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  • AI for Robotics: Toward Embodied and General Intelligence in the Physical World
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AI for Robotics: Toward Embodied and General Intelligence in the Physical World Paperback – May 3 2025

by Alishba Imran (Author), Keerthana Gopalakrishnan (Author)
5.0 out of 5 stars 3 ratings

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This book approaches robotics from a deep learning perspective. Artificial intelligence (AI) has transformed many fields, including robotics. This book shows you how to reimagine decades-old robotics problems as AI problems and is a handbook for solving problems using modern techniques in an era of large foundation models.The book begins with an introduction to general-purpose robotics, how robots are modeled, and how physical intelligence relates to the movement of building artificial general intelligence, while giving you an overview of the current state of the field, its challenges, and where we are headed. The first half of this book delves into defining what the problems in robotics are, how to frame them as AI problems, and the details of how to solve them using modern AI techniques. First, we look at robot perception and sensing to understand how robots perceive their environment, and discuss convolutional networks and vision transformers to solve robotics problems such as segmentation, classification, and detection in two and three dimensions. The book then details how to apply large language and multimodal models for robotics, and how to adapt them to solve reasoning and robot control. Simulation, localization, and mapping and navigation are framed as deep learning problems and discussed with recent research. Lastly, the first part of this book discusses reinforcement learning and control and how robots learn via trial and error and self-play.The second part of this book is concerned with applications of robotics in specialized contexts. You will develop full stack knowledge by applying the techniques discussed in the first part to real-world use cases. Individual chapters discuss the details of building robots for self-driving, industrial manipulation, and humanoid robots. For each application, you will learn how to design these systems, the prevalent algorithms in research and industry, and how to assess trade-offs for performance and reliability. The book concludes with thoughts on operations, infrastructure, and safety for data-driven robotics, and outlooks for the future of robotics and machine learning.In summary, this book offers insights into cutting-edge machine learning techniques applied in robotics, along with the challenges encountered during their implementation and practical strategies for overcoming them.What You Will LearnExplore ML applications in robotics, covering perception, control, localization, planning, and end-to-end learningDelve into system design, and algorithmicand hardware considerations for building efficient ML-integrated robotics systemsDiscover robotics applications in self-driving, manufacturing, and humanoids and their practical implementationsUnderstand how machine learning and robotics benefitcurrent research andorganizationsWho This Book Is ForSoftware and AI engineers eager to learn about robotics, seasoned robotics and mechanical engineers looking to stay at the cutting edge by integrating modern AI, and investors, executives or decision makers seeking insights into this dynamic field
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  1. ISBN-13
    979-8868809880
  2. Publisher
    Apress Publishers
  3. Publication date
    May 3 2025
  4. Language
    English
  5. Dimensions
    15.49 x 2.74 x 23.5 cm
  6. Print length
    451 pages
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From the Back Cover

This book approaches robotics from a deep learning perspective. Artificial intelligence (AI) has transformed many fields, including robotics. This book shows you how to reimagine decades-old robotics problems as AI problems and is a handbook for solving problems using modern techniques in an era of large foundation models.The book begins with an introduction to general-purpose robotics, how robots are modeled, and how physical intelligence relates to the movement of building artificial general intelligence, while giving you an overview of the current state of the field, its challenges, and where we are headed. The first half of this book delves into defining what the problems in robotics are, how to frame them as AI problems, and the details of how to solve them using modern AI techniques. First, we look at robot perception and sensing to understand how robots perceive their environment, and discuss convolutional networks and vision transformers to solve robotics problems such as segmentation, classification, and detection in two and three dimensions. The book then details how to apply large language and multimodal models for robotics, and how to adapt them to solve reasoning and robot control. Simulation, localization, and mapping and navigation are framed as deep learning problems and discussed with recent research. Lastly, the first part of this book discusses reinforcement learning and control and how robots learn via trial and error and self-play.The second part of this book is concerned with applications of robotics in specialized contexts. You will develop full stack knowledge by applying the techniques discussed in the first part to real-world use cases. Individual chapters discuss the details of building robots for self-driving, industrial manipulation, and humanoid robots. For each application, you will learn how to design these systems, the prevalent algorithms in research and industry, and how to assess trade-offs for performance and reliability. The book concludes with thoughts on operations, infrastructure, and safety for data-driven robotics, and outlooks for the future of robotics and machine learning.In summary, this book offers insights into cutting-edge machine learning techniques applied in robotics, along with the challenges encountered during their implementation and practical strategies for overcoming them.What You Will LearnExplore ML applications in robotics, covering perception, control, localization, planning, and end-to-end learningDelve into system design, and algorithmicand hardware considerations for building efficient ML-integrated robotics systemsDiscover robotics applications in self-driving, manufacturing, and humanoids and their practical implementationsUnderstand how machine learning and robotics benefitcurrent research andorganizations

About the Author

Alishba Imranis a machine learning and robotics developer focusing on robot learning for manipulation and perception. She is currently conducting research in reinforcement learning and unsupervised learning with Pieter Abbeel at the Berkeley AI Research Lab. Alishba has worked on perception at Cruise, developed simulation object manipulation methods at NVIDIA, and led efforts to reduce prosthetics costs at SJSU. At Hanson Robotics, she improved manipulation in Sophia the Robot, and at Kindred.AI, she contributed to robots that have picked up over 140 million units in production.
Keerthana Gopalakrishnanis a senior research scientist at Google Deepmind, working on robot manipulation and the Gemini project. She was educated at Carnegie Mellon University and Indian Institute of Technology. Her research concerns large language models for robotic planning, scaling visual language models for low level control, and cross-embodiment robot learning.

Product details

  • ASIN ‏ : ‎ B0DFLTZ4SQ
  • Publisher ‏ : ‎ Apress Publishers
  • Publication date ‏ : ‎ May 3 2025
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 451 pages
  • ISBN-13 ‏ : ‎ 979-8868809880
  • Item weight ‏ : ‎ 635 g
  • Dimensions ‏ : ‎ 15.49 x 2.74 x 23.5 cm
  • 鶹 Rank: #584,699 in Books (See Top 100 in Books)
  • Customer Reviews:
    5.0 out of 5 stars 3 ratings

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  • hayoung lee
    5.0 out of 5 stars Awesome, Excellent.
    Reviewed in the United States on July 15, 2025
    Verified Purchase
    I ordered this book from Korea.
    This is exactly the book I have been looking for.
    It hasn't been translated yet in Korean, but the explanation is straightforward enough even for foreigners.
    I have worked in robotics area for couple years, yet I am not a traditional robotics engineer. I am more like a software engineer working with robot software. I have looked into how I can advance robot software beyond slam, navigation, etc. This book explains how I can combine robot software with AI. If you know robot side decent enough, and took intro of AI class, this book will be tremendously helpful.
    Report
  • Ultraviolet Frog
    5.0 out of 5 stars A good introduction to modern/near-future robotics
    Reviewed in the United States on May 26, 2025
    Verified Purchase
    "AI for Robotics" is a one of the kind book that describes the current (early 2025) state of art in Robotics, an industry that's being around for a while, but undergoing a very rapid disruption by new methods and capabilities. Much of the content of the book was invented less than 2-3 years ago and not covered by any other printed books.

    The book does a good job of introducing many of the concepts and methods in the modern/near-future robotics, but it's not an exhaustive textbook. The reader is expected to follow and read the linked papers and online materials for deeper understanding.

    It shall be noted that the book requires prior understanding of programming, linear algebra, statistics and basic understanding of how neural nets work. I would totally recommend it to people with at least 2 years of college behind their backs, but it would not be a fun read for middle- and high-school students.
    Customer image
    Ultraviolet Frog
    5.0 out of 5 stars
    A good introduction to modern/near-future robotics

    Reviewed in the United States on May 26, 2025
    "AI for Robotics" is a one of the kind book that describes the current (early 2025) state of art in Robotics, an industry that's being around for a while, but undergoing a very rapid disruption by new methods and capabilities. Much of the content of the book was invented less than 2-3 years ago and not covered by any other printed books.

    The book does a good job of introducing many of the concepts and methods in the modern/near-future robotics, but it's not an exhaustive textbook. The reader is expected to follow and read the linked papers and online materials for deeper understanding.

    It shall be noted that the book requires prior understanding of programming, linear algebra, statistics and basic understanding of how neural nets work. I would totally recommend it to people with at least 2 years of college behind their backs, but it would not be a fun read for middle- and high-school students.
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  • Francois
    5.0 out of 5 stars a must read for AI engineers
    Reviewed in the United States on July 8, 2025
    Verified Purchase
    One of the best book about real utilization of AI, a must read !
    Report