Most robotics code is written to run fast and ship — the math that matters gets buried under optimization passes, hardware glue, and ROS boilerplate. This collection inverts that priority: every algorithm is implemented in plain Python with minimal dependencies, kept short enough to read top to bottom and watch the math unfold in an animated plot. It behaves more like an executable textbook than a deployment library.
What Sets It Apart
- Breadth across the full autonomy stack — localization, mapping, SLAM, path planning, and path tracking all live in one repo, so you can trace how EKF localization feeds an MPC tracker without stitching together five separate projects.
- Legibility over performance — implementations favor clarity, so studying RRT* or FastSLAM here teaches the algorithm itself rather than someone's production shortcuts.
- Most examples ship with a matplotlib animation, turning abstract update equations into something you watch converge — far stickier than a static derivation.
- Backed by a companion textbook site and an arXiv write-up, so the running code and the underlying theory stay in step.
Who It's For
Great fit if you are a student, researcher, or engineer learning a specific robotics method and want a clean reference to read, run, and modify line by line. Look elsewhere if you need production-grade speed, real-time guarantees, or ROS integration — the code trades deployment readiness for readability, so you port the ideas into a real robot, not the files themselves.
