The Planning Algorithms in AI and Robotics course at Skoltech, MS in Data Science, during T2, 2021-2022. About us: we are the Mobile robotics Lab. at Skoltech

This repository includes all material used during the course: Class notes, unedited videos of the lectures and problem sets.

• Instructor: Gonzalo Ferrer
• Teaching Assistant: Aleksandr Gamaiunov
• Teaching Assistant: Timur Akhtyamov

• L01: Introduction. What is planning?
• L02: Discrete Planning
• L03: Configuration Space
• L04: Sampling-based Planning
• L05: Discrete Optimal Planning
• L06: Continious Optimal Planning
• L07: Decision Making and Games
• L08: Markov Decision Process
• L09: Reinforcement Learning
• L10: General Course Discussion

Deadline dates for submitting problem sets, in the folder PS*:

• PS1: Discrete planning (14-November-2021)
• PS2: Sampling-based planning (25-November-2021)
• PS3: Value Iteration (5-December-2021)
• PS4: Decision Making (5-December-2021)

The final project could be either of the following, where in each case the topic should be closely related to the course:

• An algorithmic or theoretical contribution that extends the current state-of-the-art.
• An implementation of a state-of-the-art algorithm. Ideally, the project covers interesting new ground and might be the basis for a future conference paper submission or product.

You are encouraged to come up with your own project ideas, yet make sure to pass them by Prof. Ferrer before you submit your abstract

• Ideally 3-5 students per project (the scope of multi-body projects must be commensurate).
• Proposal: 1 page description of project + goals for milestone. This document describes the initial proposal and viability of the project.
• Presentations: The presentation needs to be 12 minutes long; There will be a maximum of 3 minutes for questions after the presentation.If your presentation lasts more than 12 minutes, it will be stopped. So please make sure the presentation does not go over.
• Paper: This should be a IEEE conference style paper, i.e., focus on the problem setting, why it matters and what is interesting/novel about it, your approach, your results, analysis of results, limitations, future directions.Cite and briefly survey prior work as appropriate but do not re-write prior work when not directly relevant to understand your approach.
• Evaluation: Each team will evaluate their colleagues’ presentations.Templates will be provided the presentation day. All these points will be summed for a final evaluation (30% of the total grade).

@Misc{ferrer2021,
  author = {Gonzalo Ferrer},
  title = {Lectures on Planning Algorithms in AI and Robotics},
  howpublished = {\url{https://github.com/MobileRoboticsSkoltech/Planning-Algorithms-T2-2021-22}},
  year = {2021}
}