capítulo por capítulo baixar AI in Robotics: Volume 1 — The Physical Machine (Chapters 1–16)

Stop watching robot demos. Learn the stack that makes them work.

This is Volume 1 of a two-volume set — Chapters 1–16, Parts I–IV. The 18 hands-on labs, the simulation and systems chapters, safety and standards, humanoids, and the appendices are in Volume 2 — Systems, Embodiments, and Labs.

By 2026, vision-language-action models command arms in factories, humanoid startups carry extraordinary valuations, and a student with a laptop can fine-tune a generalist policy. This volume takes you from “what is a frame?” to a working understanding of how a modern VLA turns a camera image and a sentence into motor commands.

• Part I — The Physical Machine. The robot as a loop; kinematics; dynamics and control from PID to MPC; state estimation and the Kalman filter; motion planning.
• Part II — Seeing the World. Cameras, depth, and point clouds; neural 3D and fast eyes; touch, proprioception, and sensor fusion.
• Part III — Learning to Act. Imitation and action chunking; reinforcement learning on real robots; sim-to-real transfer; the data pyramid.
• Part IV — Foundation Models for the Physical World. Vision-language-action models; the 2026 stack — π0, Gemini Robotics, NVIDIA GR00T, Figure Helix; world models; policy architectures and flow matching.

No PhD required. If you can write Python and look at a matrix without flinching, you can follow this book. Every chapter carries a mental model, a “what breaks in practice” section, cost tables, exercises, and takeaways. Vendor claims are labeled as vendor claims. Numbers are date-stamped, because this field moves.

Source code for the whole set lives in a free public companion repository tagged v1.0-book; the access page is at the back of this volume. The labs themselves are teaching prototypes — NumPy and small PyTorch stand-ins that run on a laptop CPU — not drop-in ROS 2, LeRobot, LIBERO, openpi, or GR00T integrations. Simulation is the default path. Cheap hardware is optional and always behind an e-stop.

Written for college students building standout final-year projects and for engineers switching into embodied AI. Honest about shipped versus working. First edition, August 2026. Errata via GitHub Issues on the companion repository.

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