Bioinspired Computing Lecture 15

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Bioinspired ComputingLecture 15

• Evolutionary Simulation Models Bio-inspired
  robotics
• Netta Cohen

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Previous lectures
Today

• Evolutionary co-evolutionary algorithms
• Neural nets (RNNs, DNNs, GasNets)
• Evolution as training for artificial neural nets

• Hardware software models
• Evolutionary simulations
• Some applications to robotics
• and to biological modelling

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What is a Model?
Model building is a tool to discover, to probe
and to test the implications of a particular way
of thinking, of a theory, or of a hypothesis
about the world.
Scientific models are not imitations of reality.
If they were, accurate models would be as hard to
understand as the systems they are modeling.
Instead of fully capturing a real system, a good
model will focus on one or a few key features,
whose behaviour it is designed to replicate.
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How do we model?
Models come in many flavours

• Mathematical EMc2
• Philosophical I think therefore I am
• Physical Galileos helicopter
• Political Utopia
• Platonic forms

theory
observation
Scientific process
prediction
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Robotics
Robots are embodied and situated they interact
with the environment they sometimes interact
with each other. Their primary functions rely on
sensory and motor behaviours. They are usually
designed to perform a very specific task.
Physical robot requirements often include
cost effectiveness and
robustness. One current trend and effort is to
move forward to more complex robots that perform
multiple, complex and adaptive tasks. The design
involved in such problems is increasingly hard
the theory is usually lacking.
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Example Cricket Behaviour

• Male crickets sing rapid repeated bursts of pure
  tone.
• Females use these to approach a single singer
  despite rough terrain/obstacles/other singers.
• Female take a zig-zag path towards male of choice.

• Existing theory incorporates different tasks
  sequentially modularly
• Recognition songs are recognised extracted
  from general noise
• Selection songs are compared
• Approach the chosen singer is approached

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How do crickets do it? Robots as models of
bio-computation
A mobile robot architecture must include
sensing, planning and locomotion which are tied
together by a model or map of the world
(Kreigman et al, 1987)

• But evidence suggests this may not be how
  crickets work
• Very few neurons appear to be involved how can
  10 neurons build a model or map of the world?
• Calculating a singers location appears
  dauntingly difficult.

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Webbs Cricket
Barbara Webb built a simple mobile robot to
explore the true mechanism.

• Webbs robot mimics the physiology of the real
  cricket
• Its ears are on its elbows and are linked by a
  tracheal tube
• Sounds arrive twice at each ear, causing a phase
  difference
• For sounds of a particular wavelength, these
  phase differences indicate the direction of the
  source.

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Simulation Models
A simulation model is executable. Simulations
unfold over time according to a set of
instructions (a protocol/algorithm) that capture
selected aspects of the dynamics of a particular
model system. By building a simulation model
according to our current theories, and observing
how it unfolds under different circumstances we
can improve our understanding of our theories and
develop better ones. Hopefully, we can make
specific predictions. With those in hand, we can
return to the real world and test them by
experimenting on the real-world systems.
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Evolutionary Simulation Models
Evolutionary simulation modelling is a
cross-disciplinary tool for complex (often
multi-agent adaptive) systems.

• Game theory evolution of sex, collective
  behaviour, etc.
• Linguistics learning, evolution.
• Geography urban sprawl, traffic networks, etc.
• By their very nature, simulations extend beyond
  equilibrium or steady state solutions and allow
  us to characterise the dynamics of the system at
  hand.
• Examples

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Evolutionary Simulation Models
The strength (and weakness) of evolutionary
simulation stems from a functional definition of
fitness. Auke Ijspeert used a GA to evolve
recurrent neural network models of salamander
locomotion. The neural net was interfaced with a
simulated salamander anatomy (muscles, joints).
The fitness function was behavioural (rewarding
forward motion). Simulations of the salamander
were assessed for its ability to walk/swim. Two
types of simulation 1) Evolution (of the
RNN) 2) Locomotion (of the body controlled by the
RNN)
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The salamander model
The strength We need not have knowledge of the
biology to define a fitness function that gives
rise to efficient and robust locomotion. A
fitness function that rewards fast forward motion
might suffice.
The weakness If we wanted to model a real
salamander, we are in for a disappointment. The
neural network that evolved bares little
resemblance to the biological one.
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What does it do?
http//birg2.epfl.ch/movies/SIMS/anim_small_salam.
gif
The salamander can

• Walk
• Swim
• Switch between walking swimming across a
  border
• Switch to swimming if it falls into the water
• Follow targets, turn, modulate speed, and more...

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How simple is it?
In fact, designing or evolving such simulation
robots based only on high-level descriptions is a
daunting challenge. A high level description of
the solution hides the crucial role of low-level
components and solutions. In fact, the salamander
motor behaviour was designed according to very
similar principles as biological neural nets for
motor behaviour which we neglected to mention
until now.
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Bio-inspired robotics
By deriving first principles from biological
robots (humans or often simpler animal behaviour)
we may be able to overcome many of the hurdles.
Applications span a wide range of disciplines,
including Industry (e.g. car production),
leisure (e.g. games industry), medicine, and
research.
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Biological motor behaviour
Biological motor behaviour has evolved to offer
refined and flexible solutions to a variety of
challenges. Motor behaviour involves any muscle
activity - heart contractions, talking, chewing,
digestion, walking, swimming, flying, even
scratching, etc.
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Biological motor behaviour (cont.)
Brain control
Central Pattern Generating Neural Networks
(CPGs) Small, relatively simple neural
systems with well-defined units, well-defined
circuitry, and well-defined function
modulation
feedback
Central Pattern Generators
reflexes
control
Muscles
Such central pattern generators are believed to
be responsible for practically all known muscle
behaviour.
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Where do we start?
In simple motor systems (insects, molluscs,
crustacea), central pattern generators have
identical architectures in all animals of the
same species. They are typically distributed
throughout the body and form a distributed
coordinated network of activity. They also
receive high level instructions from the brain
and feedback from the low-level muscles.
Ijspeerts salamander model, while high level
in its fitness function, was based on a
simulation of CPGs and muscles.
http//birg2.epfl.ch/oldbirg/SIMS/sal_wsn.htm http
//birg2.epfl.ch/movies/SIMS/anim_swim_trot_opt.gi
f
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Rhythm generation in CPG circuits
Understanding CPG circuits Models of biological
neural circuits generating self-sustained
out-of-phase (or anti-phase) oscillations.
Figure with permission A. Ayali
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A Hexapod Robot
As a different example of switching behaviour
consider Randall Beers hexapod robot controller
The hexapods six legs are free to swing
laterally and to be raised or lowered. It is
stable when the polygon formed by the lowered
feet (dashed line) contains its centre of mass
(cross).

• The hexapods task is to fast forward motion.
• Six-legged insects achieve this task is a number
  of ways
• Metachronal Wave Pairs of legs swing while
  others stand
• Tripod gait 3 legs achieve stability, the other
  3 swing

This and all subsequent figures from Beer (1995).
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The Neural Architecture
Each of the six individual legs is controlled by
a fully-recurrent network of five continuous-time
neurons.
Each neuron receives the legs current angle as
an input.
Motion is governed by the output of three motor
neurons.
Each neuron inhibits its counterpart in the
adjacent leg controllers.
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Finding An Effective Controller
The problem To discover weights that provide
the controller with dynamics that, when coupled
with the dynamics of the hexapod body, cause the
hexapod to move forward.
Details of the GA During evolution, the
controller generally had access to the leg angle
input, but sometimes this input was missing.
This prevented the controller from relying on the
input alone. (Real insects can tolerate the
removal of limbs, readjusting their gait
appropriately.)
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Finding An Effective Controller
The successful evolved controllers all generated
tripod gaits.
A with sensors
Notice Bs slower, more erratic gait.
Adapted from Beer (1995)
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How Does It Use Its Sensors?
The same controller improves its performance by
exploiting sensory information when it is
available
The leg-angle input (continually oscillating
between forward and backward values) is used to
entrain the controller.
As the leg swings forward sensory feedback
promotes the stance phase. As the leg swings
back, feedback promotes the swing phase
feedback fine tunes the cyclic behaviour.
This solution allows the hexapod to respond
adaptively to changes in the length of its legs.
Although longer legs are slower, the net will
automatically slow down to compensate.
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Additional examples?
The same principles can be used to design complex
adaptive walking behaviour for more human-like
robot activity. Rybak et al. Have used CPG
principles together to design and build physical
walking robots. Again, the adaptive mechanisms of
the robots allow them to save the balance problem
without an explicit specification by the designer.
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Salamander simulations revisited
The salamander simulation was performed in small
steps

• Small simplified CPG-like networks were assumed
  for each vertebrate of the spinal cord
• Each CPG was evolved to generate anti-phase
  oscillations
• A series of connections were evolved down the
  spinal cord
• Swimming behaviour was evolved
• Sensory inputs were added on
• Walking behaviour was then superimposed
• Additional features (switching, turning, vision,
  etch.)

http//lslwww.epfl.ch/birg/salamander.shtml
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Some take home messages
Evolutionary simulation models are important
scientific as well as engineering tools.
Bio-inspired concepts and design can help develop
robust robots and simulation robots may also help
us understand the biological systems.
The choice of fitness function, the choice of
assessments, and the amount of detail in a model
all depend on the specific motivation underlying
the research.
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Suggested reading

• Ijspeert AJ (2001) A connectionist central
  pattern generator for the aquatic and terrestrial
  gaits of a simulated salamander Biological
  Cybernetics, 84, 331-348.
• Randy Beer (1995), A dynamical systems
  perspective on agent-environment interaction,
  Artificial Intelligence, 72,173-215.
• Visually guided walking http//bach.ece.jhu.edu/
  etienne/labweb/projects/index.html and
• http//www.rybak-et-al.net/legloc.html
  and
• tutorial http//www.iguana-robotics.com/presentat
  ions/cpgchip/ppframe.htm
• http//www.med.unifi.it/didonline/anno-I/informat
  ica/analogcomputers.html
• http//www.comp.leeds.ac.uk/johnb/celegans/