use
some biological mechanisms but have no concern about altering them in
ways that make it a worse representation e.g. industrial robot arms,
most computer vision, most neural net controllers. It also rules out
much of the behaviour-based approach in robotics which uses
"algorithms specifying robot behaviours that have analogy to
behaviours of life-form[s]" (Yamaguchi, 1998, p.3204) but makes no
serious attempt to compare the results to natural systems. Probably
the largest set of borderline cases thus excluded is the use of
various learning mechanisms for robot behaviour, except those
specifically linked to animal behavioural or physiological studies.

There is already a surprisingly substantial amount of work even
applying these criteria. The earliest examples come from mid-century,
where theories of equilibrium (Ashby, 1952 learning (Shannon, 1951)
and sensorimotor control (Grey Walter, 1961) were tested by building
animal machines of various kinds - a number of other early examples
are discussed in Young (1969). Current work tends to be more focused
on specific biological systems, and ranges across the animal kingdom,
from nematodes to humans. Table 1 lists a selection of recent studies,
and to illustrate the approach I will describe three examples here in
more detail.
  1. A robot model of rat hippocampus: Burgess et al. (1997,1998, 2000)
  have presented a model of the rat hippocampus implemented on a
  robot. "The use of a robot ensures the realism of the assumed
  sensory inputs and enables true evaluation of the navigational
  capability" (Burgess et al., 1997, p.1535). The robot uses
  edge-filtering on a camera image to sense the distance of walls in
  its environment and a combination of visual and odometric
  information to link the distance to the allocentric direction of
  the walls, rotating in place to cover a sufficient field of view.
  They argue that these mechanisms "provide realistic
  simulationsince the rat's visual and odometric system appear to be
  relatively unsophisticated" (Burgess et al., 2000, p.306). This
  sensory information is encoded computationally by sensory 'cells'
  that effectively have 'receptive fields' for different directions
  and distances of walls. These feed to an array of 'entorhinal
  cells' which combine connections from sensory cells. These connect
  to the layer of 'place cells' with the connection pattern
  modifiable by competitive learning: thus representing the learnt
  place dependent activity of cells observed in rat hippocampus.
  These cells further connect to a small number of goal cells, which
  also receive input from 'head direction' cells. By Hebbian
  learning of these connections when a goal is encountered, the
  network forms a representation which can be used to guide the
  robot's movement back to a goal position from novel locations.
  "[To] maintain close contact with the experimental situations in
  which the place cell data constraining the model was collected,
  the robot was tested in simple rectangular environments" (Burgess
  et al., 2000, p.306). The results show the robot is capable of
  good self localisation while wandering in the environment and can
  reliably return to the goal position from novel locations. The
  effects of changing the environment (e.g. the proportions of the
  rectangle, or adding a new barrier) on the place cell
  representation and the search behaviour can be compared to the
  results in rats; some predictions from the model have been
  supported (Burgess et al., 2000). They further predict that cells
  with 'receptive fields' for direction and distance of barriers
  will be found within or upstream of the entorhinal cortex, but
  this is yet to be confirmed.
  2. A robot model of desert ant navigation: The impressive homing
  capabilities of the desert ant Catyglyphis have been the subject
  of long study (Wehner, 1994). Several aspects of this behaviour
  have been investigated in robot models that operate in the same
  Sahara environment (Lambrinos et al., 1997; Moller et al., 1998;
  Lambrinos et al., 2000). Insects can use the polarisation pattern
  of the sky as a compass, with three 'POL' neurons in the brain
  integrating the response from crossed-pairs of filters at three
  different orientations. This sensor-neural morphology has been
  duplicated in the robot. Two different models for extracting
  compass direction were considered: a 'scanning' mechanism that
  rotates to find a peak response which indicates the solar meridian
  (as had been previously proposed for the ant); and a novel
  'simultaneous' mechanism that calculates the current direction
  from the pattern of neural output. The 'simultaneous' mechanism
  was substantially more efficient as the robot (or ant) does not
  need to rotate 360 degrees each time it wants to refer to the
  compass. This compass was successfully used in a path integration
  algorithm, reducing the error in the robot's return to its
  starting location.
  A further development of the robot allowed the testing of
  hypotheses about landmark navigation. A conical mirror placed
  above a camera enabled the robot to get a 360 degree view of the
  horizon comparable to that of the ant. The 'snapshot' model
  proposed by Cartwright & Collett 1983 was implemented first: this
  matches the landmarks in a current view with a stored view, to
  create a set of vectors whose average is a vector pointing
  approximately in the home direction. The ability of this model to
  return the robot to a location was demonstrated in experiments
  with the same black cylinders as landmarks as were used for the
  ant experiments. Further, a simplification of the model was
  proposed, in which the robot (or animal) only stores an 'average
  landmark vector' rather than a full snapshot, and it was shown
  that the same homing behaviour could be reproduced. To provide
  "insights as to how the visual homing might be implemented in
  insect brains" (p.243) , it has recently been implemented in
  analog electronic hardware (Moller, 2000) and successfully tested
  on a robot in reproductions of experiments performed on bees in
  which landmarks are moved or removed.
  3. A robot model of human motor control: Schaal & Sternad (2001)
  present a comparison of human and robot behaviour to analyse the
  control of motor trajectories. This is used to addressed a
  critical question - does the apparent '2/3 power law' relating
  endpoint velocity to path curvature in human movement represent an
  explicit parameter implemented directly in the nervous system, or
  is it merely the by-product of other control mechanisms? The study
  measured humans making cyclic drawing motions, and modelled the
  behaviour using a 7 degree-of-freedom anthropomorphic robot arm,
  with PID control of joint movements based on simple sinusoidal
  target trajectories. The frequency, amplitude and phase of the
  sinusoids were estimated from measurements on the human subjects.
  They found that "As in the human data, for small perimeter values
  [the 2/3 law] was produced quite accurately, but, as in the human
  subjects, the same deterioration of power law fits were apparent
  for increasing pattern size" (p.67). Moreover they could explain
  these deviations as a consequence of non-linearities in the
  kinematic transform from joint control to end-effector
  trajectories, and explain the power law as emergent from
  mechanisms for ensuring smooth movement in joint space.

It can thus be seen that useful results for biology have been already
been gained from robotic modelling. But it is still pertinent to ask:
Why use robots to simulate animals? How does this methodology differ
from alternative approaches to modelling in biology? To answer these
questions it is necessary to understand the different ways in which
models can vary, which will now be examined.

3. Dimensions for describing models

Figure 2: Dimensions for describing models

Figure 2 presents a seven-dimensional view of the 'space' of possible
biological models. If the 'origin' is taken to be using the system
itself as its own model (to cover the view expressed by Rosenblueth &
Wiener (1945) as "the best material model of a cat is another, or
preferably the same, cat" p.316) then a model may be distanced from
its target in terms of abstraction, approximation, generality or
relevance. It may copy only higher levels of organisation, or
represent the target using a very different material basis, or only
roughly reproduce the target's behaviour. Exactly what is meant here
by each of listed dimensions, and in what ways they are (and are not)
related will be discussed in detail in what follows. They are
presented as an attempt to capture, with a manageable number of terms,
as much as possible of the variation described and discussed in the
literature on modelling, and to separate various issues that are often

Though it is generally more popular in the literature to classify
models into types (see for example the rather different taxonomies
provided by Achinstein (1968), Haefner, (1996), Harre (1970b) and
Black (1962)), there are precedents for this kind of dimensional
description of models. Some authors attempt to use a single dimension.
For example Shannon (1975) presents a diagram of models situated on a
single axis that goes from exact physical replicas at one end to
highly abstracted symbolic systems at the other. By contrast, Schultz
& Sullivan (1972) present a list of some 50-odd different dimensions
by which a model may be described. One set of dimensions widely
discussed in ecological modelling was proposed by Levins in 1966. He
suggested that models could vary in realism, precision and generality
(in his 1993 reply to Orzack & Sober's (1993) critique he notes that
this was not intended to be a formal or exhaustive description).
Within the systems approach to modelling the most commonly discussed
dimensions are complexity, detail and validity as well as more
practical or pragmatic considerations such as cost (e.g. Rothenberg
(1989) includes cost-effectiveness as part of his definition of
simulation). Brooks & Tobias (1996) discuss some proposed methods for
measuring these factors, and also point out how some of the
connections between these factors are not as simple as seems to be
generally thought.

Many of the debates about 'appropriate' biological simulation assume
that there are strict relations between certain aspects of modelling.
Neural nets are said to be more accurate than symbol processing models
because they are lower level; Artificial Life models are said to be
general because they are abstract; neuromorphic models are said to be
more realistic because they use a physical implementation. However,
none of these connections follow simply from the nature of modelling
but depend on background assumptions about biology. Is inclusion of a
certain level essential to explaining behaviour? Can general laws of
life be found? Are physical factors more important than information
processing in understanding perception? The arguments for using robot
models in biology, as for any other approach, reflect particular views
about biological explanation. This will be further discussed in
section 4 which applies the defined dimensions to describe the
biorobotic approach.

3.1 Biological Relevance

Is the biological target system clearly identified? Does the model
generate hypotheses for biology?

Models can differ in the extent to which they are intended to
represent, and to address questions about, some real biological
system. Work in biorobotics varies in biological relevance. For
example Huber & Bulthoff (1998) use a robot to test the hypothesis
that a single motion-sensitive circuit can control stabilisation,
fixation and approach in the fly. This work is more directly
applicable to biology than the robot work described by Srinivasan et
al. (1999) utilising bee-inspired methods of motor control from visual
flow-fields, which does not principally aim to answer questions about
the bee. Similarly, the robotuna (Triantafyllou & Triantafyllou, 1995)
and 'robopike' were specifically built to test hypotheses for fish
swimming - "The aim of these robots is to help us learn more about the
complex fluid mechanics that fish use to propel themselves" (Kumph,
1998) - whereas the pectoral fin movements implemented on a robot by
Kato & Inaba (1998), though based on close study of Black bass, are
not tested primarily for how well they explain fish swimming

Another expression of this dimension is to distinguish between
investigation of "the model as a mathematical statement and the model
as empirical claim about some part of the physical world" (Orzack &
Sober, 1993, p.535).Investigating a model for its own sake is often
regarded critically. Hoos (1981) describes as "modilitisbeing more
interested in the model than the real world and studying only the
portions of questions that are amenable to quantitative treatment"
(p.42). Bullock (1997) criticises Artificial Life where "simulations
are sometimes presented as artificial worlds worthy of investigation
for their own sakeHowever this practice is theoretically bankrupt, and
such [result] statements have no scientific currency" (p.457). But
Caswell (1988), for example, defends the need to investigate
theoretical problems raised by models independently of their fit to
reality. Langton's (1989) advocacy of investigating life as it could
be is an example. As in 'pure' maths, the results may subsequently
prove to have key applications, but of course there is no guarantee
that the "model-creating cycle" will not end up "spiralling slowly but
surely away from reality" (Grimm, 1994, p.645) without any
reconnection occurring.

It is worth explicitly mentioning in this context that a model that is
'irrelevant' for biology might have utility in other respects. Models
may serve for purposes of communication or education; or be employed
for prediction and control. Moreover, there may be some value in
investigating the technological aspects of a model: the mechanisms may
have utility independent of their adequacy in explaining their origin.
Arkin (1998) describes robots that abstract and use "underlying
details" from biological sciences "unconcerned with any impact on the
original discipline" (p.32). Such 'models' should then be evaluated
with respect to engineering criteria (6), rather than how well they
represent some natural system.

Biologically 'irrelevant' models, then, are those too far removed from
biology to connect their outcomes back to understanding the systems
that inspired them. For a non-robotic example, doubts are expressed
about the relevance of artificial neural networks by e.g. Miall
(1989): "it is not clear to what extent artificial networks will help
in the analysis of biological networks" (p.11). The main criteria for
relevance could be taken to be the ability of the model to generate
testable hypotheses about the biological system it is drawn from. For
example the robot studies of Triantafyllou & Triantafyllou (1995)
mentioned above suggest that fish use the creation of vortexes as a
means of efficient tail-fin propulsion.

Arbib & Liaw (1995) provide their as definition of a biological model:
"a schema-based model becomes a biological model when explicit
hypotheses are offered as to how the constituent schemas are played
over particular regions of the brain" (p.56) (in their case, this
involves the use of simulated and robot models of the visual guidance
of behaviour in the frog). Generalised, this seems an appropriate test
for relevance: are the mechanisms in the model explicitly mapped back
to processes in the animal, as hypotheses about its function? In
biorobotics this may sometimes concern neural circuitry, e.g. in a
model of auditory localisation of the owl (Rucci et al. 1999). But it
can also occur at a relatively high level, such as using shaping
methods in learning (Saksida et al., 1997) or involve testing a simple
algorithm such as the sufficiency of a small set of local rules to
explain collecting and sorting behaviour in ants (Melhuish et al.,
1998; Holland et al, 1999). The point is to use the robot model to
make a serious attempt at addressing biological questions, at whatever
level these may exist.

This notion of 'relevance' appears to be what at least some authors
mean by the term 'realism' in describing models. Churchland &
Sejnowski (1988) appear to define 'realistic' in this way "realistic
models, which are genuinely and strongly predictive of some aspect of
nervous system dynamics or anatomy" vs. "simplifying models, which
though not so predictive, demonstrate that the nervous system could be
governed by specific principles" (p.744). But this is rather different
to their definition in Sejnowski et al. (1988) of a realistic model as
a "large scale simulation that tries to incorporate as much of the
cellular detail as is available" made "realistic by adding more
variables and more parameters" (p.1300). It seems unlikely that they
believe only models realistic in the latter sense can be realistic in
the former sense - indeed they argue in Churchland et al. that "a
genuine perfect model, faithful in every detail, is as likely to be
incomprehensible as the system itself" (p.54). However, 'realistic' is
often used to mean 'detailed', or 'not abstract'. For example: Beer et
al. (1998) specify realistic in relation to robot models as "literally
try to emulate in every detail a particular species of insect" (p.
32); Manna & Pnueli (1991) define realism as degree of detail; Palsson
& Lee (1993) directly equate realistic to complex- a decision on
realism is how many factors to include; and Orzack & Sober (1993)
redefine Levins' (1966) realism as "takes into account more
independent variables known to have an effect" (p.534).

However it is clear that Levins (1966) was concerned to argue against
the assumption that a model can only be made 'realistic' by being more
detailed. His discussion of real & general models includes a number of
quite simple and abstract examples: the issue of realism is the extent
to which they improve understanding of the biological system, i.e.
what I have here called relevance. Schultz & Sullivan (1972) make a
useful distinction between modelling that tries to build a complete
"picture of reality" versus building a device for learning about
reality: i.e. it may be possible for a model to be too detailed (or
'realistic' in one sense) to actually be productive of hypotheses (or
'realistic' in the other sense). Collin & Woodburn (1998) similarly
refer to the possibility of "a model in which the incorporated detail
is too complex...for it to contribute anything to the understanding of
the system" (p.15-16). The relevance of a model to biology, and the
detail it includes, are separable issues which should not be conflated
under the single term 'realism'.

3.2 Level

What are the base units of the model?

This dimension concerns the hierarchy of physical/processing levels
that a given biological model could attempt to represent. Any
hypothesis will usually have elemental units whose "internal structure
does not exist or can be ignored" (Haefner, 1996, p.4). In biology
these can range from the lowest known mechanisms such as the physics
of chemical interactions through molecular and channel properties,
membrane dynamics, compartmental properties, synaptic & neural
properties, networks and maps, systems, brains and bodies, perceptual
and cognitive processes, up to social and population processes
(Shepherd, 1990). The level modelled in biorobotics usually includes
mechanisms of sensory transduction, for example the sonar sensors of
bats (Kuc, 1997) including the pinnae movements (Peremans et al.,
1998), or of motor control, such as the six legs of the stick insect
(Pfeiffer et al., 1995) or the multi-jointed body of the snake
(Hirose, 1993). The central processing can vary from a rule-based
level through high level models of brain function such as the control
of eye movements (Schall & Hanes, 1998), to models of specific neuron
connectivity hypothesised to underlie the behaviour, such as
identified neural circuitry in the cricket (Webb & Scutt, 2000), and
even the level of dendritic tree structure that explains the output of
particular neurons such as the looming detector found in the locust
and modelled on a robot by Blanchard et al. (1999). The data for the
model may come from psychophysics (e.g. Clark's (1998) model of
saccades), developmental psychology (Scassellati, 1998) or
evolutionary studies (Kortmann & Hallam, 1999), but most commonly
comes from neuroethological investigations.

This notion of level corresponds to what Churchland & Sejnowski 1988
call levels of organisation and as they note this does not map onto
Marr's well-known discussion of levels of analysis (1982). Marr's
levels (computational, algorithmic and implementational) apply rather
to any explanation across several levels of organisation and describes
how one level (be that network, neuron or channel) considered as an
algorithm relates to the levels above (computation) and below
(implementation). In fact this point was made clearly by Feibleman
(1954): "For any organisation, at any given level, its mechanism lies
at the level below and its purpose at the level above"(p.61).

One source of the conflict over the 'correct level' for biological
modelling may be that levels in biology are relatively close in
spatio-temporal scale, as contrasted with macro and micro levels in
physics by Spriet & Vansteenkiste (1982). They point out that
"determination of an appropriate level is consequently less evident"
(p.46) in biological sciences. Thus it is always easy to suggest to a
modeller that they should move down a level; while it is obviously
impractical to pursue the strategy of always working at the lowest
level. Koch (1990) makes the interesting point that low-level details
may be unimportant in analysing some forms of collective neural
computation, but may be critical for others - the 'correct level' may
be problem specific, and "which really are the levels relevant to
explanation of in the nervous system is an empirical, not an a priori,
question" (Churchland et al., 1990, p.52).

Another problem related to levels is the misconception that the level
of a model determines its biological relevance. A model is not made to
say more about biology just by including lower-level mechanisms. For
example, using a mechanism at the neural level doesnt itself make a
model realistic: most neural network controlled robots have little to
do with understanding biology (Zalzala & Morris, 1996). Moreover,
including lower levels will generally make the model more complex,
which may result in its being intractable and/or incomprehensible.
Levins (1993) provides a useful example from ecological models: it is
realistic to include a variable for the influence of nutrients; less
realistic to include specific variables for nitrogen and oxygen if
thereby other nutrient effects are left out. It is also important to
distinguish level from accuracy (see below) as it is quite possible to
inaccurately represent any level. Shimoyama et al. (1996) suggest that
to "replicate functionality and behaviournot necessarily duplicate
their anatomy" in building robot models of animals is to be "not
completely faithful" (p.8): but a model can faithfully replicate
function at different levels.

3.3 Generality

How many systems does the model target?

A more general model is defined as one that "applies to more
real-world [target] systems" (Orzack & Sober, 1993, p.534). Some
researchers in biorobotics appear sanguine about the possibility of
generality e.g. Ayers et al. (1998) claim "locomotory and taxis
behaviours of animals are controlled by mechanisms that are conserved
throughout the animal kingdom" and thus their model of central pattern
generators is taken to be of high generality. Others are less
optimistic about general models. Hannaford et al (1995), regarding
models of motor control with broad focus, opines "because of their
broad scope, it is even more difficult for these models to be tested
against the uncontroversial facts or for them to predict the results
of new reductionist experiments". This suggests that increasing
generality decreases relevance, so it should be noted that, strictly
speaking, a model must be relevant to be general - if it doesn't apply
to any specific system, then how can it apply to many systems (Onstad,
1988)? But a model does not have to be general to be relevant.

The obvious way to test if a model is general is to show how well it
succeeds in representing a variety of different specific systems. For
many models labelled 'general' this doesnt happen. When it is
attempted, it usually requires a large number of extra situation or
task specific assumptions to actually get data from the model to
compare to the observed target. This is a common criticism of optimal
foraging studies (Pierce & Ollanson, 1987): that provided enough task
specific assumptions are made, any specific data can be made to fit
the general model of optimality. A similar critique can be made of
'general' neural nets (Verschure, 1996) - a variety of tasks can be
learned by a common architecture, but only if the input vectors are
carefully encoded in a task specific manner. Raaijmakers (1994) makes
a similar point for memory models in psychology and pertinently asks
is this any better than building specific models in the first place?

The most common confusion regarding generality is that what is
abstract will thereby be general. This can often be found in writings
about artificial life simulations, and Estes (1975) for example makes
this claim for psychological models. Shannon (1975) talks about "the
most abstract and hence the most general models" (p.10) and Haefner
(1996) suggests more detail necessarily results in less generality.
Sejnowski et al (1988) describe simplifying models as abstracting from
individual neurons and connectivity to potentially provide general
findings of significance for the brain. Sometimes this argument is
further conflated with 'levels', for example Wilson (1999) discusses
how "component neurons may be described at various levels of
generality" (p.446) contrasting the 'abstraction' of spike rates to
the 'detail' of ionic currents - but an ionic current description is
actually more general as it applies to both spiking and non-spiking
neurons. The membrane potential properties of neurons are very general
across biology but not particularly abstract; whereas logical
reasoning is quite abstract but not very general across biological
systems. Obviously some concepts are both abstract and general such as
feedback and many concepts are neither. Moreover precisely the
opposite claim, i.e. that more detail makes models more general, is
made by some authors e.g. Black (1962), Orzack & Sober (1993). The
reasoning is that adding variables to a model will increase its scope,
because it now includes systems where those variables have an
influence, whereas before it was limited to systems where they do not.

Grimm (1994) points out that insofar as generality appears to be lost
when increasing detail, it may simply be because the systems being
modelled are in fact unique, rather than because of an inherent
trade-off between these factors. This raises the important issue that
"generality has to be found, it cannot simply be declared" (Weiner,
1995, p.155). That is to say the generality of a model depends on the
true nature of the target(s). If different animals function in
different ways then trying to generalise over them wont work you are
left studying an empty set. Robertson (1989) makes the same point with
regard to neural networks "[neural] circuits that are unique in their
organisation and operation demand unique models if such models are to
be useful" (p.262); Taylor (1989) similarly argues for ecology that
simple models are "not shortcuts to ecological generality".
Consequently, one strategy is to instead work on understanding
specific systems, from which general mechanisms, if they exist, will
emerge (Arbib & Liaw, 1995). Biology has often found that the
discovery and elucidation of general mechanisms tends to come most
effectively from close exploration of well-chosen specific
instantiations (Miklos, 1993) such as the fruitfly genome or squid
giant axon.

3.4 Abstraction

How many elements and processes from the target are included in the

Abstraction concerns the number and complexity of mechanisms included
in the model; a more detailed model is less abstract. The 'brachiator'
robot models studied by Saito & Fukuda (1996) illustrate different
points on this spectrum: an early model was a simple two-link device,
but in more recent work they produce a nine-link, 12 degree-of-freedom
robot body with its dimensions based on exact measurements from a 7-8
year-old female simiang skeleton. 'Abstraction' is not just a measure
of the simplicity/complexity of the model however (Brooks & Tobias,
1996) but is relative to the complexity of the target. Thus a simple
target might be represented by a simple, but not abstract, model, and
a complex model still be an abstraction of a very complex target.

Some degree of abstraction is bound to occur in most model building.
Indeed it is sometimes taken as a defining characteristic of modelling
- "A model is something simple made by the scientist to help them
understand something complicated" (Segev, 1992, p.414). It is
important to note that abstraction is not directly related to the
level of modelling: a model of a cognitive process is not, of its
nature, more or less abstract than a model of channel properties. The
amount of abstraction depends on how the modeller chooses to describe
and represent the processes, not what kind of processes they
represent. Furthermore, the fact that some models - such as biorobots
- have a hardware 'medium' (see below) does not make them necessarily
less abstract than computer simulations. A simple pendulum might be
used as an abstract physical model for a leg, whereas a symbolic model
of the leg may include any amount of anatomical detail. As Etienne
(1998) notes "Robots tend to simulate behaviour and the underlying
neural events on the basis of a simplified architecture and therefore
less precisely than computers" (p.286).

How much abstraction is considered appropriate seems to largely
reflect the tastes of the modeller: should biology aim for simple,
elegant models or closely detailed system descriptions? Advocates of
abstraction include Maynard Smith (1974) "Should we not therefore put
into the model everything that we think might be important?
construction of such complex models is a waste of time" (p.116), and
Molenaar (1994) "precisely by simplification and abstraction that
models are most useful" (p.101). The latter gives as reasons for
preferring more abstract models that complex models are harder to
implement, understand, replicate or communicate. An important point is
they thereby become hard for reviewers to critique or check (e.g.
Rexstad & Innis (1985) report a surprising number of basic errors in
published models they were attempting to reimplement to test
simplification techniques). Simpler models are easier to falsify and
reduce the risk of merely data-fitting, by having fewer free
parameters. Their assumptions are more likely to be transparent.
Another common argument for building a more abstract model is to make
the possibility of an analytical solution more likely (e.g. the
abstraction of neural sprouting proposed by Elliot et al., 1996).

However abstraction carries risks. The existence of an attractive
formalism might end up imposing its structure on the problem so that
alternative, possibly better, interpretations are missed. Segev (1992)
argues that in modelling neurons, we need to build complex detailed
models to discover what are appropriate simplifications. Details
abstracted away might turn out to actually be critical to
understanding the system. As Kaplan (1964) notes the issue is often
not just over-simplification per se, but whether we have "simplified
in the wrong way" or that "what was neglected is something important
for the purposes of that very model"(p.281). For explaining biological
behaviour, abstracting away from the real problems of sensorimotor
interaction with the world is argued, within biorobotics, to be an
example of the latter kind: in this case, abstraction reduces
relevance because the real biological problem is not being addressed.

3.5 Structural Accuracy

Is the model a true representation of the target?

Accuracy is here intended to mean how well the mechanisms in the model
reflect the real mechanisms in the target. This is what Zeigler calls
structural validity: "if it not only produces the observed real system
behaviour but truly reflects the way in which the real system operates
to produce this behaviour" (1976, p.5) as distinct from replicative
and predictive validity, i.e. how well the input/output behaviour of
the system matches the target (7). This notion has also been dubbed
"strong equivalence" (Fodor, 1968). Brooks & Tobias (1996) call this
the credibility of the model, and Frijda (1967) suggests
"[input/output] performance as such is not as important as convincing
the reader that the reasons for this performance are plausible"
(p.62). Thus Hannaford et al. (1995) lay out their aims in building a
robot replica of the human arm as follows: "Although it is impossible
to achieve complete accuracy, we attempt to base every specification
of the systems function and performance on uncontroversial
physiological data".

One major issue concerning the accuracy of a model is "how can we
know?" (this is also yet another meaning of 'realism'). The
anti-realist interpretation of science says that we cannot. The fact
that certain theories appear to work as explanations is not evidence
to accept that they represent reality, because the history of science
has shown us to be wrong before (the pessimistic meta-induction,
Laudan, 1981). On the other hand, if they do not approximately
represent reality then how can we build complex devices that actually
work based on those theoretical assumptions (the no miracle argument',
Putnam 1975)? Not wishing to enter this thorny territory, it will
suffice for current purposes to argue for no more than an
instrumentalist position. If we cant justifiably believe our models,
we can justifiably use them (Van Fraassen, 1980). Accuracy in a model
means there is "acceptable justification for scientific content of the
model" (Rykiel, 1996, p.234) relative to the contemporary scientific
context in which it is built; and that it is rational (Cartwright,
1983) to attempt "experimental verification of internal mechanisms"
(Reeke & Sporns, 1993, p.599) suggested by the model.

Inaccuracies in models should affect our confidence in using the model
to make inferences about the workings of the real system (Rykiel,
1996), but do not rule out all inference provided "assumptions [are]
made explicit so that the researcher can determine in what direction
they falsify the problem situation and by how much" (Ackoff, 1962,
p.109). Horiuchi & Koch (1999) make this point for neuromorphic
electronics "By understanding the similarities and differencesand by
utilising them carefully, it is possible to maintain the relevance of
these circuits for biological modelling" (p.243). Thus accuracy can be
distinguished from relevance. It is possible for a model to address
'real' biological questions without utilising accurate mechanisms.
Many mathematical models in evolutionary theory fit this description.
Dror & Gallogly (1999) describe how "computational investigations that
are completely divorced, in practice and theory, from any aspect of
the nervous systemcan still be relevant and contribute to
understanding the biological system" (p.174), for example as described
by Dennett (1984) to "clarify, sharpen [and] systematise the purely
semantic level characterisation" (p.203) of the problem to be solved.

Accuracy is not synonymous with 'amount of detail' included in the
model. This is well described by Schenk (1996) in the context of
'tree' modelling. He notes that researchers often assume a model with
lots of complex detail is accurate, without actually checking that the
details are correct. Or, a particular simplification may be widely
used, and justified as a necessary abstraction, without looking at
alternatives that may be just as abstract but more accurate.
Similarly, it has already been noted that accuracy does not relate
directly to the level of the representation a high-level model might
be an accurate representation of a cognitive process where a low-level
model may turn out not to be accurate to brain biology.

A widely used term that overlaps with both 'relevance' and 'accuracy'
is biological plausibility. This can be taken simply to mean the model
is applicable to some real biological system; or used to describe
whether the assumptions on which the model are based are biologically
accurate. More weakly, it is sometimes used to mean that the model
"does not require biologically unrealistic computations" (Rucci et
al., 1999, p.?). In fact this latter usage is probably a better
interpretation of 'plausible', that is, it describes models where the
mechanism merely could be like the biology, rather than those where
there are stronger reasons to say the mechanism is like the biology -
the latter is 'biological accuracy', and neither is a pre-requisite
for 'biological relevance' in a model.

3.6 Match

To what extent does the model behave like the target?

This dimension describes how the model's performance is assessed. In
one sense it concerns testability: can we potentially falsify the
model by comparing its behaviour to the target? For example, the
possibility that the lobster uses instantaneous differences in
concentration gradients between its two antennules to do chemotaxis
was ruled out by testing a robot implementation of this algorithm in
the real lobster's flow-tank (Grasso et al. 2000). However assessment
of a biorobot may be simply in terms of its capabilities rather than
directly relate back to biology. While a significant role for robot
models is the opportunity to compare different control schemes for
their success (e.g. Ferrell (1995) looks at three different
controllers, two derived from biology, for six-legged walking) simply
reporting what will work best on a (possibly inaccurate) robot model
does not necessarily allow us to draw conclusions about the target
animal behaviour.

When a direct comparison with biology is attempted, there is still
much variability on this dimension regarding the nature of the
possible match between the behaviours. Should the behaviours be
indistinguishable or merely similar? Are informal, expert or
systematic statistical investigations to be used as criteria for
assessing similarity? Is a qualitative or quantitative match expected?
Can the model both reproduce past data and predict future data? Some
modelling studies provide little more than statements that, for
example, "the overall behaviour looked quite similar to that of a real
moth" (Kuwana et al., 1995, p.375). Others make more direct
assessment, e.g. Harrison & Koch (1999) have tested their analog VLSI
optomotor system in the real fly's flight simulator and "repeated an
experiment often performed on flies", showing for example that the
transient oscillations observed in the fly emerge naturally from
inherent time-delays in the processing on the chip. Even where
biological understanding is not the main aim of the study, it is
possible that "animals provide a benchmark" for evaluating the robot
system, such as Berkemeier & Desai's (1996) comparison of their
"biologically-styled" leg design to the hindlimb of a cat at the level
of force and stiffness parameters.

There are inevitable difficulties in drawing strong conclusions about
biological systems from the results of robot models. As with any
model, the performance of similar behaviour is never sufficient to
prove the similarity of mechanisms - this is the problem of
underdetermination. Some authors are concerned to stress that
behavioural match is never sufficient evidence for drawing conclusions
about the accuracy or relevance of a model (e.g. Deakin, 1990; Oreskes
et al., 1994). Uttal (1990) goes so far as to say that "no formal
model is verifiable, validatable or even testable with regard to
internal mechanisms" and claims this is "generally accepted throughout
other areas of science". But the widespread use of models in exactly
the way so deplored suggests that most modelers think a reasonable
defence for the practice can be made in terms of falsification or
coincidence. If the model doesnt match the target then we can reject
the hypothesis that led to the model or at least know we need to
improve our model. If it does match the target, better than any
alternatives, then the hypothesis is supported to the extent that we
think it unlikely that such similar behaviour could result from
completely different causes. This is sometimes more formally justified
by reference to Bayes theorem (Salmon, 1996).

However there are some limitations to this defence. Carrying out the
comparison of model and target behaviours can be a sufficiently
complex process that neither of the above points apply. First, how can
we be sure that the measurements on the real system are correct? If
the model doesnt match we may reject the measurements rather than the
model. Second, an interpretation process is required to convert the
behaviour of the model and target into comparable form. This
interpretation process may be wrong, or more worryingly, may be
adjusted until the match comes out right - "interpretive steps may
inadvertently contain key elements of the mechanism" (Reeke & Sporns,
1993, p.598). Third, it is not uncommon for models to have their
parameters tuned to improve the match. As Hopkins & Leipold 1996
demonstrate, this practice can in fact conceal substantial errors in
the model equations or in the data. Finally Bower & Koch (1992)
provide a sobering view of the likelihood of a model being rejected on
the basis of failure to match experiments:

"experiments needed to prove or disprove a model require a multi-year
dedicated effort on the part of the experimentalistfalsification of
any one such model through an experimentum crucis can be easily
countered by the introduction of an additional ad hoc hypothesis or by
a slight modification of the original model. Thus the benefit, that
is, the increase in knowledge, derived from carrying out such time-
and labour- intensive experiments is slight" (p.459)

3.7 Medium

What is the simulation built from?

Hypotheses can be instantiated as models in various different forms,
and hardware implementation is one of the most striking features of
biorobotics compared to other biological models. Doucet & Sloep (1992)
list mechanical, electric, hydraulic, scale, map, animal, game and
program as different forms a model might take. A popular taxonomy is
'iconic', 'analog' and 'symbolic' models (e.g. Black, 1962; Schultz &
Sullivan, 1972; Kroes, 1989; Chan & Tidwell, 1993) but the definitions
of these terms do not stand up to close scrutiny. Iconic originally
derives from representation, meaning something used to stand in for
something else, and is used that way by some authors (Harre, 1970b;
Suppe, 1977) to mean any kind of analogy-based model. However, it is
now often defined specifically as using "another instance of the
target type" (Chan & Tidwell, 1993), or "represent the properties by
the same properties with a change of scale" (Schultz & Sullivan, 1972,
p.6). One might assume this meant identity of materials for the model
and the target, as discussed below, but the most cited example is
Watson & Cricks scale model of DNA, which was built of metal, not
deoxyribonucleic acid. Yet 'analog' models are then distinguished from
'iconic' as models that introduce a "change of medium" (Black, 1962)
to stand in for the properties. A popular example of an analog model
is the use of electrical circuit models of mechanical systems. Some
authors include computer models as analogs e.g. Achinstein (1968)
whereas others insist they are symbolic e.g. Lambert & Brittan (1992).
But whether the properties are shared or analogous or merely
symbolically represented depends entirely on how the properties are
defined: whether the 'essence' of a brain is its chemical
constitution, its connectivity pattern or its ability to process
symbols depends on what you are trying to explain. All models are
'iconic', or share properties, precisely from the point of view that
makes the model usefully stand in for the target for a particular
purpose (Durbin (1989) calls this "the analogy level"). Hence I will
abandon this distinction and consider the medium more literally as
what the model is actually built from.

A model can be constructed from the same materials as its target.
Bulloch & Syed (1992) describe culture models i.e. the reconstruction
of simplified networks of real neurons in vitro as models of networks
in vivo; and Miklos (1993) argues for the use of transgenic techniques
to "build novel biological machines to test our hypotheses" (p.843).
Kuwana et al. (1995) use actual biological sensors - the antennae of
moths on their robot model and note these are 10,000 times more
sensitive than available gas sensors. In these cases the
representation of the target properties is by identity in the model

However most models are not constructed from the same materials. They
may share some physical properties with their targets, e.g. a vision
chip and an eye both processes real photons. Gas sensing is
substituted for pheromone sensing in Ishida et al.'s (1999) robot
model of the moth, but they replicate other physical features of the
sensor, for example the way that the moths wings act as a fan to draw
air over the sensors. Models may use similar physical properties. This
may mean that the properties can be described by the same mathematics
e.g. the subthreshold transistor physics used in neuromorphic design
are said to be equivalent to neuron membrane channel physics
(Etienne-Cummings et al, 1998). Or it may be a 'looser' mapping. The
robot model of chemotaxis in C. Elegans (Morse et al., 1998) uses a
light source as an analog for a chemical gradient in a petri dish,
while preserving a similar sensor layout and sensitivity. Models may
also use quite different properties to stand in for the properties
specified in the target e.g. a number in the computer processor
labelled activity to represent the firing rate of a neuron, or the use
of different coloured blocks in a robot arena to represent 'food' and

In fact nearly all models use all these modes of representation to
various extents in creating correspondences to the hypothesised target
variables. Thus 'symbolic' computer simulations frequently use time to
represent time (Schultz & Sullivan, 1972); 'iconic' scale models tend
to use materials of analogous rigidity rather than the same materials;
mathematical models can be treated as a short-hand for building a
physically 'analogous' system. Rather than sharply contrasting 'kinds'
of models, what is of relevance are the constraints the medium imposes
on the operation of the model. What makes a representation more
symbolic is that the medium is more arbitrary or neutral with respect
to representing the target properties. Symbols rest on "arbitrary
conventionsno likeness or unlikeness it may bear to a its subject
matter counts as a reason why it is a symbol for, or of, a " (Harre,
1970, p.38). More physical models are chosen because the medium has
some pre-existing resemblance to the properties we wish to represent,
such as the use of analog VLSI to implement neural processing (Mead,
1989). The medium may contribute directly to the accuracy and
relevance of the model, or simply make it easier to implement, run or
evaluate as described by Quinn & Espenscheid (1993):

"Even in the most exhaustive [computer] simulations some potentially
important effects may be neglected, overlooked or improperly modelled.
It is often not reasonable to attempt to account for the complexity
and unpredictability of the real world. Hence implementation in
hardware is often a more straightforward and accurate approach for
rigorously testing models of nervous systems" (p.380)

Doucet & Sloep (1992) point out "the way physical models operate is,
as it were, ruled by nature itselfrules for functioning of conceptual
[symbolic] modelswe make ourselves" (p.281). Symbolic models may
implicitly rely on levels of precision in processing that are unlikely
to be possible to real systems. Computer programs can represent a
wider range of possible situations than we can physically model, but
physical models cannot break the laws of physics.

4. The position of biorobotics

In section 2.4 I discussed in what sense biorobots can be considered
biological models - in particular, how robots can be used as physical
simulations of organisms, to test hypotheses about the control of
behaviour. How, then, does biorobotics differ from other modelling
approaches in biology? If it is suggested that "the use of a robot
ensures the realism" (Burgess et al., 1997, p.1535) of a model, does
this mean making the model more relevant for biology, making it more
detailed, making it more accurate, making it more specific (or
general?), making it a 'low-level' model, making the performance more
lifelike, or just that the model is operating with 'real' input and

In this section, I will use the framework developed above to clarify
how biorobotics differs, on various dimensions, from other kinds of
biological models. I will also advance arguments for why the resulting
position of biorobots in modelling 'space' is a good one for
addressing some fundamental questions in explaining biological
behaviour. I do not intend to suggest that it is the only correct
approach - "there is no unique or correct model" (Fowler, 1997, p.8)
of a system. However "there are good and bad models" (op. cit.)
relative to the purposes of the model builder. Thus this discussion
will also illustrate for what purposes in understanding biology
biorobotics appears to have particular strengths.

4.1 Relevance to biology

A notable feature that distinguishes recent biorobotic research from
earlier biologically-inspired approaches in robotics (such as the
'behaviour-based' approach articulated by Brooks Brooks (1986) and
Arkin (1998)) is the increased concern as to whether and how the robot
actually resembles some specified biological target. Thus most of the
robot studies listed in table 1 cite the relevant biological
literature that has guided decisions on what to build, how to build
it, and how to assess it; frequently a biological investigator has
initiated or collaborated directly in the research. The likelihood of
being able to apply the results back to biology is thus increased,
even where this was not the primary aim in the initial robot
construction. Biorobotics has been able to confirm, develop and refute
theories in several areas of biology, as already described in a number
of examples above.

A distinction was drawn in previous sections between using biorobots
as biological models and using them for engineering, and it is
sometimes argued that these are incompatible, or at least orthogonal,
concerns (e.g. Hallam, 1998; Pfeifer, 1996). Nevertheless many of
those working in biorobotics claim to be doing both. For example
Hirose et al. (1996) include as biorobotics both "build robots that
emulate biological creatures" and "use development of robots to
understand biological and ethological concepts" (p.95). Espenschied et
al. (1996) in describing their work on robot models of insect walking
claim "results that demonstrate the value of basing robot control on
principles derived from biologyalsoprovide insight into the mechanisms
of locomotion in animals and humans" (p.64). Lambrinos et al. (1997)
regarding their robot model of desert ant navigation suggest "On the
one hand, the results obtained provide support for the underlying
biological models. On the other handcomputationally cheap navigation
methods for mobile robots are derived" (p.?). Raibert (1986) in
discussing methods for legged locomotion points out "In solving
problems for the machine, we generate a set of plausible algorithms
for the biological system. In observing the biological behaviour, we
explore plausible behaviours for the machine" (p.195).

Indeed, even where the explicit aim in building the robot model is
said to be just engineering or just biology, the process of doing one
is very likely to involve some of the other. It is the engineering
requirement of making something that actually works that creates much
of the hypothesis testing power of robotic models of biological
systems. This is well described by Raibert (1986):
"To the extent that an animal and a machine perform similar locomotion
  tasks, their control systems and mechanical structure must solve
  similar problems. By building machines we can get new insights
  into these problem, and we can learn about possible solutions. Of
  particular value is the rigor required to build physical machines
  that actually work" (p.3)

In the other direction, building a robot 'inspired' by an animal
source presupposes a certain degree of knowledge about that source. If
as Ayers et al. (1998) claim "biologically-based reverse engineering
is the most effective procedure" to design robots, then we need to
understand the biology to build the robots - in Ayers' case this has
involved exhaustive analysis of the underlying 'units' of action in
the locomotion behaviour of the lobster. That is, our goal is as
defined by Shimoyama et al. (1996) "to understand activation and
sensing of biological systems so that we can build equivalents" (p.8)
or Leoni et al. (1998) "a proper comprehension of human biological
structures and cognitive behaviour is fundamental to design and
develop a [humanoid] robot system" (p.2274). The robot designers
motives thus overlap substantially with those of the biologist.

4.2 Level

It is sometimes argued in biorobotics that this methodology should
focus on lower levels, or working from 'bottom-up'. In fact Taddeucci
& Dario (1998a) describe explicitly, in the context of models of
eye-hand control, what most biorobotics researchers do implicitly i.e.
work both top-down and bottom-up on the problems. The possible
influence of lower level factors is kept in mind, and the exploration
of the interaction of levels is engaged in. While this is perhaps true
of many other modelling approaches, robotic implementation
specifically supports the consideration and integration of different
levels of explanation because of its emphasis on requiring a complete,
behaving system as the outcome of the model. For example, Hannaford at
al. (1995) primarily consider their robot arm as a "mechanism or
platform with which to integrate information", particularly the
interaction of morphology and neural control. Thus the context of the
behaviour of the organism is always included in a robot model,
counteracting the tendency in biological studies to lose sight of this
context in close study of small parts of the underlying mechanisms.

The level of mechanism modelled by the robot will reflect the level of
information currently available from biology. Interest in a particular
level of explanation (such as single neuron properties) may bias the
choice of target system e.g. towards invertebrate systems in which
identified neurons have been mapped (Franceschini, 1996). On the other
hand, interest in a particular target may determine the level at which
an accurate model can be attempted. For example, Etienne (1998)
reviews the behavioural and physiological data on mammalian navigation
and concludes that lack of information about the actual interactions
of the neural systems "leaves the field wide open to speculative
modelling" (p.283) at the level of networks.

In addition, biorobotic systems emphasise the 'physical' level in the
performance of sensing and action. That is, the dynamics of the
physical interaction of the robot/animal and its environment are seen
to be as critical in explaining its behaviour as the processing or
neural connectivity (Chiel & Beer, 1997). It is often found that
engaging closely in modelling the periphery simplifies central or
higher level processing. For example, Mura & Shimoyama (1998) note
that copying the circuitry of insect visual sensors "closely
integrates sensing and early stage processing" to "ease off decision
making at a higher processing level" (p.1859), and Kubow & Full (1999)
discuss the extent to which running control is actually done by the
mechanical characteristics of the cockroachs legs. Some of the most
interesting results from biorobotic modelling are demonstrations that
surprisingly simple control hypotheses can suffice to explain
apparently complex behaviours when placed in appropriate interaction
with the environment. Examples include the use of particular optical
motion cues to achieve obstacle avoidance that slows down the robot in
cluttered environments without explicit distance cues being calculated
(Franceschini et al. 1992), the 'choice' between sound sources with
different temporal patterns resulting from a simple four-neuron
circuit in the robot cricket (Webb & Scutt, 2000), and the use of limb
linkage through real world task constraints to synchronise arm control
(Williamson, 1998).

4.3 Generality

In engineering, robots built for specific tasks have to date been more
successful than 'general purpose' ones. Similarly in biorobotics the
most successful results to date have been in the context of modelling
specific systems - particular competencies of particular animals.
There is some doubt whether modelling 'general' animal competencies
(e.g. by simulating 'hypothetical' animals such as Pfeifer's (1996)
'fungus eater' or Bertin & van de Grind's (1996) 'paddler') will tell
us much about any real biological system. Without regrounding the
generalisations by demonstrating the applicability of the results to
some specific real examples, the problem modelled may end up being
'biological' only in the terminology used to describe it.

An example of the tendency to more specificity is the shift in
research described by Nelson & Quinn (1998) from generic six-legged
walkers (Espenscheid, 1996) to a robot that closely copies the anatomy
and mechanics of the cockroach. As they explain, the desired movement
capabilities for the robot - fast running and climbing abilities -
depend on quite specific properties such as the different functions of
the rear, middle and front pair of legs. Hence the specific morphology
has to be built into the robot if it is to be able to exploit features
such as the propulsive power of the rear legs and the additional
degrees of freedom in the front legs that enable the cockroach to

If important factors in understanding behaviour lie in the specific
sensorimotor interface, then it is necessary to model specific systems
in sufficient detail to encompass this. 'Generalising' a sensorimotor
problem can result in changing the nature of the problem to be solved.
What is lost are the properties described by Wehner (1987) as 'matched
filters', the specific fit of sensor (or motor) mechanisms to the
task. The sound localisation of crickets is a good illustration.
Crickets have a unique auditory system in which the two ears are
connected by a tracheal tube to form a pressure difference receiver.
This gives them good directionality but only for a specific frequency
- that of the calling song they need to localise. Copying this
mechanism in a robot model it was possible to demonstrate that this
factor alone can suffice to reproduce the cricket's ability to respond
only to songs with the carrier frequency of conspecifics (Lund et al.,

Note that while 'matched filters' are by their nature specific to
particular animals, the concept is a general one. Similarly, while the
neural circuitry modelled in the cricket robot is highly specific to
the task (and hence very efficient) the idea it uses of exploiting
timing properties of neural firing is a general one. Thus we can see
general principles emerging from the modelling of specific systems.
Moreover, the 'engineering' aspect of biorobotics enhances the
likelihood of discovering such generalities as it attempts to transfer
or apply mechanisms from biology to another field, the control of
man-made devices.

4.4 Abstraction

It might be assumed that the aims discussed so far of increasing
relevance by having a clearly identified target system, and increasing
specificity rather than trying to invent general models, require that
biorobotic models become more detailed. Beer et al. (1997) suggests as
a principal for this approach "[generally to] err by including more
biology than appears necessary" (p.33). However others believe that
abstraction does not limit relevance e.g. McGeer (1990) "it seems
reasonable to suppose that our relatively simple knee jointed model
has much to say about walking in nature" (p.1643). Indeed has been
suggested that a key advantage of biorobotics is the discovery of
simpler solutions to problems in biology because it takes an abstract
rather than analytic approach (Meyer, 1997). It is clear that some
quite abstract robot representations have usefully tested some quite
specific biological hypotheses. For example, there is minimal
representation of biological details in the physical architecture of
Beckers et al's (1996) robot 'ants', Burgess et al.'s (1997) 'rat' or
indeed the motor control of the robot 'cricket' mentioned above, but
nevertheless it was possible to demonstrate interesting resemblance in
the patterns of behaviour of the robots and the animals, in a manner
appropriate to testing the hypotheses in question.

Rather than being less abstract, it might better be said that
biorobotics has adopted different abstractions from simulations (or
from standard robot control methods (Bekey,1996 ; Pratt & Pratt,
1998a)). Robots are not less abstract models just because they are
physically implemented - a two-wheeled robot is a simpler model of
motor control than a six-legged simulation. What does distinguish
abstraction in biorobotics from simulations is that it usually occurs
by leaving out details, substitution, or simplifying the
representation, rather than by idealising the objects or functions to
be performed. Thus even two-wheeled motor control has to cope with
friction, bumps, gravity and so on whereas a six-legged computer
simulation may restrict itself to representing only the kinematic
problems of limb control and ignore the dynamics entirely.

Different aspects of the systems are often abstracted to different
degrees in biorobotics. Thus models involving quite complex sensors
often use very simple two-wheeled motor control rather than equally
complex actuators. Edelman et al. (1992) describe relatively complex
neural models but test them in rather abstract tasks. Though some
robots are tested in quite complex environments, the majority have a
simplified environment constructed for them (though in some cases this
is not much different from the controlled environment used to test the
animals). Pfeifer (1996) and Cruse (2000) have made the point that
this imbalance in abstraction may itself lead to a loss of biological
relevance. What is needed is to ensure that the assumptions involved
in the abstraction are clear, and justified. A good example is the
description by Morse et al. (1998) of the simplifications they adopted
in their robot model of chemotaxis in C. elegans, such as the
biological evidence for abstracting the motor control as a constant
propulsive force plus a steering mechanism provided by contraction of
opposing muscles.

4.5 Accuracy

If 'highest possible accuracy' was considered to be the aim in
biorobotics, then there are many ways in which existing systems can be
criticised. Most robot sensors and actuators are not directly
comparable to biological ones: they differ in basic capability,
precision, range, response times and so on. Binnard (1995) in the
context of building a robot based on some aspects of cockroach
mechanics, suggests that the "tools and materialsare fundamentally
different" (p. 44), particularly in the realm of actuators. Ayers
(1995) more optimistically opines that "Sensors, controlling circuits
and actuators can readily be designed which operate on the same
principles as their living analogs". The truth is probably somewhere
in between these extremes. Often the necessary data from biology is
absent or not in a form that can easily be translated into
implementation (Delcomyn et al., 1996). The process of making
hypotheses sufficiently precise for implementation often requires a
number of assumptions that go well beyond what is accurately known of
the biology. As for abstraction, there is also a potential problem in
having a mismatch in the relative accuracy of different parts of the
system. For example it is not clear how much is learnt by using an
arbitrary control system for a highly accurate anatomical replica of
an animal; or conversely applying a detailed neural model to control a
robot carrying out a fundamentally different sensorimotor task.

Biorobotics researchers are generally more concerned with building a
complete, but possibly rough or inaccurate model, than with strict
accuracy per se. That is, the aim is to build a complete system that
connects action and sensing to achieve a task in an environment, even
if this limits the individual accuracy of particular parts of the
model because of necessary substitutions, interpolations and so on.
While greater accuracy is considered worth striving for, a degree of
approximation is considered a price worth paying for the benefits of
gaining a more integrated understanding of the system and its context,
in particular the "tight interdependency between sensory and motor
processing" (Pichon et al., 1989, p.52). This is exemplified in their
robot 'fly' by the use of self movement to generate the visual input
required for navigation.

Projects that set out to build 'fully accurate' models tend not to get
completed, and we can learn more from several somewhat inaccurate
models than from one incomplete one. In several cases the accuracy has
then been increased iteratively, for example, the successive moves
from a slower, larger robot implementation of the cricket robot (Webb,
1994), to a robot capable of processing sound at cricket speed (Lund
et al., 1998), and then to a controller that more closely represents
the crickets neural processing (Webb & Scutt, 2000). Indeed, Levins
(1966) argues that building multiple models is a useful strategy to
compensate for inevitable inaccuracies because results common to all
the models are 'robust' with respect to the individual inaccuracies of

4.6 Match

It should be admitted that the assessment of the behaviour relative to
the target is still weak in most studies in biorobotics. It is more
common to find only relatively unsupported statements that a robot
"exhibited properties which are consistent with experimental results
relating to biological control systems" (Leoni et al., 1998, p.2279).
One encouraging trend in the direction of more carefully assessing the
match is the attempt to repeat experiments with the same stimuli for
the robot and the animal. For example Touretsky & Saksida (1997)
describe how they "apply our model to a task commonly used to study
working memory in rats and monkeys- the delayed match to sample task"
(p.219), and Sharpe & Webb (1998) draw on data in ant chemical
trail-following behaviour for methods and critical experiments to
assess a robot model's ability to follow similar trails under similar
condition variations, such as changes in chemical concentration. Some
behaviours lend themselves more easily than others to making
comparisons for example the fossilised worm trails reproduced in a
robot model by Prescott & Ibbotson (1997) provide a clear behavioural
'record' to attempt to copy with the robot.

The accuracy of the robot model may impose its own limits on the
match. Lambrinos et al. (1997) note, when testing their polarisation
compass and landmark navigation robot in the Sahara environment, that
despite the same experimental conditions "it is difficult to compare
the homing precision of these agents, since both their size and their
method of propulsion are completely different" (p.?). There is also
the inherent problem in any modelling, that reproducing the same
behaviour is not proof that the same underlying mechanism is being
used by the robot and the animal. There are some of ways in which the
biorobotics approach can attempt to redress these limitations. By
having a specific target, usually chosen because there is substantial
existing data, more extensive comparisons can be made. Using a
physical medium and more accurately representing environmental
constraints reduces the possibility that the world model is being
tuned to make the animal model work, rather than the reverse. The
interpretation of the behaviour is more direct. Voegtlin & Verschure
(1999) argue, in their robot implementation of models of classical
conditioning, that by combining levels, and thus satisfying
constraints from "anatomy, physiology and behaviour" the argument from
match is strengthened.

Finally, biorobotic modelling has been instrumental in driving the
collection of further data from the animal. Quinn & Ritzmann (1998)
describe how building a cockroach-inspired robot has "required us to
make detailed neurobiological and kinematic observations of
cockroaches" (p.239). Correctly matching the behaviour is perhaps less
important then revealing what it is we need to know about the animal
to select between possible mechanisms demonstrated in the robot.

4.7 Medium

The most distinctive feature of the biorobotics approach is the use of
hardware to model biological mechanisms. It is also perhaps the most
often questioned - what is learnt that could not be as effectively
examined by computer simulation? One justification relates to the
issue of building 'complete' models discussed above - the necessity
imposed by physical implementation that all parts of the system
function together and produce a real output. Hannaford et al. (1995)
argue that "Physical modelling as opposed to computer simulation is
used to enforce self consistency among co-ordinate systems, units and
kinematic constraints" in their robot arm.

Another important consideration is that using identity in parts of a
model can sometimes increase accuracy at relatively little cost. Using
real water or air-borne plumes, or real antennae sensors, saves effort
in modelling and makes validation more straightforward. Dean (1998)
proposes that by capturing the body and environmental constraints,
robots provide a stronger "proof in principle" that a certain
algorithm will produce the right behaviour. In engineering,
demonstration of a real device is usually a more convincing argument
than simulated results. Thus one direction of current efforts in
biorobotics is the attempt to find materials and processes that will
support better models. Dario et al. (1997) review sensors and
actuators available for humanoid robots. Kolacinski & Quinn (1998)
discuss elastic storage and compliance mechanisms for more muscle-like
actuators. Mojarrad & Shahinpoor (1997) describe polymeric artificial
muscles that replicate undulatory motions in water, which they use to
test theoretical models of animal swimming. On a similar basis some
researchers use dedicated hardware for the entire control system (i.e.
not a programmed microcontroller). Franceschini et al.'s (1992) models
of the fly motion detection system used to control obstacle avoidance
are developed as fully parallel, analog electronic devices. Maris &
Mahowald 1998 describe a complete robot controller (including contrast
sensitive retina and motor spiking neurons) implemented in analog
VLSI. Cited advantages of hardware implementations include the ability
to exploit true parallelism, and increased emphasis on the
pre-processing done by physical factors such as sensor layout. It is
important to note, however, that simply using a more physical medium
does not reduce the need for "ensuring that the relevant physical
properties of the robot sufficiently match those of the animal
relative to the biological question of interest" (Beer et al., 1998,
p.777). Electronic hardware is not the same medium as that used
biology, and may lend itself to different implementations a particular
problem is that neural connectivity is three dimensional where
electronic circuits are essentially two-dimensional.

However a more fundamental argument for using physical models is that
an essential part of the problem of understanding behaviour is
understanding the environmental conditions under which it must be
performed - the opportunities and constraints that it offers. If we
simulate these conditions, then we include only what we already assume
to be relevant, and moreover represent it in a way that is inevitably
shaped by our assumptions about how the biological mechanism works.
Thus our testing of that mechanism is limited in a way that it is not
if we use a real environment, and the potential for further discovery
of the actual nature of the environment is lost. Thus Beckers et al.
(1996) et al suggest "systems for the real world must be developed in
the real world, because the complexity of interactions available for
exploitation in the real world cannot be matched by any practical
simulation environment" (p.183). Flynn & Brooks (1989) argue that
"unless you design, build, experiment and test in the real world in a
tight loop, you can spend a lot of time on the wrong problems" (p.15).

   5. Conclusions

"It was by learning the inner workings of nature that man became a
builder of machines" (Hoffer, cited by Arkin, 1998, p.31).

"We've only rarely recognised any mechanical device in an organism
with which we weren't already familiar from engineering" (Vogel, 1999,

Biorobotics, as the intersection of biology and robotics, spans both
views represented by the quotes above - understanding biology to build
robots, and building robots to understand biology. It has been argued
that robots can be 'biological models' in several different senses.
They can be modelled on animals - the biology as a source of ideas
when attempting to build a robot of some target capability. They can
be models for animals - robotic technology or theory as a source of
explanatory mechanisms in biology. Or they can be models of animals -
robots as a simulation technology to test hypotheses in biology. Work
on this last kind of 'biorobot', and the potential contribution it can
make to biology, has been the main focus of discussion in this paper.

To assess biorobotics in relation to other kinds of simulations in
biology, a multidimensional description of approaches to modelling has
been proposed. Models can be compared with respect to their relevance,
the level of organisation represented, generality, abstraction,
structural accuracy, behavioural match, and the physical medium used
to build them. Though interrelated, these dimensions are separable:
models can be relevant without being accurate, general without being
abstract, match behaviour at different levels, and so on. Thus a
decision with respect to one dimension does not necessarily constrain
a modeller with respect to another.

I agree with Levins (1993) that a dimensional description should not
be primarily considered as a means of ranking models as 'better' or
'worse' but rather as an elucidation of potential strategies. The
strategy of biorobots has here been characterised as: increasing
relevance and commitment to really testing biological hypotheses;
combining levels; studying specific systems that might illustrate
general factors; abstracting by simplification rather than
idealisation; aspiring to accuracy but concerned with building
complete systems; looking for a closer behavioural match; and using
real physical interaction as part of the medium. The motivations for
this strategy have been discussed in detail above, but can be
compactly summarised as the view that biological behaviour needs to be
studied in context, that is in terms of the real problems faced by
real animals in real environments.

Thus the justification of the biorobotic approach is grounded in a
particular perspective on the issues that need to be addressed.
Different approaches to modelling will reflect differing views about
the processes being modelled, and the nature of the explanations
required. One aim of this paper is to encourage other modellers to
clarify their strategies and the justification for them - even if it
is only by disagreement over the included dimensions. Indeed,
different views of 'models' reflect different views of the 'nature of
explanation', as has been long discussed in the philosophy of science.
It has not been possible to pursue all these meta-issues, some of
which seem in any case to have little relevance to everyday scientific
use of simulation models. What is critical is that the conclusions
that can be drawn from a model are only as good as the representation
provided by that model. In this respect, by working on real problems
in real environments, robots can make good models of real animals.


1.Although Suppe(1977) distinguishes this representational use of
'model' from model used in the mathematical sense of a semantic
interpretation of a set of axioms such that they are true. There is
not space in this article to discuss this model theoretic approach in
the philosophy of science (Carnap, 1966; Nagel,  1961; Suppe,  1977)
or the formal systems theoretic approach to models developed by
Zeigler (1976), and adopted in many subsequent works (e.g variants in
Halfon, 1983; Maki & Thompson, 1973; Spriet & Vansteenkiste, 1982;
Widman & Loparo, 1989. These formal/logical definitions are in any
case not easy to apply to real examples of models in science where
Modelling is certainly an art, involving a number of logical gaps
(Redhead, 1980, p.162).

2. Black (1962) suggests this generic usage of 'model' is a
pretentious substitute for theory  whereas Stogdill (1970) calls it an
unpretentious name for a theory.

3.  Implementation is sometimes taken to mean actually reproducing a
real copy of the system (Harnad, 1989), i.e. replication; this is not
intended here.

4. It should be acknowledged that there are several, fairly
widespread, definitions of simulation more restricted than the usage I
have adopted here. First there is the usage that contrasts iterative
solutions for mathematical systems to analytical solutions (Forrester,
1972). Second, there is the emphasis on simulations being processes
i.e. dynamic vs. static models or an operating modelone that is itself
a process (Schultz & Sullivan, 1972, p.?). These distinctions have
some validity, but I am going to ignore them for convenience, as
analytical and static models stand in the same relationship to targets
and hypotheses as iterative or temporal ones. Third is the usage of
simulation to refer to relatively detailed models of specific systems
(e.g. of a particular species in certain niche) as opposed to more
general models (e.g. of species propagation) which may also be
implemented on computers for iterative solutions (e.g. Levins, 1993;
Maynard Smith, 1974). Fourth is the distinction of simulations as
models that only attempt to match input-output behaviour (e.g. Ringle,
1979; Dreyfus, 1979) as opposed to models that are supposed to have
the same internal mechanisms as their target. These latter
distinctions often carry the implication that simulations are used for
applications and models for science, i.e. these distinctions tend to
be polemic rather than principled (Palladino, 1991), and they are
certainly not clear-cut.

5. The term source is taken from Harre (1970b) who discusses this
notion extensively. Unfortunately the term 'source' is also
occasionally used for what I have called the target, by some authors.

6. 'Biologically-inspired' robots can be criticised at times for using
'biological' as an excuse for not evaluating the mechanism against
other engineered solutions, while using 'inspired' as a disclaimer for
being required to show it applies to biology.

7. Some authors do use 'accuracy' in the sense of 'replicative
validity' e.g. Bhalla et al. (1992) accuracy is defined as the average
normalized mean square difference between the simulator output and the
reference curve p.453). The term 'match' is used instead in this
article (see section 3.7).

Table 1: Examples of biorobot research. This is intended to be a
representative sampling not a fully comprehensive listing.

Subject area Examples References
Simple sensorimotor control


Moth pheromone tracking Kuwana, Shimoyama, & Miura, 1995; Ishida,
Kobayashi, Nakamoto, & Moriisumi, 1999; Kanzaki, 1996,
Ant trail following Sharpe & Webb, 1998; Russell, 1998
Lobster plume following Grasso, Consi, Mountain, & Atema, 1996; Ayers
et al., 1998
C. elegans gradient climb Morse, Ferree, & Lockery, 1998


Cricket phonotaxis Webb, 1995; Lund, Webb, & Hallam, 1998; Webb &
Scutt, 2000
Owl sound localisation Rucci, Edelman, & Wray, 1999
Human localisation Horiuchi, 1997; Huang, Ohnishi, & Sugie, 1995
Bat sonar Kuc, 1997; Peremans, Walker, & Hallam, 1998


Locust looming detection Blanchard, Verschure, & Rind, 1999; Indiveri,
Frog snapping Arbib & Liaw, 1995
Fly motion detection to control movement Franceschini, Pichon, &
Blanes, 1992; Hoshino, Mura, Morii, Suematsu, & Shimoyama, 1998; Huber
& Bulthoff, 1998; 1997; Harrison & Koch, 1999
Praying mantis peering Lewis & Nelson, 1998
Human oculomotor reflex Horiuchi & Koch, 1999; Shibata & Schaal, 1999
Saccade control Clark, 1998; Schall & Hanes, 1998


Ant polarized light compass Lambrinos et al., 1997
Lobster anemotaxis Ayers et al., 1998
Cricket wind escape Chapman & Webb, 1999
Trace fossils Prescott & Ibbotson, 1997
Complex motor control


Stick insect Cruse et al., 1998; Pfeiffer et al., 1995
Cockroach Espenschied et al., 1996; Nelson & Quinn, 1998; Binnard,
Four-legged mammal Ilg et al., 1998; Berkemeier & Desai, 1996


Tail propulsion Triantafyllou & Triantafyllou, 1995; Kumph, 1998
Pectoral fin Kato & Inaba, 1998
Undulation Patel et al., 1998
Flagellar motion Mojarrad & Shahinpoor, 1997


Insect wings

Miki & Shimoyami 1998;Fearing, 1999

Pornsin-Sirirak & Tai, 1999


Spinal circuits Hannaford et al., 1995
Cerebellar control Fagg et al., 1997
Grasping Leoni et al., 1998
Rhythmic movement Schaal & Sternad, 2001
Haptic exploration Erkman et al., 1999


   Special issue Advanced Robotics 11(6): 1997

Brooks & Stein, 1993

Hirai et al., 1998


Running & Hopping 1986
Brachiation Saito & Fukuda, 1996
Mastication Takanobu et al., 1998
Snakes Hirose, 1993, Review in Worst, ,
Paper wasp nest construct Honma, 1996


Ant/bee landmark homing Moller, 2000; Möller et al., 1998


Rat hippocampus Burgess et al., 1997

Gaussier et al., 1997


review Gelenbe et al., 1997
Collective behaviours Beckers et al., 1996

Melhuish et al., 1998
Learning Edelman et al., 1992; Sporns, forthcoming

Scutt & Damper, 1997

Saksida et al., 1997

Voegtlin & Verschure, 1999

Chang & Gaudiano, 1998


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