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AI in Robotics (Book in Progress)

أنا أعمل حالياً على كتاب بعنوان AI in Robotics يركز على كيفية استخدام تقنيات الذكاء الاصطناعي لبناء روبوتات ذكية تعمل في العالم الحقيقي.

I’m currently writing a book called AI in Robotics, focused on practical AI techniques for building robots that work in the real world.

Preface

Robotics has undergone a fundamental transformation over the past decade, driven largely by advances in Artificial Intelligence (AI). Traditional robotic systems, which relied heavily on precise models, predefined rules, and structured environments, are increasingly being complemented—or replaced—by learning-based approaches capable of operating in complex, uncertain, and dynamic real-world conditions. This book was written to reflect that shift and to provide a structured, practical introduction to AI as it is applied in modern robotic systems.

The motivation for this book comes from my professional experience working at the intersection of robotics, AI, and real-world deployment. While there is an abundance of literature on AI algorithms and an equally rich body of work on classical robotics, I have often found a gap between the two: many AI resources lack physical embodiment, while many robotics texts stop short of modern learning-based methods. This book aims to bridge that gap by focusing on AI that runs on robots, not just in theory, but in practice.

The primary objective of this book is to guide the reader through the core concepts of AI in robotics, starting from perception and computer vision, moving through learning and decision-making, and culminating in reinforcement learning and physical AI. Rather than treating these topics as isolated disciplines, the book emphasizes their integration within complete robotic systems. Throughout the chapters, I highlight how perception, control, and learning interact, and how design choices in one component affect the behavior of the system as a whole.

This book is intended for senior undergraduate students, graduate students, researchers, and practicing engineers in robotics and related fields. It assumes a basic background in linear algebra, probability, and programming, as well as introductory knowledge of robotics concepts such as sensors, actuators, and coordinate frames. Where mathematical formulations are necessary, they are presented with an emphasis on intuition and physical interpretation, with the goal of supporting understanding rather than mathematical rigor alone.

A key principle guiding this book is the close connection between simulation and real-world robotics. Many examples and discussions reflect challenges encountered when deploying AI on physical robots, including issues related to real-time constraints, safety, data efficiency, and sim-to-real transfer. Wherever possible, concepts are grounded in practical scenarios drawn from industrial robots, mobile platforms, manipulators, and humanoid systems.

The book is organized progressively. Early chapters establish the foundations of robotic perception and computer vision, followed by machine learning methods used for representation and prediction. Later chapters focus on reinforcement learning, embodied intelligence, and physical AI, highlighting how learning-based systems interact with the physical world. The final chapters explore current research directions and open challenges, providing context for future developments in the field.

This is my first book, and it reflects both what I have learned from the field and what I believe is essential for the next generation of roboticists. My hope is that this text will serve not only as a learning resource, but also as a practical reference that encourages readers to think critically about how intelligence, learning, and embodiment come together in real robotic systems.

Mohammad Alshamsi

Abstract

In this chapter, I discuss the role of Artificial Intelligence (AI) as a key enabler in the advancement of robotics. AI provides robots with the ability to perceive their environment, make decisions, and adapt their behavior in real time. I explore how methods such as machine learning, computer vision, reinforcement learning, and natural language processing are being applied to robotic systems to enhance autonomy, flexibility, and human–robot interaction. The chapter also examines applications across manufacturing, healthcare, logistics, and service robotics, while reflecting on the challenges of safety, ethics, and scalability. My aim is to highlight how the fusion of AI and robotics is shaping the next generation of intelligent, collaborative systems capable of operating in dynamic and unstructured environments.

High-Level Book Structure

This structure is logical, progressive, and textbook-friendly.

Part I – Foundations

Sets the language and tools.

Introduction to AI in Robotics

  • Why classical robotics is not enough
  • From rule-based systems to learning-based autonomy
  • Simulation vs real-world robots

Robotic Perception Fundamentals

  • Sensors (cameras, LiDAR, IMU)
  • Coordinate frames
  • Data pipelines in robots

Part II – Computer Vision for Robotics

Computer Vision Basics

  • Image formation
  • Feature extraction
  • Classical vs learning-based vision

Deep Learning for Visual Perception

  • CNNs for detection and segmentation
  • Pose estimation
  • Visual SLAM (high level)

Vision in Real Robots

  • Latency and real-time constraints
  • Camera calibration
  • ROS 2 vision pipelines
  • Simulation → real transfer

Part III – Learning and Decision-Making

Machine Learning in Robotics

  • Supervised vs unsupervised learning
  • Data collection from robots
  • Overfitting in physical systems

Reinforcement Learning (RL)

  • MDPs explained intuitively
  • Reward design for robots
  • Policy vs value-based methods

Deep Reinforcement Learning

  • Actor–critic methods
  • Sim-to-real challenges
  • Safety and sample efficiency

Part IV – Physical AI and Embodied Intelligence

Physical AI and Embodiment

  • Why embodiment matters
  • Learning with physics constraints
  • Interaction with the real world

Human–Robot Interaction

  • Multimodal interaction (vision, speech)
  • Social robots
  • Ethical and safety considerations

Case Studies

  • Manipulation
  • Locomotion
  • Teleoperation + AI
  • Industrial and humanoid robots

Part V – Future Directions

Trends and Open Challenges

  • Foundation models for robotics
  • World models
  • Lifelong learning
  • Responsible AI in robotics

Chapter 1 — Introduction to Artificial Intelligence in Robotics

1.1 Motivation and Scope

Robots have traditionally been designed to operate in structured and predictable environments. Classical approaches in robotics rely on accurate mathematical models, carefully tuned controllers, and explicitly programmed behaviors. While these methods have achieved remarkable success in controlled settings such as factory automation, they often struggle when robots are required to operate in unstructured, dynamic, or human-centered environments. This limitation has motivated the increasing integration of Artificial Intelligence (AI) into robotic systems.

AI provides robots with the ability to interpret sensory data, make decisions under uncertainty, and adapt their behavior through experience. Rather than relying solely on predefined rules, AI-enabled robots can learn from data and interaction, allowing them to cope with variability in the environment, changes in system dynamics, and incomplete information. The purpose of this chapter is to introduce the role of AI in robotics and to establish the conceptual foundation for the topics covered in the remainder of this book.

This chapter outlines why AI is necessary for modern robotics, how it complements classical methods, and how intelligent behavior emerges from the interaction between perception, learning, and control within a physical system.

1.2 From Classical Robotics to Intelligent Systems

Classical robotics is built on well-defined models of kinematics, dynamics, and control. These models enable precise motion planning and execution when system parameters and environmental conditions are known. However, in real-world applications, robots must deal with sensor noise, modeling errors, unexpected obstacles, and interactions with humans and other agents.

AI techniques address these challenges by allowing robots to reason probabilistically, extract meaningful representations from high-dimensional sensory inputs, and improve performance through learning. Machine learning methods can be used to estimate models, recognize objects, predict outcomes, and select actions. Importantly, AI does not replace classical robotics; rather, it extends it. Effective robotic systems often combine model-based control with data-driven learning, leveraging the strengths of both approaches.

This shift from purely model-driven systems to hybrid intelligent systems represents a fundamental change in how robots are designed and deployed. Understanding this transition is essential for developing robots that can operate reliably outside of controlled laboratory environments.

1.3 Perception as the Foundation of Intelligence

Perception is a central component of intelligent robotic behavior. Without the ability to perceive and interpret the environment, a robot cannot make informed decisions or adapt its actions. Cameras, depth sensors, LiDAR, and tactile sensors generate large volumes of data that must be processed in real time.

Computer vision and sensor fusion techniques enable robots to extract structure and meaning from this data. AI-based perception systems allow robots to recognize objects, estimate their pose, track motion, and understand scenes at a semantic level. These capabilities form the basis for higher-level reasoning and decision-making.

For this reason, the book begins with computer vision and robotic perception. Establishing a strong understanding of how robots see and interpret the world is critical before addressing learning and control strategies. Throughout the book, perception is treated not as an isolated module, but as an integral part of the robotic system.

1.4 Learning and Adaptation in Robotics

Learning allows robots to improve their performance over time and to generalize across tasks and environments. Supervised learning methods enable robots to map sensory inputs to desired outputs, such as object labels or control commands. Unsupervised learning can be used to discover structure in data, while reinforcement learning allows robots to learn behaviors through interaction and feedback.

In robotics, learning must account for physical constraints, safety requirements, and limited data. Unlike purely virtual domains, robotic systems cannot explore arbitrarily without risk. This makes data efficiency, stability, and interpretability especially important. The chapters on machine learning and reinforcement learning address these issues in detail, with an emphasis on methods that are suitable for real robotic platforms.

1.5 Embodiment and Physical AI

A key theme of this book is embodiment—the idea that intelligence is shaped by a robot’s physical form and its interaction with the environment. AI algorithms do not operate in isolation; their behavior is influenced by sensor placement, actuator limitations, mechanical design, and environmental contact.

Physical AI emphasizes learning and decision-making that respect physical laws and leverage interaction with the world. Examples include learning control policies that exploit dynamics, adapting to contact-rich manipulation tasks, and coordinating perception and motion in real time. This perspective highlights why robotics presents unique challenges and opportunities for AI research.

1.6 Structure of the Book

The remainder of this book is organized to reflect a progression from foundational concepts to advanced applications. Early chapters focus on robotic perception and computer vision, followed by machine learning methods used for representation and prediction. Subsequent chapters introduce reinforcement learning and decision-making in continuous, physical environments. The final sections explore physical AI, human–robot interaction, and emerging research directions.

Each chapter combines conceptual explanations with practical considerations, emphasizing the connection between algorithms and real robotic systems. Where appropriate, examples from simulation and real hardware are discussed to illustrate both capabilities and limitations.

1.7 Concluding Remarks

AI has fundamentally expanded what robots are capable of achieving. However, building intelligent robotic systems requires more than applying algorithms in isolation; it requires an understanding of how perception, learning, control, and physical embodiment interact. This chapter has introduced the motivation and scope of AI in robotics, setting the stage for the detailed topics that follow.

The chapters ahead aim to equip the reader with both the theoretical insight and practical perspective needed to design, analyze, and deploy intelligent robotic systems in real-world environments.

What the book covers

  • Robot perception (cameras, depth, LiDAR) and sensor fusion
  • Control, planning, and decision making
  • Learning-based robotics (imitation learning, reinforcement learning)
  • Vision for robotics (detection, tracking, segmentation, pose)
  • ROS2-oriented implementation notes and practical tips
  • Real projects, debugging workflows, and deployment lessons

Status

  • Writing in progress
  • Chapters and examples will be published incrementally on this website

Feedback / collaboration

If you have suggestions for chapters, examples, or want to review a draft section, reach out via:

  • LinkedIn: https://linkedin.com/in/mohammad-robot
  • GitHub: https://github.com/MohammadRobot