Student Feedback
Great introductory course for CS students interested in Robotics. It's also a fun and low-workload option for those looking to take an optional course.
The grading is 100% practical. This year, we had two projects (self-localization and path planning), with weekly (relaxed) presentations during lab classes, two reports, and one final presentation covering both projects.
The theoretical classes were taught by Prof. Rita Cunha. They were sometimes a bit slow, but overall interesting, even if a bit superficial. It's easy to skip them since some topics aren't directly applied to the projects or...
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This course is extremely introductory. You learn to work with ROS, but most of it is self-taught.
The projects mainly involve using existing packages to make the robot do basic tasks like localization, pathfinding, and so on.
The theory gives you just the minimum needed to complete the projects. You’ll cover things like mapping and localization algorithms, Markov Chains and MDPs (Markov Decision Processes)
But honestly, you can learn these topics better and in more depth in other Intelligent Systems courses.
Syllabus
The theory classes mostly skim over general robotics topics like 'here are some cool robots moving around' or 'this is what sensors are...', and other things you probably already know, like pathfinding algorithms. Only about 15–20% of the theory felt like new, useful content.
Projects
There are 3 mini-projects, done in groups of 2. They’re small and reasonably fun.
This year's project was basically implementing a Roomba. We had to make a robot move around, localize itself, and map the room. In each assignment, we had to write a report about the experience.
While the...