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Learn where AI accelerates ROS 2 work and where it will crash your robot - by fixing the code an LLM actually gets wrong.
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An AI-assisted ROS 2 node - business logic from the model, real-time and safety structure owned by you - repaired concept by concept.
Sample incident
Fix the QoS the LLM left off so the sensor publisher matches its subscriber.
Full syllabus
An LLM writes plausible rclpy fast, but it silently drops the ROS 2 specifics — QoS, executors, lifecycle — that decide whether nodes actually talk.
AI is fastest at boilerplate — launch files, URDF, .msg — but it hallucinates physics values and reaches for deprecated Gazebo Classic tags.
MCP servers let an LLM agent call ROS tools directly. Powerful — and dangerous when a tool publishes without validating what it sends.
AI reads logs and explains tf/QoS/lifecycle errors well — but you close the loop by verifying its fix against the running system.
Un-grounded LLMs hallucinate APIs and mix Humble and Jazzy. Retrieval over the actual docs kills both.
LLMs write code that runs, not code that meets a deadline. In a control loop, that difference is a crash.
LLMs and VLMs plan well but hallucinate perception. Close the loop with real sensors before you act.
Put it together: an AI-assisted node with a persisted checkpoint and a safe, idempotent recovery path.