Books

Discover how machines learn to make decisions, and how you can too. Learning to Learn: Reinforcement Learning Explained for Humans breaks down complex AI concepts into simple, human-friendly explanations with practical projects and step-by-step guides. Perfect for beginners, developers, and anyone curious about the future of intelligent systems.

As the author, I (Anshuman Mishra) have written this book with the spirit of mentorship — not just to explain how chatbots work, but to help you build one confidently and ethically. I have taught AI, programming, and computer science for nearly two decades, and I have seen countless students struggle to bridge the gap between theory and implementation.This book closes that gap. It teaches you what to do, why to do it, and how to do it right. It’s not just a manual — it’s a journey from curiosity to mastery.You are not just learning to build a chatbot; you are learning to create intelligence — responsibly, creatively, and with purpose.

As the author, I (Anshuman Mishra) have written this book with the spirit of mentorship — not just to explain how chatbots work, but to help you build one confidently and ethically. I have taught AI, programming, and computer science for nearly two decades, and I have seen countless students struggle to bridge the gap between theory and implementation.This book closes that gap. It teaches you what to do, why to do it, and how to do it right. It’s not just a manual — it’s a journey from curiosity to mastery.You are not just learning to build a chatbot; you are learning to create intelligence — responsibly, creatively, and with purpose.

Pedagogical Highlights ·        Illustrations and Diagrams: Each topic is accompanied by clear, labeled figures showing transformations, kinematic chains, and algorithmic workflows.·        Mathematical Derivations: Detailed step-by-step derivations of equations — from rotation matrices to dynamic equations of motion.·        Conceptual Summaries: Every chapter concludes with key takeaways and conceptual summaries to reinforce learning.·        Case Studies and Exercises: Includes practical assignments and research-oriented projects to inspire deeper exploration.·        Interdisciplinary Connection: Bridges the gap between mechanical design, control systems, and artificial intelligence through unified modeling. Intended Audience ·        Engineering Students — especially from Computer Science, Electronics, Mechanical, and Mechatronics backgrounds.·        MCA/M.Tech Students specializing in AI, Data Science, or Automation.·        Researchers working on intelligent control, robotics simulation, or human-robot collaboration.·        Industry Professionals seeking to understand how AI can enhance robotic modeling and performance.·        Faculty Members developing new courses or reference material in Robotics and Artificial Intelligence. Educational and Research Impact This book is not just a compilation of topics; it is a comprehensive educational framework. Each chapter is designed to act as a mini research guide, encouraging experimentation, simulation, and publication.The author’s academic experience of over 18 years brings an authentic balance of teaching methodology and research insights. Students will gain confidence in deriving equations, implementing algorithms, and developing hybrid AI-robotic systems. Future Outlook The future of robotics lies in adaptability — machines that learn from their surroundings and optimize their actions dynamically. With advances in quantum computing, neural hardware, and real-time AI systems, the mathematical models explored in this book will form the foundation for the next generation of intelligent machines.From autonomous drones to AI-driven robotic surgeons, the applications are endless, and all of them depend on the same universal principles — mathematics and intelligence.This book will help its readers not only understand these principles but also innovate upon them.

The future of robotics lies in adaptability — machines that learn from their surroundings and optimize their actions dynamically. With advances in quantum computing, neural hardware, and real-time AI systems, the mathematical models explored in this book will form the foundation for the next generation of intelligent machines.From autonomous drones to AI-driven robotic surgeons, the applications are endless, and all of them depend on the same universal principles — mathematics and intelligence.This book will help its readers not only understand these principles but also innovate upon them.

Sensors lie. Great robots learn how to combine them. Sensor Fusion Made Easy is a practical guide to Kalman Filters, IMUs, GPS fusion, LiDAR, SLAM, and modern robotics estimation systems — designed for engineers, ROS2 developers, drone builders, and autonomous systems enthusiasts. Learn how real robots turn noisy sensor data into reliable decisions.

Model Predictive Control sounds complicated—until you see how naturally it fits the way you already think. Look ahead. Plan. Adjust. Repeat. This book turns a powerful control method into something intuitive, practical, and surprisingly simple.

Kalman Filters Made Easy is a clear, intuitive introduction to one of engineering’s most powerful tools for dealing with noisy, imperfect, real‑world data. Instead of drowning you in equations, this book builds deep understanding through real examples — from drifting GPS signals to unpredictable sensor timing — and shows how engineers combine noisy measurements and imperfect models to estimate what’s really happening. If you work with robotics, drones, autonomous systems, or sensor‑driven devices, this guide gives you the mental models you need to design reliable systems in an uncertain world. This reflects the book’s core message: “The world gives us noisy, imperfect measurements. We want clean, reliable, accurate information. The filter is the bridge between the two.”

If a humanoid robot carries about $28,000 a year in fixed cost, then at $60 contribution per billable hour it breaks even at roughly 467 billable hours per year, or just under 9 hours per week. But if supervision is heavier and contribution drops to $28 per hour, break-even jumps to 1,000 hours per year, or about 19.2 hours per week.

If you value precision, depth, and professional-grade explanations—you’ll find this book indispensable.What if the secret to building truly intelligent robots has been right in front of us all along—hidden in the brains of mammals?For decades, robotics has stumbled on the same problem: machines that can perform tasks, but fail when the world shifts, bends, or surprises them. The solution isn’t another narrow algorithm. It’s a complete rethinking of cognition—modeled on the very systems that evolution has refined for survival, learning, and adaptability.

If you value precision, depth, and professional-grade explanations—you’ll find this book indispensable.What if the key to building more robust autonomous machines lies not in imitating the complexity of the human brain, but in revisiting the ancient simplicity of reptilian cognition?Reptilian Cognitive Architecture for Robotics explores the evolutionary roots of intelligence and translates them into practical frameworks for robotic design.

Not science fiction but a serious scientific exploration of our near future.What happens when robots transcend their roles as tools and assistants to become independent entities, collaborating and thriving in their own societies? Planet of the Robots offers an in-depth, scientific analysis of how robotic civilizations could emerge and evolve, shaping the course of humanity and our world.

Transform your factory from automated to intelligent.The factories that will dominate the next decade aren’t just automated — they’re aware. Machines learn. Robots collaborate. Systems adapt before failure ever happens. This is the evolution of manufacturing intelligence, and this book is your blueprint to master it.

Most books about humanoid robots are written to impress.This one is written for people who have to make them work.There is no hype here. No futurist pep talk. No glossy promises. This is a deployment manual — intentionally dry — because deployment punishes optimism and rewards discipline.Humanoid robots are moving out of demos and into real environments right now: factories, warehouses, logistics sites, healthcare, and service operations. That transition is happening without mature standards, settled regulations, or stable vendor stacks.Organizations are deploying anyway.That is the problem this book solves.This Is a Deployment Book, Not a Robotics Book

If machines are ever going to think, they have to do more than process data — they have to understand it.Integrating Data Fusion and Cognitive Architectures: Volume I – Foundations and Mechanisms is the blueprint for that transformation. It bridges two worlds that have lived apart for decades: the hard mathematics of sensor fusion and the structured reasoning of cognitive science. The result? A unified system that can perceive, reason, and adapt — not someday, but now.