LLM Agents for ROS 2 Robots: Local Models, MCP, Safety

About this course

This course contains the use of artificial intelligence. The narration is a synthetic voice; every number on screen comes from a lab you can run.36 lectures, about 2.0 hours. Each lecture has a folder with the working code, a broken copy with one classic mistake, and a check script that passes only when the fix works and the mistake is caught.

What was measured, with a small local model (qwen2.5:3b) on one laptop GPU. Your numbers may differ by a few trials:- Tool design: raw velocity tools reached the target 0 of 20 times; a go_to skill tool, 15 of 20 (95% interval 0.53 to 0.89). Lecture 3.2.- Safety: a keep-out rule written only in the prompt was broken in 13 of 20 trials; the server-side fence, 0 of 20, at a measured cost: it also stopped 7 of 7 targets whose straight path crossed the zone. Lecture 6.1.- Capstone: a 3-waypoint patrol completed 19 of 20 times, scored from the robot's true path. Lecture 8.1.- Choosing a model: a smaller model completed 0 of 20 missions while saying DONE every time. Lecture 8.2.

What this course does not cover: real robots (everything runs in turtlesim and a simulated TurtleBot), frontier or cloud models, vision language action models, fine-tuning, manipulation with Move

It, multi-robot fleets.

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