Tutorial: Running Local LLMs on NVIDIA Jetson to Power Robot AI Assistants

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A new developer tutorial outlines how to build an AI assistant for robots using a large language model running locally on NVIDIA Jetson edge hardware. The architecture separates the LLM's role strictly to interpreting natural-language requests and generating structured intents, while a deterministic safety layer handles all actual robot command validation and execution via ROS 2. Developers need an NVIDIA Jetson kit, a compatible JetPack and ROS 2 environment, and basic knowledge of Python or C++ and ROS 2 concepts. The guide walks through setting up a ROS 2 workspace, creating packages, and testing a publisher-subscriber pipeline before integrating AI components. An optional Flutter-based operator interface is also described, communicating with the robot through a secure gateway rather than directly exposing the ROS graph.

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