國立虎尾科技大學 |

AI for robotics = toward embodied and general intelligence in the physical world /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	AI for robotics/ by Alishba Imran, Keerthana Gopalakrishnan.
Reminder of title:	toward embodied and general intelligence in the physical world /
Author:	Imran, Alishba.
other author:	Gopalakrishnan, Keerthana.
Published:	Berkeley, CA :Apress : : 2025.,
Description:	xxiii, 451 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Robotics. -
Online resource:	https://doi.org/10.1007/979-8-8688-0989-7
ISBN:	9798868809897

AI for robotics = toward embodied and general intelligence in the physical world /
Imran, Alishba.

AI for roboticstoward embodied and general intelligence in the physical world /[electronic resource] :by Alishba Imran, Keerthana Gopalakrishnan. - Berkeley, CA :Apress :2025. - xxiii, 451 p. :ill. (some col.), digital ;24 cm.

Chapter 1: Introduction: General Purpose Robotics -- Chapter 2: Robot Perception: Sensors and Image Processing -- Chapter 3: Robot Perception: 3D Data and Sensor Fusion -- Chapter 4: Foundation Models in Robotics -- Chapter 5: Simulation -- Chapter 6: Mapping, Localization, and Navigation -- Chapter 7: Reinforcement Learning and Control -- Chapter 8: Self Driving Cars -- Chapter 9: Industrial Robotics -- Chapter 10: Humanoid Robotics -- Chapter 11: Data-Driven Robotics in Practice.

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 Learn Explore ML applications in robotics, covering perception, control, localization, planning, and end-to-end learning Delve into system design, and algorithmic and hardware considerations for building efficient ML-integrated robotics systems Discover robotics applications in self-driving, manufacturing, and humanoids and their practical implementations Understand how machine learning and robotics benefit current research and organizations.

ISBN: 9798868809897

Standard No.: 10.1007/979-8-8688-0989-7doiSubjects--Topical Terms:

561941
Robotics.

LC Class. No.: TJ211

Dewey Class. No.: 629.892

AI for robotics = toward embodied and general intelligence in the physical world /
LDR:04371nam a2200325 a 4500 001 1162642
003 DE-He213
005 20250502130149.0
006 m d
007 cr nn 008maaau
008 251029s2025 cau s 0 eng d
020 $a 9798868809897 $q (electronic bk.)
020 $a 9798868809880 $q (paper)
024 7 $a 10.1007/979-8-8688-0989-7 $2 doi
035 $a 979-8-8688-0989-7
040 $a GP $c GP
041 0 $a eng
050 4 $a TJ211
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 629.892 $2 23
090 $a TJ211 $b .I34 2025
100 1 $a Imran, Alishba. $3 1489466
245 1 0 $a AI for robotics $h [electronic resource] : $b toward embodied and general intelligence in the physical world / $c by Alishba Imran, Keerthana Gopalakrishnan.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2025.
300 $a xxiii, 451 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Chapter 1: Introduction: General Purpose Robotics -- Chapter 2: Robot Perception: Sensors and Image Processing -- Chapter 3: Robot Perception: 3D Data and Sensor Fusion -- Chapter 4: Foundation Models in Robotics -- Chapter 5: Simulation -- Chapter 6: Mapping, Localization, and Navigation -- Chapter 7: Reinforcement Learning and Control -- Chapter 8: Self Driving Cars -- Chapter 9: Industrial Robotics -- Chapter 10: Humanoid Robotics -- Chapter 11: Data-Driven Robotics in Practice.
520 $a 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 Learn Explore ML applications in robotics, covering perception, control, localization, planning, and end-to-end learning Delve into system design, and algorithmic and hardware considerations for building efficient ML-integrated robotics systems Discover robotics applications in self-driving, manufacturing, and humanoids and their practical implementations Understand how machine learning and robotics benefit current research and organizations.
650 0 $a Robotics. $3 561941
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Python. $3 1115944
700 1 $a Gopalakrishnan, Keerthana. $3 1489467
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/979-8-8688-0989-7
950 $a Professional and Applied Computing (SpringerNature-12059)