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ROS Robotics Projects,

You're reading from   ROS Robotics Projects, Build and control robots powered by the Robot Operating System, machine learning, and virtual reality

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781838649326
Length 456 pages
Edition 2nd Edition
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Author (1):
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Ramkumar Gandhinathan Ramkumar Gandhinathan
Author Profile Icon Ramkumar Gandhinathan
Ramkumar Gandhinathan
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Table of Contents (14) Chapters Close

Preface 1. Getting Started with ROS FREE CHAPTER 2. Introduction to ROS-2 and Its Capabilities 3. Building an Industrial Mobile Manipulator 4. Handling Complex Robot Tasks Using State Machines 5. Building an Industrial Application 6. Multi-Robot Collaboration 7. ROS on Embedded Platforms and Their Control 8. Reinforcement Learning and Robotics 9. Deep Learning Using ROS and TensorFlow 10. Creating a Self-Driving Car Using ROS 11. Teleoperating Robots Using a VR Headset and Leap Motion 12. Face Detection and Tracking Using ROS, OpenCV, and Dynamixel Servos 13. Other Books You May Enjoy

Deep learning for robotics

Here are the main areas in robotics where we can apply deep learning:

  • Deep-learning-based object detector: Imagine a robot wants to pick a specific object from a group of objects. What can be the first step in solving this problem? It should identify the object first, right? We can use image processing algorithms such as segmentation and Haar training to detect an object, but the problem with those techniques is that they are not scalable and can't be used for many objects. Using deep learning algorithms, we can train a large neural network with a large dataset. It can have good accuracy and scalability compared to other methods. Datasets such as ImageNet (http://image-net.org/), which have a large collection of image datasets, can be used for training. We also get trained models that we can just use without training. We will look at an ImageNet...
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