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Evolutionary Robotics

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Title: Evolutionary Robotics


1
Evolutionary Robotics
  • Tom Ziemke
  • Dept. of Computer Science
  • University of Skövde, Sweden
  • tom_at_ida.his.se

2
Largely based on
  • Nolfi Floreano (2000). Evolutionary Robotics
    The Biology, Intelligence and Technology of
    Self-Organizing Machines. MIT Press.

3
Evolutionary Robotics (ER)
  • ER the attempt to develop robots and their
    sensorimotor control systems through an automatic
    design process ( self-organization) involving
    artificial evolution
  • general procedure (as in all evolutionary
    computing)
  • take an initial population of random individuals
  • evaluate each individuals fitness
  • let population reproduce using fitness-biased
    selection, crossover, mutation, etc.
  • Go back to step ?

4
Why robotics is hard
  • behavioral, physical systems are difficult to
    design
  • much more than computer programs they depend on
    the interaction with their environment, which is
  • often dynamic, unpredictable, etc.
  • usually not fully accessible for the robot
  • robot and environment form a dynamical system
  • robots sensory state is a function of the
    environment and its own actions
  • not easy to know a priori what internal
    mechanisms result in which behavior (and the
    other way round!)

5
ER vs. Robot Learning
  • robot learning (e.g. using ANNs,) relies on the
    capacity to self-organize and generalize from a
    limited training set
  • learning can be very useful where a (formal)
    model of the control task is lacking (e.g.
    ALVINN)
  • but it requires explicit feedback
  • targets in each time step in supervised learning
  • occasional feedback in reinforcement learning
  • ER shares reliance on self-organization
  • but required amount of feedback (much) lower
  • no constraints on what can be evolved

6
What is actually evolved?
  • most common evolution of neural network weights
    as an alternative to conventional training
  • because local (gradient descent) search methods
    have serious limitations
  • in particular recurrent nets are difficult to
    train
  • supervised and reinforcement learning require
    more a priori knowledge
  • evolution of initial weights for life-time
    learning
  • evolution of learning rules
  • evolution of network architectures /
    modularization
  • evolution of robot morphologies
  • e.g. brain-body co-evolution

7
Khepera robot
  • small size makes it easy to build and re-arrange
    environments (and relatively simple to simulate)

8
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9
Example (1) - Looping maze
10
Looping maze experiment
  • goals
  • carried out entirely on the physical robot
  • simple fitness function that emphasizes
    environmental interaction
  • network
  • eight inputs from infrared sensors
  • two outputs control motors (1 forward, 0
    backward, 0.5 no motion)
  • output layer is self-recurrent
  • population
  • 80 individuals (networks)
  • fitness evaluation 80 steps ( 300 ms)

11
Fitness function
  • goal
  • robot should move fast, stay away from
    obstacles
  • F V (1 - ?(?v)) (1 - I)
  • V average rotation speed of the wheels
  • maximized by speed
  • ?v difference in speed
  • maximized by straight motion
  • i highest sensor activity (between 0 no object,
    1 touching object)
  • maximized by distance from objects

12
Fitness curve (as usual)
13
Evolved direction of motion
  • direction of motion not specified, but frontal
    direction emerges after a few generation

14
  • state-space of the three components of the
    fitness function
  • motion of evolution (equilibirum points of the
    best individuals)
  • V 0.6
  • 1-i 0.6 (i.e. i 0.4)
  • 1 - ?(?v) 0.4

15
Example (2) - Homing
  • additional sensors
  • 2 light sensors
  • one IR under the robot detects black and white
  • battery (sim.)
  • linear decrease in 50 cycles
  • instantly recharged in the black area, near the
    light tower

16
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17
Fitness function
  • simpler version of the previous one
  • F V (1 - i)
  • V average rotation speed of the wheels
  • maximized by speed
  • i highest sensor activity (between 0 no object,
    1 touching object)
  • maximized by distance from objects
  • robot has to return to the zone, otherwise
    battery runs out, but it shouldnt stay there
    (fitness 0)

18
Evolutionary process
  • population of 100 individuals
  • each started with a full battery in each
    generation in a random position
  • maximum of steps set to 150
  • robot evolved for 10 days in a dark room (except
    for the light tower)
  • after about 200 generations individuals were
    capable of
  • navigating around the environment
  • avoiding walls and the recharging area
  • starting a homing trajectory when 1/3 of battery
    power left
  • returning to the area when there were only 2-3
    battery-steps to go
  • leaving immediately after recharge

19
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20
Simulation vs. Reality
  • controllers are often evolved in simulation
    because
  • robot can damage itself or the environment when
    making mistakes
  • evolution takes a lot of time
  • many individuals many generations evaluation
    period
  • transfer simulation ? reality
  • possible if simulation is sufficiently realistic
  • but that makes simulation more time-consuming
  • evolved controllers are often evaluated or
    evolved further on the physical robot

21
Example (3) - Garbage-collecting robot
  • 60 cm 35 cm environment, surrounded by 3cm
    walls
  • equipped with 2 DOF gripper (up-down, open-close)
  • task pick up target objects (height 3 cm
    diameter 2.3 cm)
  • search for objects, avoiding walls
  • recognize and approach objects
  • pick up objects
  • search for a wall, avoiding other objects
  • recognize and approach wall
  • release object

22
Example (4) - Competitive Co-Evolution
  • CCE the evolution of two or more competing
    populations with coupled fitness
  • e.g. predator - prey
  • may enhance the power of artificial evolution
  • evolutionary arms races competing populations
    can reciprocally drive each other to
    incrementally increasing levels of behavioral
    complexity

23
CCE Experiments with Kheperas
24
Body Brain Chicken Egg?
  • Creating artificial life forms through
    evolutionary robotics faces a "chicken and egg"
    problem
  • Learning to control a complex body is dominated
    by inductive biases specific to its sensors and
    effectors, while
  • building a body which is controllable is
    conditioned on the pre-existence of a
    brain. (Funes Pollack, 1997)

25
Example (5) Experimental Setup
26
Evolving behavior and vision in predator and prey
  • Preconditions
  • Same maximum speed in both robots
  • Speed in predator constrained by the view angle

27
Prey dominate
28
Variation Adding a constraint
  • Preconditions
  • Same maximum speed in both robots
  • Speed in both robots constrained by the view
    angle

29
Predators dominate
30
Morphological space
  • Prey choose speed over vision

31
Evolution of physical structures (Brandeis)
  • E.g. Lego structures, first in simulation
  • e.g. a bridge, fitness length from starting
    point

32
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33
Golem Project - Brain-Body Co-Evolution
  • In simulation and reality Golem project at
    Pollacks DEMO Lab, Brandeis (Lipson Pollack,
    2000)
  • physical robot is (semi-) automatically
    constructed using 3D solid printing from
    thermoplastic material (only motors to be added)

34
Open issues in evolutionary robotics
  • designers influence is still strong fitness
    function, genotype-phenotype mapping,
    environment, population sizes, robot body (number
    and position of sensors, etc.), control
    architecture, etc.
  • but there is active research on all of those
    issues
  • for applications time is certainly still a
    problem
  • relevance for the understanding of natural
    systems
  • ER models are extremely simplified
  • but useful
  • for understanding general principles (e.g.
    co-evolution, interaction between learning and
    evolution, etc.)
  • fairly assumption-free modeling and hypothesis
    testing
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