Download An Emergent Framework for Self-Motivation in Developmental

Evolving robot brains
using vision
Lisa Meeden
Computer Science Department
Swarthmore College
Can be used in a variety of
classes

Introduction to Cognitive Science


Artificial Intelligence


Students observe and describe results of evolution
Students modify the evolutionary process and
report on the different outcomes
Robotics

Students use this example as a base for designing
their own projects
Genetic Algorithm


Start with a random population of individuals
For each generation of the evolution process:
Fitness proportionate selection
 Reproduction
 Mutation


Repeat until best member of population is good
enough
Framework


Pyrobot simulator
Green robot



Evolving brain
Sensors: camera and sonar
Red robot


Fixed brain: move straight and
avoid obstacles
Sensors: sonar
Genetic Algorithm Details



Evolve the weights of a fixed size 3-layer neural network
that maps sensors to motors
Initialize 10 neural networks with random weights
Allow robot to move for 250 steps, fitness based on:


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
Absolute value of translation speed
Whether the robot is stalled
Centeredness of red blob in camera image
Closeness of red blob in camera image
Evolve for 10 generations, saving the best weights from
each generation
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Conclusions


Using vision in a very simplified way, but it
enables students to appreciate the power of
evolution in a relatively short demonstration
Evolving neural network weights, rather than
using fully supervised algorithms such as backpropagation, allows students to create more openended robot learning problems