About this book:

*Data Engineering for Robotic AI* provides a comprehensive technical guide to building the data infrastructure required to develop, train, and deploy intelligent autonomous systems. The book emphasizes that while AI model architectures often receive the most attention, the reliability of a robot depends on a specialized data "circulatory system" that can handle high-volume, multimodal sensor data—including LiDAR, cameras, radar, and IMUs—tightly coupled to space and time. It outlines the entire lifecycle of robotic data, from edge acquisition and sub-millisecond time synchronization to the implementation of spatiotemporal schemas and scalable lakehouse storage architectures.

The text delves deeply into the "craft and science" of annotation, moving beyond simple 2D labeling to complex 3D perception, trajectory prediction, and behavioral intent. To address the "long tail" of rare and dangerous real-world events, the book advocates for a hybrid approach combining real-world logs with high-fidelity synthetic data, digital twins, and procedural scenario generation. It introduces active learning and closed-loop curation as essential strategies for identifying the most informative data points, thereby reducing annotation costs and accelerating the development of robust models.

A significant portion of the book is dedicated to the operational and ethical requirements of robotics, covering MLOps, continuous integration/deployment (CI/CD), and rigorous evaluation through simulation-in-the-loop. It establishes data governance, privacy, and safety as first-class engineering requirements, particularly for robots operating in human-centric environments like homes or hospitals. By providing detailed audit trails, versioning, and provenance patterns, the book ensures that robotic AI development is reproducible and compliant with emerging global regulations.

Ultimately, the work serves as a practical playbook for engineers and technical leaders, offering "build vs. buy" frameworks and cross-domain case studies from autonomous vehicles to industrial automation. It argues that sustained improvement in robotic intelligence is achieved through a virtuous cycle: field telemetry informs data curation, which in turn powers model retraining and rigorous validation. This systematic approach transitions robotics from ad-hoc experimentation to a scalable, professionalized engineering discipline capable of deploying safe and trustworthy machines in the physical world.

What You'll Find Inside:

• How to design end‑to‑end multimodal data pipelines that synchronize, calibrate, and fuse camera, LiDAR, radar, IMU, and tactile signals for robust robotic perception.
• Strategies for edge data acquisition and telemetry, including ROS 2/DDS bridges, adaptive compression, and reliable store‑and‑forward over intermittent links.
• Storage architectures for robotic AI: building lakehouses on object stores, managing cold archives, and ensuring immutability, versioning, and lineage for reproducible experiments.
• Annotation practices for 2D/3D perception, mapping, planning, and control, with quality‑control metrics, human‑in‑the‑loop review, and synthetic data generation via simulation and digital twins.
• MLOps for robotics: CI/CD for model training/validation, continuous evaluation with simulation‑in‑the‑loop, and closed‑loop data curation that turns field feedback into improved datasets.

Who's It For:

This book is aimed at data engineers, roboticists, machine‑learning practitioners, and technical leaders who need to build scalable, reliable data pipelines for robot‑powered AI. It provides concrete workflows, tooling recommendations, and design patterns that help teams move from ad‑hoc scripts to reproducible, production‑grade data engineering. Readers working on warehouse robots, autonomous vehicles, service robots, or inspection drones will find actionable guidance to improve data quality, ensure compliance, and accelerate model iteration.