Evolutionary Robotics

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Evolutionary Robotics

• Gabriela Ochoa

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Robotics - Introduction

• Robots in the movies (C3P0, Terminator)
  fantastic, intelligent, even dangerous forms of
  artificial life
• Robots of today are not walking, talking
  intelligent machines
• Today, we find most robots working for people in
  factories, warehouses, and laboratories
• In the future, robots may show up in other
  places our schools, our homes, even our bodies.

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Mainstream Robotics

• Most robots a helping hand. Help people with
  tasks that would be difficult, unsafe, or boring
  for a real person
• A robot have 5 main components
• Controller
• Arm
• Drive
• End-Effector
• Sensor

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Autonomous Robotics

• Robot A versatile mechanical device equipped
  with effectors and sensors under the control of a
  computing system
• Human eyes, ears (sensors) hands, legs, mouth
  (effectors)
• Robot cameras, infrared range finders (sensors)
  various motors (effectors)
• Autonomous Robots those that make decisions on
  their own, guided by the feedback they get from
  their physical sensors

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Classic AI Approach
PERCEPTION
REPRESENTATION IN WORLD MODEL --
REASONING
ACTION
Divide Conquer a complex problem is
decomposed into separate, less daunting
subproblems Clasical approaches tu robotics,
assume a primary decomposition into Perception,
Planning and Action Modules
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Subsumtion Architecture (Rodney Brooks)

• Problems with the classical approach
• It is not clear how a robot control system should
  be decomosed
• Complex Interactions between subsystems, not only
  direct links, also mediated via the environment
• Subsumtion Architecture slow and and careful
  building up of a robot control system layer by
  layer
• Get something simple working (debugged) first
• Then try and add extra 'behaviours'

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The Evolutionary Approach

• Brooks' subsumption architecture is
  'design-by-hand', but inspired by an incremental,
  evolutionary approach
• Alternative explicitly use evolutionary
  techniques to incrementally evolve increasingly
  complex robot control systems
• When evolving robot 'nervous systems' with some
  form of GA, then the genotype ('artificial DNA')
  will have to encode
• The architecture of the robot control system
• Also maybe some aspects of its body/motors/sensors

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Evolutionary Robotics

• The use of evolutionary (genetic) algorithms to
  develop artificial nervous systems' for robots.
• ER can be done
• for Engineering purposes - to build useful robots
• for Scientific purposes - to test scientific
  theories
• It can be done
• for Real or
• in Simulation

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Approaches to Evolutionary Robotics

• R. D. Beer Agent control using NNs
  (continuous-time recurrent NNs) Tasks
  Chemotaxis, locomotion control 6-legged
  insect-like
• M. Colombeti, M. Dorigo Classifier Systems
• D. Floreano Kephera robots, simple recurrent NNs
• J. Koza, C. Raynolds GP to develop S. Arch.
• R. Watson Embodied Evolution
• I. Harvey, P. Husband U. Sussex continuous-time
  recurrent NNs CTRNN

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Sussex Approach to ER

• Robot as a whole body, sensors, motor and
  control system (or nervous system) as a
  dynamical system coupled with a dynamic
  environment
• Continuos time recurrent NN, with temporal delays
  on links
• Genotypes specify the architecture of the NN

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A top-down view of the simulated agent, showing
bilaterally symmetric sensors (photoreceptors).
Reversing the sensor connections will initially
have a similar effect to moving the light source
as shown.
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Dynamic Recurrent Neural Networks DRNNs

• DRNNs, or CTRNNs
• Convenient way of specifying a class of
  dynamical systems
• Different genotypes will specify different DSs,
  giving robots different behaviours.

This is just ONE possible DRNN, which ONE
specific genotype specified.
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DRNN Basics
The basic components of a DRNN are these (1 to 4
definite, 5 optional)
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NNs Equations

• CTRNNs (continuous-time recurrent NNs), where for
  each node (i 1 to n) in the network the
  following equation holds

• yi activation of node i
• ?i time constant, wji weight on connection
  from node j to node i
• ?(x) sigmoidal (1/1e-x)
• i bias,
• Ii possible sensory input.

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Genotypes

• Finite number of nodes
• Thresholds or details of non linear activatation
  function
• Links and connections between nodes sepecifying
  weights and time-delays on the links
• Optional Weight-changing rules
• Subset of the nodes, designed as input nodes,
  receiving sensory inputs
• Output or motor nodes
• Other nodes (hidden) arbitrary number

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Genetic Encoding Scheme
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Example of an Experiment

• Simulation round, two-wheeled, mobile robot with
  touch sensors and two visual sensors
• Task robot need to reach the centre of a
  simulated circular arena (white walls and black
  floor and ceiling)
• Fitness function
• robots are rated on the basis of how much time
  they spent at or near the centre.
• Measuring the distance d of the robot from the
  centre, and weighting this distance by a Gaussian
  G
• Over 100 time steps, G is summed to give a final
  score
• Robot starts near the perimeter, facing in a
  random direction

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Evaluating a robot

• When you evaluate each robot genotype, you
• Decode it into the network architecture and
  parameters
• Possibly decode part into
• body/sensor/motor parameters
• Create the specified robot
• Put it into the test environment
• Run it for n seconds, scoring it on the task.
• Any evolutionary approach needs a selection
  process, whereby the different members of the
  population have different chances of producing
  offspring according to their fitness

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Results
Typical path of a succesfully evolved robot wich
heads fairly directly for the centre of the room
and circles there, using input from 2
photoreceptors. The direction the robot is
facing is indicated by arrows for each time step
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Real Robot vs. Simulation

• Evaluate on a real robot, or Use a Simulation ?
• On a real robot it is expensive, time-consuming
  -- and
• for evolution you need many many evaluations.
• On a simulation it should be much faster
• BUT AI (and indeed Alife) has a history of toy,
  unvalidated simulations, that 'assume away' all
  the genuine problems that must be faced.
• Eg grid worlds "move one step North, Magic
  sensors "perceive food"

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Principled Simulations ?

• How do you know whether you have included all
  that is necessary in a simulation?
• -- only ultimate test, validation, is whether
  what works in simulation ALSO works on a real
  robot.
• How can one best insure this, for Evolutionary
  Robotics ?
• Noise put an envelope-of-noise, (through
  variations driven by random numbers), around
  crucial parameters whose real values you are
  unsure of.
• Evolve for more robustness than strictly
  necessary"