DD2600 Robot Learning and Embodied AI 7.5 credits

• Fundamental methods for robot learning, learning in state spaces, learning with sensors and motors in reality.
• Learning in order to see: 3D understanding, localisation, image models with open vocabulary, image-language models.
• Learning in order to map: Neural world representations and mapping.
• Learning in order to act: Imitation learning and reinforcing learning with robots.
• Towards embodied AI: Large language models for robotics, foundation models for robotics and embodied image-language-action models.

After passing the course, the student shall be able to:

• explain the basic ideas and the challenges of learning for robots
• give an account of the concept embodied artificial intelligence and how robot learning can be combined with large-scale pre-trained neural networks such as multi-modal language models
• give an account of the theoretical background behind the methods for robot learning that are most common
• analyse advanced research in the area and critically evaluate the methods' weaknesses and strengths

in order to be able to

• implement, analyse and evaluate simple systems for robot learning
• absorb information about and read literature in the area
• implement methods based on new research and evaluate them.

Basic knowledge in deep learning, 7.5 higher education credits, equivalent completed course DD2424/DD2437.

If the course is discontinued, students may request to be examined during the following two academic years.

• LAB1 - Laboratory work, 4.5 credits, grading scale: P, F
• PRO1 - Project work, 3.0 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Olov Andersson

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex