Evolutionary robotics

Summary

The course gives an introduction to evolutionary computation, its major algorithms, applications to optimization problems (including evolution of neural networks), and application to design and control of robots. It includes software exercises and project to evolve, build, and test a robot.

Content

Learning Prerequisites

Important concepts to start the course

Programming skills (Phython, Java, C++)

Learning Outcomes

By the end of the course, the student must be able to:

• Apply new tools for software and hardware engineering
• Translate acquired theoretical knowledge in practical implementations during laboratory sessions

Teaching methods

Lectures, software exercises, and project involving evolution of morphology and neural control of robot, 3D printing of parts and assembly, test characterization of evolved robots.

Expected student activities

Attending lectures, asking critical questions, taking all exercises and completing assignments for the following week, forming groups and performing collaboratively project with physical robots, writing and presenting project results

Assessment methods

Mini-project report in powerpoint + presentation + written exam

Supervision

Resources

Bibliography

Floreano, D. & Mattiussi, C. (2008) Bioinspired Artificial Intelligence. MIT Press (selected chapters)

Ressources en bibliothèque

• Bioinspired Artificial Intelligence / Floreano

Moodle Link

• https://go.epfl.ch/MICRO-515