A TUTORIAL Stefano Nolfi Neural Systems & Artificial Life National Research Council Roma, Italy [email protected] Dario Floreano Microengineering Dept

A TUTORIAL

Stefano NolfiNeural Systems & Artificial Life

National Research Council

Roma, Italy

[email protected]

Dario FloreanoMicroengineering Dept.

Swiss Federal Institute of Technology

Lausanne, Switzerland

[email protected]

TUTORIAL Stefano Nolfi & Dario Floreano, 2000

The method

fitness function

ivV 11

test

test

test

Gen. 0

selectreproduceand mutate

Gen. 1

select

Gen. n

………..

reproduceand mutate

genotype-to-phenotypemapping


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 orteaching 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

2 0

3 2 02 0

8 0

0

2 5 6

5 1 2

7 6 8

1 0 2 4

y

z

x

0

2 0

1 4 02 0 0

2 6 0

0

2 5 6

5 1 2

7 6 8

1 0 2 4

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

motorspace

sensoryspace

environment

discriminate

approach

avoid

explore

distaldescription

proximaldescription

[Nolfi, 1997]


Discrimination Task (1)

explore

avoid

approach

discriminate

sensors actuators

decomposition and integration

walls and cylinders

small and large cylinders

[Nolfi, 1996,1999]



genotype phenotype 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 CollectingBehavior

Human Design

Explore

Discriminate

Displace in front

Pick-up

Release

COORDINATE

Garbage CollectingBehavior

Adaptation

?

?

?

…….

?

?

Is modularity useful in ER ?

What is the relation between self-organized neural modules and behaviors ?

[Nolfi, 1997]


The Garbage Collecting Task (1)

SelectorNeurons

OutputNeurons

IR-Sensors LB-Sensor

left m. right m. pick-up release

motors (b)motors (a)motors

motors

IR-sensors & BL-sensor IR-sensors & BL-sensor

IR-sensors & BL-sensor IR-sensors & BL-sensor

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

12

0 250 500 750 1000

generations

succ

essf

ul e

poch

s

[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

11

?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 effectsto compensate mutations [Harvey, 1997]

(it may hold only in few cases)

A C

B1Q

P

B2

A=weights evolved for food findingC=weights trained for predictionB1, B2= new position after mutationFitness=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 signlearning rule- hebb- postsynaptic- presynaptic- covariancelearning 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 solutionsmay be discovered overand over again. Thiscycling 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 fitnesslandscape is static and fitness is a monotonicfunction of progress, in competitive co-evolutionthe fitness landscape can be modified by thecompetitor and fitness function is no longer anindicator 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 fitness100s generationsCIAO method et al.Evolution of sensors

Ball-catching agents[Sims, 1994]

Distance-based fitnessRare 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

aa

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 predatorfitness

best preyfitness

d

t

t

d

Predators do not attemptto 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 HardwareModule for Kheperahttp://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, 1999http://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]

Documents

A TUTORIAL Stefano Nolfi Neural Systems & Artificial Life National Research Council Roma, Italy [email protected] Dario Floreano Microengineering Dept