Tutorial.floreano

A TUTORIAL
Stefano Nolfi
Neural Systems & Artificial Life
National Research Council
Roma, Italy
nolfi@ip.rm.cnr.it
Dario Floreano
Microengineering Dept.
Swiss Federal Institute of Technology
Lausanne, Switzerland
dario.floreano@epfl.ch

TUTORIAL Stefano Nolfi & Dario
The method
fitness function
ivV 11
t e s t
t e s t
t e s t
G e n . 0
s e le c t
r e p r o d u c e
a n d m u t a t e
G e n . 1
s e le c t
G e n . n
… … … . .
r e p r o d u c e
a n d m u t a t e
genotype-to-phenotype
mapping

Behavior-Based Robotics & ER
locomote
avoid hitting things
explore
manipulate the world
build maps
sensors actuators
behavior-based
robotics
[Brooks, 1986]
evolutionary robotics
?
?
?
?
?
sensors actuators

Learning Robotics & ER
sensors
motors
desired output or
teaching signal
[Kodjabachian & Meyer, 1999]

Artificial Life & ER
[Menczer and Belew, 1997] [Floreano and Mondada 1994]

How to Evolve Robots
evolution on the real world
[Floreano and Nolfi, 1998]
evolution on simulation
+ test on the real robot
[Nolfi, Floreano, Miglino, Mondada 1994]

Evolution in the Real World
mechanical
robustness
energy
supply
analysis
[© K-Team SA] [© K-Team SA] [© K-Team SA]
[Floreano and Mondada, 1994]

Evolution in Simulation
Different physical sensors and actuators may perform differently
because of slight differences in their electronics or mechanics.
Physical sensors deliver uncertain values and commands to
actuators have uncertain effects.
The body of the robot and the environment should be accurately
reproduced in the simulation.
0
20
320
20
80
0
256
512
768
1024
y
z
x
0
20
140
200
260
0
256
512
768
1024
y
z
x
4th IF sensor 8th IF sensor
[Nolfi, Floreano, Miglino and Mondada 1994; Miglino, Lund, Nolfi, 1995]

Designing the Fitness Function
FEE functions that describe how the controller should work (functional), rate the system on
the basis of several variables and constraints (explicit), and employ precise external
measuring devices (external) are appropriate to optimize a set of parameters for complex
but well defined control problem in a well-controlled environment.
BII functions that rate only the behavioral outcome of an evolutionary controller
(behavioral), rely on few variables and constraints (implicit) that be computed on-board
(internal) are suitable for developing adaptive robots capable of autonomous operation in
partially unknown and unpredictable environments without human intervention.
[Floreano et al, 2000]

Genetic Encoding
Evolvability
Expressive power
Compactess
Simplicity
[Gruau, 1994, Nolfi and Floreano 2000]

Adaptation is more Powerful than
Decomposition and Integration
The main strategy followed to develop mobile robots has
been that of Divide and Conquer:
1) divide the problem into a list of hopefully simpler sub-problems
2) build a set of modules or layers able to solve each sub-problem
3) integrate the modules so to solve the whole problem
Unfortunately, it is not clear how a desired behavior should
be broken down

Proximal and Distal Descriptions of
Behaviors
motor
space
sensory
space
environment
discriminate
approach
avoid
explore
distal
description
proximal
description
[Nolfi, 1997]

Discrimination Task (1)
explore
avoid
approach
discriminate
sensors actuators
decomposition and integration
walls
and
cylinders
small
and
large
cylinders
[Nolfi, 1996,1999]

g e n o t y p e p h e n o t y p e explore
avoid
approach
discriminate
sensors actuators
[Nolfi, 1996]

[Scheier, Pfeifer, and Kuniyoshi, 1998]
Evolved robots act so to select sensory patterns that are
easy to discriminate

The Importance of Self-organization
Operating a decomposition at the level of the distal description
of behavior does not necessarily simplify the challenge
By allowing individuals to self-organise, artificial evolution
tends to find simple solutions that exploit the interaction
between the robot and the environment and between the
different internal mechanism of the control system.
[Nolfi, 1996,1997]

Modularity and Behaviors
Garbage Collecting
Behavior
Human Design
Explore
Discriminate
Displace in front
Pick-up
Release
C
O
O
R
D
I
N
A
T
E
Garbage Collecting
Behavior
Adaptation
?
?
?
…….
?
?
Is modularity useful in ER ?
What is the relation between self-organized
neural modules and behaviors ?
[Nolfi, 1997]

The Garbage Collecting Task (1)
Selector
Neurons
Output
Neurons
IR-Sensors LB-Sensor
left m. right m. pick-up release
m o t o r s ( b )m o t o r s ( a )m o t o r s
m o t o r s
I R - s e n s o r s & B L - s e n s o r I R - s e n s o r s & B L - s e n s o r
I R - s e n s o r s & B L - s e n s o r I R - s e n s o r s & B L - s e n s o r
A B
C D
[Nolfi, 1997]

The Garbage Collecting Task (2)
There is not a correspondence
between self-organized neural
modules and sub-behaviors
Modular neural controller able to
self-organize outperform other
architectures
0
3
6
9
1 2
0 2 5 0 5 0 0 7 5 0 1 0 0 0
g e n e r a t io n s
successfulepochs
[Nolfi, 1997]

Evolving “complex” behaviors
Bootstrap problem: selecting individuals directly for their
ability to solve a task only works for simple tasks
Incremental Evolution: starting with a simplified version
of the task and then progressively increasing complexity
Including in the selection criterion also a reward for
sub-components of the desired behavior
Start with a simplified version of the task and then
progressively increase its complexity by modifying
the selection criterion

Visually-Guided Robots
[Cliff et al. 1993; Harvey et al. 1994]

Learning & Evolution: Interactions
• Different time scales, different mechanisms, similar effects
• Learning Advantages in Evolution [Nolfi & Floreano, 1999]:
– Adapt to changes that occur faster than a generation
– Extract information that might channel the course of evolution
– Help and guide evolution
– Reduce genetic complexity and increase population diversity
• Learning Costs in Evolution [Mayley, 1997]:
– Delay in the ability to achieve fit behaviors
– Increased unreliability (learning wrong things)
– Physical damages, energy waste, tutoring
• Baldwin effect [Baldwin, 1896; Morgan, 1896; Waddington, 1942]

Hinton & Nowlan model [1987]
• Learning samples space in the surrounding of the individual
• Fitness landscape is smoothed and evolution becomes faster
• Baldwin effect (assimilation of features normally « learnt »)
• Model constraints:
– Learning task and evolutionary task are the same
– Learning is a random process
– Environment is static
– Genotype and Phenotype space are correlated
00?11???0111?0?1?0?1
1
1
?
0
?
0
Fitness=correct combination of weights

Different Tasks [Nolfi, Elman, Parisi, 1994]
- Evolving for food
- Learning predictions
- Learning mechanism=BP
- Increased speed & fitness
- Genetic assimilation

Perspectives on Landscape
Correlated landscapes
[Parisi & Nolfi, 1996]
Relearning effects
to compensate mutations
[Harvey, 1997]
(it may hold only in few cases)
A C
B1
Q
P
B2
A=weights evolved for food finding
C=weights trained for prediction
B1, B2= new position after mutation
Fitness=higher when closer to A

Evolutionary Reinforcement Learning
• Evolving both action and
evaluation connection
strengths [Ackley & Littman, 1991]
• Action module modifies
weights during lifetime
using CRBP
• ERL better better
performance than E alone
or RL alone
• Baldwin effect
• Method validated on
mobile robots [Medeen, 1996]

Evolutionary Auto-teaching
• All weights genetically
encoded, but one half
teaches the other half
using Delta rule [Nolfi & Parisi,
1991]
• Individuals can live in one
of two environments,
randomly determined at
birth
• Learning individuals adapt
strategy to environment
and display higher fitness
Learning
No learning

Evolution of Learning Mechanisms (1)
• Encoding learning rules, NOT learning weights [Floreano & Mondada, 1994]
• Weights always initialized to random values
• Different weights can use different rules within same network
• Adaptive method can be applied to node encoding (short genotypes)
1 synapse
synapse sign
synapse strength
Genetically-determined
1 synapse
synapse sign
learning rule
- hebb
- postsynaptic
- presynaptic
- covariance
learning rate
Adaptive

Sequential task & unpredictable change
• Faster and better results
[Floreano & Urzelai, 2000]
• Automatic decomposition of
sequential task
• Synapses continuously
change
• Evolved robots adapt online
to upredictable change [Urzelai &
Floreano, 2000]:
– Illumination
– From simulations to robots
– Environmental layout
– Different robotic platform
– Lesions to motor gears
[Eggenberge et al., 1999]
Genetically-determined Adaptive

Summary
• Learning is very useful for robotic evolution:
– accelerates and boosts evolutionary performance
– can cope with fast changing environments
– can adapt to unpredictable sources of change
• Lamarck evolution (inherit learned properties) may provide short-
term gains [Lund, 1999], but it does not display all the advantages
listed above [Sasaki & Tokoro, 1997, 1999]
• Distinction between learning and adaptation [Floreano & Urzelai, 2000]:
– Adaptation does not necessary develops and capitalize upon
new skills and knowledge
– Learning is an incremental process whereby new skills and
knowledge are gradually acquired and integrated

Competitive Co-evolution
• Fitness of each population depends on fitness of opponent
population. Examples:
– Predator-prey
– Host-parasite
• It may increase adaptive power by producing an
evolutionary arms race [Dawkins & Krebs, 1979]
• More complex solutions may incrementally emerge as
each population tries to win over the opponent
• It may be a solution to the boostrap problem
• Fitness function plays a less important role
• Continuously changing fitness landscape may help to
prevent stagnation in local minima [Hillis, 1990]

Co-evolutionary Pitfalls
The same set of solutions
may be discovered over
and over again. This
cycling behavior may end
up in very simple
solutions.
Solution: Retain best
individuals of last few gens
(Hall-of-Fame->all gens).
Whereas in conventional evolution the fitness
landscape is static and fitness is a monotonic
function of progress, in competitive co-evolution
the fitness landscape can be modified by the
competitor and fitness function is no longer an
indicator of progress.
Solution: Master Fitness (after evolution test
each best against all best), CIAO graphs (test
each best against all previous best).

Examples of Co-evolutionary Agents
Simulated predator-prey
[Cliff & Miller, 1997]
Distance-based fitness
100s generations
CIAO method et al.
Evolution of sensors
Ball-catching agents
[Sims, 1994]
Distance-based fitness
Rare good results

Co-evolutionary Robots
• Energetically autonomous
• Predator-prey scenarion
• Time-based fitness
• Controllers downloaded to
increase reaction speed
• Retain last best 5 controllers
for testing individuals
• Predators=vision+proximity
• Prey=proximity+faster
• Predator genotype longer
• Prey has initial position
advantage
Floreano, Nolfi, & Mondada, 1998

Co-evolutionary Results
20 40 60 80 100
0.2
0.4
0.6
0.8
1
20 40 60 80 100
0.2
0.4
0.6
0.8
1
generations generations
best predator
fitness
best prey
fitness
d
t
t
d
Predators do not attempt
to minimize distance
Prey maximize distance
progress best fun

Increasing Environmental Complexity
36240
…prevents premature cycling [Nolfi & Floreano, 1999]

Summary
• Competitive co-evolution is challenging because:
– Fitness landscape is continuously changing
– Hard to monitor progress online
– Cycling local minima
• When environment is sufficiently complex, or Hall-of-Fame
method is used, the system develops increasing more
complex solutions
• It can work and capitalize on very implicit, internal, and
behavioral fitness functions by exploring a large range of
behaviors triggered by opponents
• When co-evolving adaptive mechanisms, prey resort to
random actions whereas predators adapt online to the prey
strategy and report better performance [Floreano & Nolfi, 1997]

Evolvable Hardware
• Evolution of electronic circuits
http://www.cogs.susx.ac.uk/users/adrianth/EHW_groups.html
• Evolution of body morphologies (including sensors)
• Why evolve hardware?
– Hardware choice constrains environmental interactions and
the course of evolution
– Evolved solutions can be more efficient than those designed
by humans
– Develope new adaptive materials with self-configuration and
self-repair abilities

Evolutionary Control Circuits
• Thompson’s unconstrained evolution
• Xilinx, family 6000, overwrite global
synchronization
• Tone reproduction
• Robot control
• Fitness landscape studies (very rugged,
neutral networks)
Evolvable Hardware
Module for Khepera
http://www.aai.ca

Evolutionary Control Circuits
• Keymeulen: evolution of vision
based controllers
• Find ball while avoiding obstacles
• Constrained evolution, entirely
on physical robot
• De Garis: CAM Brain, composed
of tens of Xilinx FPGAs, 6000 family
• Growth of neural circuits using CA
with evolved rules
• Willing to evolve brain for kitten robot.
Pitfall: speed limited by sensory-
motor loop.

Evolutionary Morphologies
• Evolution of Lego Structures [Funes et al,, 1997]
• Bridges
• Cranes
• Extended to objects and robot bodies
• see www.demo.cs.brandeis.edu
• Example of evolved crane [Funes et al,, 1997]

Co-evolutionary Morphologies
Karl Sims, 1994 Komosinski & Ulatowski, 1999
http://www.frams.poznan.pl
Effect of doubling sensor range on body/wheel size [Lund et al., 1997]

Suggestions for Further Research
• Encoding and mapping of control systems
• Exploration of alternative building blocks
• Integration of growth, learning, and maturation
• Incremental and open-ended evolution
• Morphology and sensory co-evolution
• Application to large-scale circuits
• User-directed evolution
• Comparison with other adaptive techniques
• Further readings:
– Nolfi, S. & Floreano, D. Evolutionary Robotics. The Biology, Technoloy, and
Intelligence of Self-Organizing Machines. MIT Press, October 2000
– Husbands, P. & Meyer, J-A. (Eds.) Evolutionary Robotics. Proceedings of the
1st
European Workshop, Springer Verlag, 1998
– Gomi, T. (Ed.) Evolutionary Robotics. Volume series: I (1997), II (1998), III
(2000), AAI Books.

Evorobot Simulator
Sources, binaries, and documentation files freely
available at:
http://gral.ip.rm.cnr.it/evorobot/simulator.html [Nolfi, 2000]

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