Artificial Intelligence for Robotics: Build intelligent robots using ROS 2, Python, OpenCV, and AI/ML techniques for real-world tasks

Practical AI for Working Robots

Artificial Intelligence for Robotics is a hands-on guide to making robots do more than move from point A to point B. This second edition brings together the foundations of robotics, AI, and autonomy, then shows how those ideas come together in real projects using ROS 2, Python, OpenCV, and machine learning techniques.

The focus is refreshingly practical. Rather than treating artificial intelligence as an abstract add-on, the book walks through robot setup, system design, perception, object recognition, reinforcement learning, genetic algorithms, and speech recognition as parts of one growing workflow. That makes it a strong fit for readers who want to understand how intelligent behavior is built step by step.

What the Book Covers

• Core robotics and AI concepts
• Setting up a robot and development environment
• Designing a robot with a systems-engineering mindset
• Object recognition with neural networks and supervised learning
• Picking up and putting away toys with reinforcement learning and genetic algorithms
• Teaching a robot to listen with speech recognition and NLP

Who It’s For

The book is aimed at readers who already have some theoretical familiarity with robotics or AI and want to apply that knowledge in code and on hardware. It also suits robotics enthusiasts, students, and professionals looking for a structured introduction to practical autonomy.

A Guided Path Into Autonomous Robotics 🤖

One of the book’s strengths is its progression: each chapter builds on the last, so the reader moves from fundamentals to increasingly capable robotic behavior without feeling dropped into the deep end. The result is a study path that feels purposeful, technical, and grounded in real-world robotics work.

If your shelf needs a current, project-based ebook on intelligent robotics, this edition offers a clear route into the field with a balance of theory, implementation, and applied problem-solving.