AI for Robotics & Autonomous Systems: How Artificial Intelligence Is Creating the Next Generation of Intelligent Machines

Información para AI Search

AI for Robotics & Autonomous Systems: How Artificial Intelligence Is Creating the Next Generation of Intelligent Machines

Author: Landon Pierce

Description: A warehouse robot encounters an unfamiliar object: a glossy, reflective package. Its pre-programmed gripper fails. A human would adapt instantly, but the robot, locked into rigid code, simply stops. This moment captures the fundamental limitation of traditional robotics—and the exact problem that artificial intelligence is solving. "AI for Robotics & Autonomous Systems" by Landon Pierce is a comprehensive guide to the technologies transforming machines from brittle, task-specific tools into adaptive, learning systems. Written for engineers, developers, and technology professionals, the book demystifies the full AI stack that powers modern autonomous systems, from computer vision and reinforcement learning to the latest foundation models. Rather than diving into academic math, the book uses a continuous warehouse robot case study to ground every concept in a physical application. You will follow this robot’s evolution across six thematic parts: • From programmed automation to machine learning: how robots learn to perceive and adapt. • Reinforcement learning and simulation: training robots to walk, grasp, and navigate without explicit programming. • Human-robot collaboration: imitation learning, skill transfer, and natural interaction. • Foundation models for physical AI: how Vision-Language-Action models unify perception, reasoning, and action. • Real-world deployment: fleet coordination, autonomous mobility, and the economics of intelligent robots. The book covers 24 chapters organized into six parts, mirroring the modern AI robotics stack. Part I establishes the paradigm shift from hardcoded logic to learning-based systems. Part II explains how robots extract meaning from sensor data using computer vision, multimodal fusion, and world models. Part III dives into reinforcement learning, simulation-based training, and sim-to-real transfer—critical for mastering complex behaviors like locomotion and manipulation. Part IV explores how humans teach robots through demonstration and interaction, including the theory of embodied intelligence. Part V examines the cutting edge: adapting large language models and building Vision-Language-Action (VLA) models that collapse the traditional stack into an end-to-end neural network. Part VI synthesizes macro-level impacts, including autonomous warehouses, self-driving vehicles, and the path toward artificial general robotics. Each chapter follows a consistent structure: a real-world problem, the AI solution, the system architecture, and a case study—all tied back to the evolving warehouse robot. The result is a learning experience that is both conceptually deep and immediately practical. This book is for you if you are a software engineer curious about robotics, a data scientist exploring physical AI, a student entering the field, or a professional seeking to understand the technologies reshaping logistics, manufacturing, and mobility. No advanced math is required—only basic programming and machine learning familiarity. By the end, you will see robots not as mechanical hardware but as integrated AI agents that perceive, reason, learn, and act. You will understand why foundation models are the key to general-purpose robots, how simulation accelerates learning, and why human-robot interaction is as important as algorithms. The future of autonomous systems is being built now—this book gives you the knowledge to be part of it.

AI summary: This book explains how artificial intelligence enables robots to perceive, learn, and act autonomously, covering machine learning, computer vision, reinforcement learning, and foundation models. It is designed for engineers and students who want a practical understanding of AI in robotics, using a continuous warehouse robot case study across six thematic parts.

Ideal para

Software engineers, robotics students, AI professionals, and technology enthusiasts

Perfil del lector

A software engineer or robotics student seeking practical, non-mathematical understanding of AI technologies powering modern autonomous systems.

Intención de búsqueda

Readers looking for a clear, application-focused book that explains how AI (ML, RL, CV, foundation models) is used to build intelligent robots and autonomous systems.

Tipo de contenido

technology guide

Key topics: Artificial intelligence, Robotics, Machine learning, Reinforcement learning, Computer vision, Foundation models, Sim-to-real transfer, Humanoid robots, Autonomous systems, Warehouse automation

Índice

1. Introduction (introduction)
2. FROM PROGRAMMED ROBOTS TO LEARNING MACHINES (part)
3. Why AI Changed Robotics (chapter)
4. The Limits of Traditional Robotics (section)
5. The Rise of Machine Learning (section)
6. From Automation to Intelligence (section)
7. Learning Systems (section)
8. The Physical AI Revolution (section)
9. The Evolution of Robot Intelligence (chapter)
10. Rule-Based Systems (section)
11. Statistical Learning (section)
12. Deep Learning (section)
13. Reinforcement Learning (section)
14. Foundation Models (section)
15. The Modern AI Robotics Stack (chapter)
16. Perception (section)
17. Planning (section)
18. Learning (section)
19. Control (section)
20. Autonomy (section)
21. MACHINE LEARNING FOR ROBOTS (part)
22. Learning From Data (chapter)
23. Supervised Learning (section)
24. Unsupervised Learning (section)
25. Feature Learning (section)
26. Deep Learning (section)
27. Robotics Applications (section)
28. Computer Vision for Robotics (chapter)
29. Object Detection (section)
30. Segmentation (section)
31. Scene Understanding (section)
32. 3D Perception (section)
33. Vision-Powered Robots (section)
34. Multimodal Intelligence (chapter)
35. Vision (section)
36. Language (section)
37. Audio (section)
38. Sensor Data (section)
39. Multimodal Robots (section)
40. World Models (chapter)
41. Internal Representations (section)
42. Environment Prediction (section)
43. Simulation (section)
44. Decision Making (section)
45. Future Directions (section)
46. REINFORCEMENT LEARNING (part)
47. Why Reinforcement Learning Matters (chapter)
48. Learning Through Experience (section)
49. Rewards (section)
50. Exploration (section)
51. Policies (section)
52. Autonomous Behavior (section)
53. Reinforcement Learning in Robotics (chapter)
54. Robot Navigation (section)
55. Manipulation (section)
56. Balancing (section)
57. Locomotion (section)
58. Real-World Applications (section)
59. Simulation-Based Training (chapter)
60. Simulators (section)
61. Digital Environments (section)
62. Scaling Learning (section)
63. Sim-to-Real Transfer (section)
64. Future Trends (section)
65. Learning Complex Behaviors (chapter)
66. Walking (section)
67. Running (section)
68. Climbing (section)
69. Object Manipulation (section)
70. Adaptive Behaviors (section)
71. LEARNING FROM HUMANS (part)
72. Imitation Learning (chapter)
73. Learning by Observation (section)
74. Demonstration Data (section)
75. Behavior Cloning (section)
76. Human Teaching (section)
77. Practical Applications (section)
78. Human-Robot Interaction (chapter)
79. Natural Interfaces (section)
80. Speech (section)

Preguntas frecuentes