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Mastering ROS 2 for Robotics Programming

You're reading from   Mastering ROS 2 for Robotics Programming Design, build, simulate, and prototype complex robots using the Robot Operating System 2

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Product type Paperback
Published in Jul 2025
Publisher Packt
ISBN-13 9781836209010
Length 576 pages
Edition 4th Edition
Concepts
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Authors (2):
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Lentin Joseph Lentin Joseph
Author Profile Icon Lentin Joseph
Lentin Joseph
Jonathan Cacace Jonathan Cacace
Author Profile Icon Jonathan Cacace
Jonathan Cacace
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Table of Contents (22) Chapters Close

Preface
1. Part I: ROS 2 Programming and Simulation
2. Introduction to ROS 2 FREE CHAPTER 3. Getting Started with ROS 2 Programming 4. Implementing ROS 2 Concepts 5. Working with Robot 3D Modeling in ROS 2 6. Simulating Robots in a Realistic Environment 7. Part II: ROS 2 Applications: Navigation, Manipulation, and Control
8. Controlling Robots Using the ros2_control Package 9. Implementing ROS 2 Applications Using BehaviorTree.CPP 10. ROS 2 Navigation Stack: Nav2 11. Robot Manipulation Using MoveIt 2 12. Working with ROS 2 and Perception Stack 13. Part III: Advanced Applications and Machine Learning
14. Aerial Robotics and ROS 2 15. Designing and Programming a DIY Mobile Robot from Scratch 16. Testing, Continuous Integration, and Continuous Deployment with ROS 2 17. Interfacing Large Language Models with ROS 2 18. ROS 2 and Deep Reinforcement Learning 19. Implementing ROS 2 Visualization and Simulation Plugins 20. Other Books You May Enjoy
21. Index

Dive into deep reinforcement learning

We humans learn through trial and error to take better actions based on the rewards and punishments we receive. Over time, we optimize our actions by maximizing the rewards and minimizing punishment. This process is called Reinforcement Learning (RL). This technique is used in various domains like robotics and AI, where agents learn to make decisions by interacting with the environment and receiving feedback.

In this section, we will discuss important concepts in RL and DRL. This will give you a quick guide before we jump into the application side of things. If you are new to RL, this section will be very useful to you. So, let’s begin by discussing RL and DRL.

Basic concepts of reinforcement learning

Reinforcement learning is a type of machine learning in which the agent learns from interacting with the environment. An agent performs actions and receives rewards or penalties. Based on the rewards and penalties, the agent adjusts...

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