Evolving robot brains using vision Lisa Meeden Computer Science Department Swarthmore College

Can be used in a variety of classes Introduction to Cognitive Science  Students observe and describe results of evolution Artificial Intelligence  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:  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

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 back- propagation, allows students to create more open- ended robot learning problems