AY 23/24 - Evolutionary Robotics

This page is about the PhD course Evolutionary Robotics, for the PhD programs:

For both, the course accounts for 1.5 CFUs and lasts 12 hours.

Program, goals, requirements # ↰

Detailed program (12h) # ↰

  • Evolutionary computation (EC).
    • Definition of global optimization problem; evolutionary algorithm (EA); selection criteria; exploration/exploitation trade-off; multi-objective problems; solution representation; popular representations with their initialization and genetic operators; properties of the representation.
    • Main EAs: genetic algorithm (GA), random search, random walk, genetic programming (GP), non-dominated sorting genetic algorithm-II (NSGA-II), evolutionary strategies (ES), MAP-Elites.
    • Comparing EAs.
  • EC applications (example of) EC to robotics
    • Evolution of robotic agents controllers (brain).
    • Evolution of Soft Robots morphologies (body).
    • Simultaneous evolution of body and brain.
    • Choosing the task, the fitness; reality gap.

Goal of the course # ↰

Knowledge and understanding # ↰

  • Know the terminology and common mathematical notation for the key concepts of EC.
  • Know and understand the working scheme of EAs.
  • Know and understand the main assessment procedures for EAs.
  • Know and understand the main usages of EC for robotics.

Applying knowledge and understanding # ↰

  • Formulate a formal problem statement, using the proper terminology and mathematical notation, for simple practical problems in order to tackle them EC techniques.

Making judgements # ↰

  • Judge if a problem can be tackled with EC.
  • Judge the technical soundness of an EC system.
  • Judge the technical soundness of the assessment of an EC system.

Communication skills # ↰

  • Describe, both in written and oral form, the motivations behind choices in the design, development, and assessment of a EC system, in particular for robotics, using the proper terminology and possibly exploiting simple plots.

Learning skills # ↰

  • Retrieve information from scientific publications about EC techniques, in particular when applied to robotics.

Requirements # ↰

Basics of programming and data structures: algorithm, data types, loops, recursion, parallel execution, tree.

Strong familiarity with manipulation of mathematical notation.

Method, language, material # ↰

Language of teaching # ↰

English

Teaching method # ↰

Frontal lectures with slide projection and live annotation on the blackboard.

Course material # ↰

Teacher’s slides # ↰

The course material (teacher’s slides) are available online.

The lectures will be recorded. The recording will be available to enrolled students upon request (an email to the teacher).

Lectures timetable and course calendar # ↰

Lectures will be held in Room A, 2nd floor, building C5, Piazzale Europa Campus on:

  • Monday 12/2, 9.00-12.00 (in practice, 9.30-12.00)
  • Tuesday 13/2, 9.00-12.00 (in practice, 9.30-12.00)
  • Friday 16/2, 9.00-12.00 (in practice, 9.30-12.00)

and, in Room B, 2nd floor, building C5, Piazzale Europa Campus, on:

  • Tuesday 20/2, 9.00-12.00 (in practice, 9.30-12.00)

The lectures will be given in person and I recommend being in the room. The lectures will not be cast in streaming, but the recordings of the lectures will be available upon request.

End-of-course test (exam) # ↰

The exam is passed without a grade. For passing the exam, the student is required to deliver a short oral presentation of a research paper chosen, by the student, from a teacher-provided list of ER papers (see below).

The presentation has to be done in English, with slides prepared by the student, and last at most 10 minutes. The presentation is expected to answer the following questions (refer to the notation in the slides):

  1. what is the research question (RQ) tackled by the paper and why is it important?
  2. what is/are, formally, the problem(s)? I.e., what are the search space $S$, the controller-related (if a brain-only optimization) observation space $O$, action space $A$, state $S_c$, the fitness function $f$?
  3. what EA(s) and representation(s) (namely, $\phi$) are used?
  4. what experiments are done to answer the RQ? How are the corresponding results presented?
  5. what are, if any, the weak points of the study?

The presentation date is set in agreement with the teacher: please try to arrange sessions with more than one student at once.

List of ER papers to choose from # ↰