About this book:

*Edge AI for Robotics: Tiny Models, Big Impact* provides a comprehensive technical guide to deploying machine learning on resource-constrained robotic platforms. The book establishes that local "edge" processing is essential for robotics to overcome latency, privacy, and connectivity issues. It highlights the shift from cloud-centric "bigger is better" mentalities to a "robotics-first" approach, where success is measured by the trade-offs between model accuracy, latency, energy consumption, and memory footprint.

The core of the text details a sophisticated toolkit for model optimization, including pruning, knowledge distillation, and low-rank approximation. It places heavy emphasis on quantization—moving from 32-bit floating-point to 8-bit integer or even binary representations—to align with the capabilities of embedded hardware like NPUs, DSPs, and microcontrollers. The book also covers architectural design patterns, such as depthwise separable convolutions and bottleneck structures, which are inherently efficient for real-time perception and control.

Beyond individual models, the book addresses system-level integration using ROS 2 and real-time scheduling to ensure deterministic behavior. It explores specialized sensing pipelines, uncertainty quantification, and "TinyRL" (Reinforcement Learning) for adaptive control. Furthermore, it introduces advanced paradigms like federated learning for swarm intelligence and on-device continual learning, which allow robots to adapt to environmental changes without returning data to the cloud.

The final section grounds these theories in practical application through detailed case studies. It examines the unique constraints of aerial drones, such as extreme power sensitivity, and the complexities of mobile ground robots navigating human-centric environments. By covering the entire pipeline from training in notebooks to flashing memory on industrial and agricultural hardware, the book provides a roadmap for building robust, safe, and autonomous intelligent machines.

What You'll Find Inside:

• Edge intelligence provides low latency, privacy, and autonomy by processing data on‑device despite tight compute, memory, and energy constraints in robots.
• TinyML design patterns—depthwise separable convolutions, bottleneck layers, global average pooling, and efficient activations—create inherently compact models suited for edge robotics.
• Model compression via pruning, knowledge distillation, and low‑rank methods, combined with quantization from 8‑bit to binary, drastically reduces size and energy use while preserving task performance.
• Matching models to hardware accelerators (GPUs, NPUs, DSPs, FPGAs) and using optimized runtimes like CMSIS‑NN and TFLite Micro enables real‑time inference on microcontrollers and edge SoCs.
• End‑to‑end benchmarking, energy‑aware architectures, robust data pipelines, and safety‑first deployment ensure compact models deliver reliable, impactful autonomy in drones, ground robots, and industrial systems.

Who's It For:

This book is for roboticists, embedded engineers, and ML practitioners who need to deploy machine learning on low‑power robotic platforms. Readers should have a working knowledge of Python and basic robotics concepts; the text introduces each technique with motivating examples and builds intuition before diving into details. By the end, they will be able to design, evaluate, and deploy compact models that meet strict latency, energy, and safety constraints while delivering big impact on resource‑constrained robots.