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Digital AI gets to skip physics. Physical AI cannot. Every grasp, every step, every navigation decision in the real world is shaped by gravity, friction, latency, sensor noise, and battery limits — constraints that no amount of model scale can paper over. Physical AI is the engineering book for the people building the systems that act in the world. It walks the full stack between "the model understands the word 'grasp'" and "the robot picked up the cup without crushing it." Token-level reasoning meeting joint torques. Vision-language-action models meeting Jetson thermal budgets. Sim-to-real bridges, data flywheels, and the regulation that's catching up. Includes four reference appendices: a glossary bridging robotics and ML vocabulary, an annotated foundational reading list, a hardware buyer's guide with 2026 prices, and a directory of open-source models, simulators, and datasets. Code examples in Python, YAML, and C++. Nearly 480 pages of citations and real engineering — every named model, dataset, paper, and regulation backed by an inline citation and a per-chapter References section. Written for robotics engineers transitioning from classical pipelines, ML engineers from the LLM world bridging into embodied systems, and infrastructure architects evaluating where Physical AI fits. A companion to the LLM Primer series.
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