Attach:Team/jan.jpg Δ Jan Peters Professor, Head ofIntelligent Autonomous Systems Office. Room E325, Building S2|02

Teaching Assistants

Attach:Team/filipe.jpg Δ Filipe Veiga Office. Room E323, Building S2|02
Attach:Team/RudiTeamList.jpg Δ Rudolf Lioutikov Office. Room E325, Building S2|02

Robot Learning 2015 Syllabus

General Information

Course web page: Robot Learning 2015

Time and place:
Wednesdays 13:30 - 15:10 at room C120@S2|02
Thursdays 10:45 – 11:30 at room C110@S2|02

TAs Office Hours: Every Friday 11:00 – 12:00 (no appointments are needed!)

Exam: TBA

TU-CAN: 20-00-0629-vl Lernende Roboter

Credits: 6.0

Mailing List: List

Homework Assignments:

  • The assignments will be made in groups of 2.
  • The groups will remain the same for the entire semester.
  • Please avoid using pencils.
  • Do not use green or red pens if handwritten.
  • Put some effort into creating nice plots as it can increase your points (part of the training).
  • Vectorized code will get a small bonus.
  • MATLAB will be used for the programming assignments.
  • If the exercise is a programming one, then code is expected to be handed in, except if noted differently.
  • Include only the important snippets of code, not whole listings.
  • Your code must be briefly documented.
  • There will be a Q&A session after each homework release.
  • The assignment will be updated if bugs are spotted (announced in the lectures).
  • After the deadline for the assignment, the solutions will be presented in class.
  • The homeworks can contribute with a bonus of 0.5 to the final grade.

Homework Release dates:

Robotics and Machine Learning in a NutshellTBA
Model Learning and Trajectory GenerationTBA
Optimal Control and Reinforcement LearningTBA
Policy Search and Inverse Reinforcement LearningTBA

Due dates: Homeworks will be graded only if they are handed in before the beginning of the lecture on the delivery date. That includes any time before the deadline if dropped in the mailbox outside Room E314@S2|02. The mailbox will be checked ten minutes before the lecture for handing-in the homeworks. Failing to comply with the deadlines will result in a score of 0 for the assignment.

iPython Notebooks:

  • The assignments will be made in groups of 2 (same groups as for the homeworks).
  • Deadline after 2 weeks of notebook assignment.
  • Notebooks can contribute with a bonus of 0.5 to the final grade.

Final Exam:

  • The exam will cover all material presented lectures, unless specifyed otherwise.
  • The exam will consist of ∼ 33 questions.
  • Students are allowed to bring to the exam a cheat sheet consisting of both pages of an A4 sheet.
  • Students are also allowed to bring a non-programmable calculator.

Final Grade: Your final grade is calculated with the following equation

and then is “floored” to the next grade {0, 0.3, 0.7}. Homeworks are optional and you can get up to one grade extra (minus) to the exam score.