NVIDIA Jetson AI Lab 教程：用 LeRobot 和 ROS 2 学机器人操作

Open-source robotics is shifting from hobby projects to shared infrastructure, with evaluation results and failures published openly rather than locked inside a few companies. Startups, students and researchers can now build on LeRobot's remote GPU inference and world action models without owning hardware fleets. Hugging Face's open stack gives it a different position from closed robotics efforts like Tesla's Optimus programme.

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Real-world robot evaluation has lacked shared standards, and publishing failed rollouts alongside successes gives the field a reproducible benchmark rather than cherry-picked demos. Labs and hobbyists can apply for free eval hours to test policies on heterogeneous hardware. eaic_sg's involvement ties the initiative to the Singapore robotics ecosystem.

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The stack is notable for treating agent harnesses like Codex and Claude Code as a dedicated robotics layer, a sign that coding agents are becoming standard tooling in robot data pipelines. Practitioners building or scaling robot fleets can use it as a vendor checklist across simulation, middleware, deployment and observability. NVIDIA's Jetson and Isaac products appear in four separate layers, underlining how concentrated edge AI hardware has become.

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